{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIS521 midterm2 notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01ac4a72-76db-4010-a36c-bd1c2437d564"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50db40fa-af71-4725-e148-b561ca33ff93"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 16, “Making Simple Decisions” (16.1-16.3)\n",
        "\n",
        "#sample_equation = r\"\"\" $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" Chapter 16.1 summary. \"\"\"\n",
        "chapter16_2 =  \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter17_2 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\"\n",
        "Stochastic transition function is the probabilty of arriving in state s' given \\\n",
        "state s and selecting action a: $p(s' | s, a)$.\\\\\n",
        "MDP is a sequential decision problem for a fully observable environment with \\\n",
        "a stochastic transition model that has additive rewards.\\\\\n",
        "Components of MDP: states, actions, transition fn, reward fn, initial state, terminal states.\\\\\n",
        "Value (utility) of a state is the expected utility starting in s and acting optimally.\\\\\n",
        "Value of a q-state is the expected utility starting out having taken action a from state s \\\n",
        "and thereafter acting optimally.\\\\\n",
        "In value iteration, start at $V_0=0$, do one ply of expectimax from each state and repeat until convergence.\\\\\n",
        "Utility of a state under a fixed policy is the expected total discounted rewards starting in s and following π.\\\\\n",
        "Policy iteration is better than value iteration because: 1) faster to converges, 2) more efficient $O(s^2)$.\\\\\n",
        "In policy iteration, do 1) policy evaluation: calculate utilities for some fixed policy (doesn't have to be optimal) until convergence, then \\\n",
        "2) policy improvement: update policy using one-step look-ahead (policy extraction) with resulting converged utilities as future values, \\\n",
        "and repeat until policy converges.\\\\\n",
        "In value iteration, each iteration updates explicitly the values and implicitly the policy but we don't track the policy.\\\\\n",
        "In policy iteration, each iteration considers one action under a fixed policy, the policy is evalulated and better one is chosen (improved).\\\\\n",
        "In worst-case minimax reasoning, only ordering matters; in average-case expectimax, magnitudes matter.\\\\\n",
        "Any rational preferences can be summarized as a utility function.\\\\\n",
        "With uncertainty, outcomes depend on utility and probability.\\\\\n",
        "Under MEU principle, agent choose the action that maximizes expected utility.\\\\\n",
        "Utility of having money: people are generally risk averse but risk-prone when deep in debt.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 8.\n",
        "# You can define any bolded term in the textbook, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 25 words long.\n",
        "\n",
        "module8_definitions = {\n",
        "\"absorbing state\": \"guarantees a terminal state will eventually be reached for every policy\",\n",
        "\"axiom of transitivity\": \"if A>B and B>C, then A>C\",\n",
        "\"Bellman equations\": \"gives a one-step lookahead relationship amontst optimal utility values\",\n",
        "\"discounting\": \"values of rewards decay exponentially; built into utility fn\",\n",
        "\"environmental history\": \"sequence of (state, action) pairs\",\n",
        "\"expected utility\" : \"sum of all possible paths generated by the policy\",\n",
        "\"insensitivity to monotonic transformation\": \"only ordering matters, magnitude does not\",\n",
        "\"markovian\": \"probability of arriving in s' depends on s and not the history of earlier states\",\n",
        "\"maximum expected utility (MEU)\": \"rational agent should choose the action that maximizes its expected utility given its knowledge\",\n",
        "\"policy (π)\": \"maps from a state to the best action which depends on the reward\",\n",
        "\"policy extraction\": \"extract the optimal policy given a set of optimal (q-)values\",\n",
        "\"rational preferences\": \"imply behaviors describable as maximazation of expected utility\",\n",
        "\"utilities\": \"functions from outcomes to real numbers that describe an agent's preferences\",\n",
        "\"utility function\" : \"sum of rewards from a sequence of state and actions\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elCCgY64spuq"
      },
      "source": [
        "## Preview of your notes for Module 8\n",
        "\n",
        "Here's a preview of your how your notes will be displayed.  At the end of this Python notebook, we'll aggregate your notes for all modules together into a single display.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYeNcdM_maE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "7f254af0-59f1-42eb-ddaa-90b503bbb5bd"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "from IPython.display import display, Markdown, Latex\n",
        "\n",
        "module8_sections = [\n",
        "  (\"AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\", chapter16_1),\n",
        "  (\"AIMA Chapter 16.2 The Basis of Utility Theory\", chapter16_2),\n",
        "  (\"AIMA Chapter 16.3 Utility Functions\", chapter16_3),\n",
        "  (\"AIMA Chapter 17.1 Sequential Decision Problems\", chapter17_1),\n",
        "  (\"AIMA Chapter 17.2 Algorithms for MDPs\", chapter17_2),\n",
        "  (\"Module 8 Lecture Notes\", module8_lecture_notes),\n",
        "]\n",
        "\n",
        "\n",
        "def length_check(sections):\n",
        "  warnings = \"\"\n",
        "  for section, summary in sections:\n",
        "    max_word_length = 500\n",
        "    max_character_length = 3000\n",
        "    if \"lecture\" in section.lower():\n",
        "      # It's OK to do longer summaries of the lectures\n",
        "      max_word_length = 2000\n",
        "      max_character_length = 12000  \n",
        "    if (len(summary) > max_character_length or len(summary.split(' ')) > max_word_length):\n",
        "      warnings += \"WARNING: Your summary of '{section}' is too long!\\n\".format(section=section)\n",
        "  return warnings\n",
        "\n",
        "def length_check_definitions(definitions):\n",
        "  warnings = \"\"\n",
        "  for concept, definition in definitions.items():\n",
        "    if(len(definition.split(' ')) > 25):\n",
        "      warnings += \"WARNING: Your definition of '{concept}' is too long!\\n\".format(concept=concept)\n",
        "  return warnings\n",
        "\n",
        "\n",
        "def format_module(module_num, title, sections, definitions, show_placeholder=False):\n",
        "  markdown = \"\"\n",
        "\n",
        "  # Check summary lengths\n",
        "  warnings = length_check(sections)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  # Check definition lengths\n",
        "  warnings = length_check_definitions(definitions)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  markdown += \"# Module {num}: {title}\\n\".format(num=module_num, title=title)\n",
        "\n",
        "\n",
        "  for section, summary in sections:\n",
        "    if show_placeholder and summary == \" Your summary here \":\n",
        "      summary = placeholder\n",
        "    markdown += \"## {section}\\n{summary}\\n\\n\".format(section=section, summary=summary)\n",
        "  \n",
        "  markdown += \"## Module {num} Definitions\\n\".format(num=module_num)\n",
        "  for concept, definition in definitions.items():\n",
        "    markdown += \"* **{concept}** - {definition}\\n\".format(concept=concept.capitalize(), definition=definition)\n",
        "  return markdown\n",
        "\n",
        "\n",
        "module8_markdown = format_module(8, \"Markov Decision Processes\", module8_sections, module8_definitions)\n",
        "display(Markdown(module8_markdown))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary. \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Your summary here \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n\nStochastic transition function is the probabilty of arriving in state s' given state s and selecting action a: $p(s' | s, a)$.\\\nMDP is a sequential decision problem for a fully observable environment with a stochastic transition model that has additive rewards.\\\nComponents of MDP: states, actions, transition fn, reward fn, initial state, terminal states.\\\nValue (utility) of a state is the expected utility starting in s and acting optimally.\\\nValue of a q-state is the expected utility starting out having taken action a from state s and thereafter acting optimally.\\\nIn value iteration, start at $V_0=0$, do one ply of expectimax from each state and repeat until convergence.\\\nUtility of a state under a fixed policy is the expected total discounted rewards starting in s and following π.\\\nPolicy iteration is better than value iteration because: 1) faster to converges, 2) more efficient $O(s^2)$.\\\nIn policy iteration, do 1) policy evaluation: calculate utilities for some fixed policy (doesn't have to be optimal) until convergence, then 2) policy improvement: update policy using one-step look-ahead (policy extraction) with resulting converged utilities as future values, and repeat until policy converges.\\\nIn value iteration, each iteration updates explicitly the values and implicitly the policy but we don't track the policy.\\\nIn policy iteration, each iteration considers one action under a fixed policy, the policy is evalulated and better one is chosen (improved).\\\nIn worst-case minimax reasoning, only ordering matters; in average-case expectimax, magnitudes matter.\\\nAny rational preferences can be summarized as a utility function.\\\nWith uncertainty, outcomes depend on utility and probability.\\\nUnder MEU principle, agent choose the action that maximizes expected utility.\\\nUtility of having money: people are generally risk averse but risk-prone when deep in debt.\n\n\n## Module 8 Definitions\n* **Absorbing state** - guarantees a terminal state will eventually be reached for every policy\n* **Axiom of transitivity** - if A>B and B>C, then A>C\n* **Bellman equations** - gives a one-step lookahead relationship amontst optimal utility values\n* **Discounting** - values of rewards decay exponentially; built into utility fn\n* **Environmental history** - sequence of (state, action) pairs\n* **Expected utility** - sum of all possible paths generated by the policy\n* **Insensitivity to monotonic transformation** - only ordering matters, magnitude does not\n* **Markovian** - probability of arriving in s' depends on s and not the history of earlier states\n* **Maximum expected utility (meu)** - rational agent should choose the action that maximizes its expected utility given its knowledge\n* **Policy (π)** - maps from a state to the best action which depends on the reward\n* **Policy extraction** - extract the optimal policy given a set of optimal (q-)values\n* **Rational preferences** - imply behaviors describable as maximazation of expected utility\n* **Utilities** - functions from outcomes to real numbers that describe an agent's preferences\n* **Utility function** - sum of rewards from a sequence of state and actions\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 9 - Reinforcement Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# Russell and Norvig, AIMA Section 17.3 “Bandit Problems”\n",
        "chapter17_3 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_2 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_3 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_4 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_5 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\"\n",
        "Reinforcement learning involves receiving feedback in the form rewards and \\\n",
        "must learn to act so as to maximized expected rewards, where all learning is \\\n",
        "based on observed samples of outcomes.\\\\\n",
        "In reinforcement learning, transition model and reward function is unknown.\\\\\n",
        "In model-based learning, 1) learn empirical MDP model by counting outcomes then 2) solve the learned MDP.\\\\\n",
        "Passive reinforcement learning with a fixed policy is still active.\\\\\n",
        "In TD learning, need to learn q-values not values in order to get optimal policy.\\\\\n",
        "In modified Q-update, when visit count n is small, a bonus is given to explore this state.\\\\\n",
        "Feature representation enables us to write a q-fn for any state using a few weights.\\\\\n",
        "In approx. Q-learning, adjust weights of active features and disprefer states by lowering weights.\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"approx. Q-learning\": \"Q-learning with feature representation\",\n",
        "\"direct evaluation\": \"compute values for each state under fixed policy and average observed sample values\",\n",
        "\"exploitation\": \"usse what you know/have previous learned\",\n",
        "\"exploration\" : \"try unknown actions to get information\",\n",
        "\"exploration function\": \"takes a value estimate and a visit count, and return an optimistic utility\",\n",
        "\"model-based learning\": \"an approximate model is learned based on experiences\",\n",
        "\"off-policy learning\": \"Q-learning converges even if acting suboptimally\",\n",
        "\"regret\": \"mistake cost - difference between expected and optimal rewards\",\n",
        "\"temporal difference learning\": \"update V(s) each time; likely outcomes will update more often\",\n",
        "\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "cellView": "form",
        "outputId": "860a5eef-0799-4971-9f56-ff0e7c5a8d76"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "\n",
        "module9_sections = [\n",
        "  (\"AIMA Chapter 17.3 Bandit Problems\", chapter17_3),\n",
        "  (\"AIMA Chapter 22.1 Learning from Rewards\", chapter22_1),\n",
        "  (\"AIMA Chapter 22.2 Passive Reinforcement Learning\", chapter22_2),\n",
        "  (\"AIMA Chapter 22.3 Active Reinforcement Learning\", chapter22_3),\n",
        "  (\"AIMA Chapter 22.4 Generalization in Reinforcement Learning\", chapter22_4),\n",
        "  (\"Module 9 Lecture Notes\", module9_lecture_notes),\n",
        "]\n",
        "\n",
        "module9_markdown = format_module(9, \"Reinforcement Learning\", module9_sections, module9_definitions)\n",
        "display(Markdown(module9_markdown))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Your summary here \n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Your summary here \n\n## Module 9 Lecture Notes\n\nReinforcement learning involves receiving feedback in the form rewards and must learn to act so as to maximized expected rewards, where all learning is based on observed samples of outcomes.\\\nIn reinforcement learning, transition model and reward function is unknown.\\\nIn model-based learning, 1) learn empirical MDP model by counting outcomes then 2) solve the learned MDP.\\\nPassive reinforcement learning with a fixed policy is still active.\\\nIn TD learning, need to learn q-values not values in order to get optimal policy.\\\nIn modified Q-update, when visit count n is small, a bonus is given to explore this state.\\\nFeature representation enables us to write a q-fn for any state using a few weights.\\\nIn approx. Q-learning, adjust weights of active features and disprefer states by lowering weights.\n\n\n## Module 9 Definitions\n* **Approx. q-learning** - Q-learning with feature representation\n* **Direct evaluation** - compute values for each state under fixed policy and average observed sample values\n* **Exploitation** - usse what you know/have previous learned\n* **Exploration** - try unknown actions to get information\n* **Exploration function** - takes a value estimate and a visit count, and return an optimistic utility\n* **Model-based learning** - an approximate model is learned based on experiences\n* **Off-policy learning** - Q-learning converges even if acting suboptimally\n* **Regret** - mistake cost - difference between expected and optimal rewards\n* **Temporal difference learning** - update V(s) each time; likely outcomes will update more often\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 10 - Probabilities and Language Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 12 “Quantifying Uncertainty” (Sections 12.1-12.7)\n",
        "chapter12_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_6 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Jurafsky and Martin, Speech and Language Processing Chapter 3 “N-Gram Language Models”  (Sections 3.1-3.5,3.7)\n",
        "SLP_chapter3_1 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\"\n",
        "Probabilistic reasoning gives a framework to use evidence to reason about how likely \\\n",
        "particular outcome is.\\\\\n",
        "Axioms of probability: MECE.\\\\\n",
        "Size of join distribution with n variables and each domain size d: $d^n$.\\\\\n",
        "Random variables are categoized into 3 types: 1) evidence variables (known), \\\n",
        "2) query variable (unknown), 3) hidden variables (part of join prob. but no impact).\\\\\n",
        "To do inference by enumeration - P(Q|e1...ek): 1) select the entires consistent with evidence, \\\n",
        "2) sum out H to get joint of Q and E, 3) normalize.\\\\\n",
        "Bayes' rule allows us to calculate conditional from its reverse.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"conditional probability\": \"prob of A given B\",\n",
        "\"evidence\" : \"observed variables\",\n",
        "\"Markov assumption\": \"current word depends on the previous k words, not the whole context\",\n",
        "\"probabilistic inference\": \"to compute a desired probability from other known probs.\",\n",
        "\"probabilistic model\": \"join distribution over a set of random variables\",\n",
        "\"probabilisitc language models\": \"computes prob of a sentence or sequence of words\",\n",
        "\"states\": \"unobserved variables\",\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "c7d5330b-7864-45c0-c66e-81df4ea917ea"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module10_sections = [\n",
        "  (\"AIMA Chapter 12.1 Acting under Uncertainty\", chapter12_1),\n",
        "  (\"AIMA Chapter 12.2 Basic Probability Notation\", chapter12_2),\n",
        "  (\"AIMA Chapter 12.3 Inference Using Full Joint Distributions\", chapter12_3),\n",
        "  (\"AIMA Chapter 12.4 Independence\", chapter12_4),\n",
        "  (\"AIMA Chapter 12.5 Bayes’ Rule and Its Use\", chapter12_5),\n",
        "  (\"AIMA Chapter 12.6 Naive Bayes Models\", chapter12_6),\n",
        "  (\"AIMA Chapter 12.7 The Wumpus World Revisited\", chapter12_7),\n",
        "  (\"SLP Chapter 3.1 N-Grams\", SLP_chapter3_1),\n",
        "  (\"SLP Chapter 3.2 Evaluating Language Models\", SLP_chapter3_2),\n",
        "  (\"SLP Chapter 3.3 Sampling sentences from a language model\", SLP_chapter3_3),\n",
        "  (\"SLP Chapter 3.4 Generalization and Zeros\", SLP_chapter3_4),\n",
        "  (\"SLP Chapter 3.5 Smoothing\", SLP_chapter3_5),\n",
        "  (\"SLP Chapter 3.7 Huge Language Models and Stupid Backoff\", SLP_chapter3_7),\n",
        "  (\"Module 10 Lecture Notes\", module10_lecture_notes),\n",
        "]\n",
        "\n",
        "module10_markdown = format_module(10, \"Probabilies and Language Models\", module10_sections, module10_definitions)\n",
        "display(Markdown(module10_markdown))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n\nProbabilistic reasoning gives a framework to use evidence to reason about how likely particular outcome is.\\\nAxioms of probability: MECE.\\\nSize of join distribution with n variables and each domain size d: $d^n$.\\\nRandom variables are categoized into 3 types: 1) evidence variables (known), 2) query variable (unknown), 3) hidden variables (part of join prob. but no impact).\\\nTo do inference by enumeration - P(Q|e1...ek): 1) select the entires consistent with evidence, 2) sum out H to get joint of Q and E, 3) normalize.\\\nBayes' rule allows us to calculate conditional from its reverse.\n\n\n## Module 10 Definitions\n* **Conditional probability** - prob of A given B\n* **Evidence** - observed variables\n* **Markov assumption** - current word depends on the previous k words, not the whole context\n* **Probabilistic inference** - to compute a desired probability from other known probs.\n* **Probabilistic model** - join distribution over a set of random variables\n* **Probabilisitc language models** - computes prob of a sentence or sequence of words\n* **States** - unobserved variables\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, Russell and Norvig, AIMA Chapter 13 “Probabilistic Reasoning” (13.1, 13.2, 13.3)\n",
        "chapter13_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\"\n",
        "Absolute independence doesn't help us to make inferences about other variables.\\\\\n",
        "Each arc in Bayes' net represents a conditional probability (local interaction).\\\\\n",
        "Bayes' net is comprised of a topology (graph) and local conditional probabilities.\\\\\n",
        "Bayes' net implicitly encode joint distributon as the product of local conditional distributions.\\\\\n",
        "Size of joint distribution tables: $2^N$; size of N-node net with up to k parents: $O(N*2^{k+1})$.\\\\\n",
        "Cause chain: evidence along the chain \"blocks\" the influence.\\\\\n",
        "Common cause: observing the cause blocks influence between effects.\\\\\n",
        "Common effect: observing an effect activates influence between possible causes.\\\\\n",
        "D-separation: 1) if one or more path is active, indenpendence is not guaranteed.\\\n",
        "2) if all paths are inactive then independence is guaranteed.\\\n",
        "3) a path is active if each triple is active.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"active path\": \"\",\n",
        "\"Bayes' net\" : \"a technique for describing complex joint distributions (models) using local distributions (cond prob)\",\n",
        "\"causal chain\": \"X -> Y -> Z; X and Z absolute independence NO, conditional independence YES\",\n",
        "\"common cause\": \"Y -> X, Y -> Z; X and Z absolute independence NO, conditional independence YES\",\n",
        "\"common effect\": \"X -> Z, Y -> Z; X and Y absolute independence YES, X and Y given Z conditional independence NO\",\n",
        "}\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "cellView": "form",
        "outputId": "c0a315a1-983b-416c-cc71-789ecc56e6a2"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module11_sections = [\n",
        "  (\"AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\", chapter13_1),\n",
        "  (\"AIMA Chapter 13.2 The Semantics of Bayesian Networks\", chapter13_2),\n",
        "  (\"AIMA 13.3 Exact Inference in Bayesian Networks\", chapter13_3),\n",
        "  (\"Module 11 Lecture Notes\", module11_lecture_notes),\n",
        "]\n",
        "\n",
        "module11_markdown = format_module(11, \"Probabilistic Reasoning and Bayes’ Nets\", \n",
        "                         module11_sections, \n",
        "                         module11_definitions)\n",
        "display(Markdown(module11_markdown))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n\nAbsolute independence doesn't help us to make inferences about other variables.\\\nEach arc in Bayes' net represents a conditional probability (local interaction).\\\nBayes' net is comprised of a topology (graph) and local conditional probabilities.\\\nBayes' net implicitly encode joint distributon as the product of local conditional distributions.\\\nSize of joint distribution tables: $2^N$; size of N-node net with up to k parents: $O(N*2^{k+1})$.\\\nCause chain: evidence along the chain \"blocks\" the influence.\\\nCommon cause: observing the cause blocks influence between effects.\\\nCommon effect: observing an effect activates influence between possible causes.\\\nD-separation: 1) if one or more path is active, indenpendence is not guaranteed.2) if all paths are inactive then independence is guaranteed.3) a path is active if each triple is active.\n\n\n## Module 11 Definitions\n* **Active path** - \n* **Bayes' net** - a technique for describing complex joint distributions (models) using local distributions (cond prob)\n* **Causal chain** - X -> Y -> Z; X and Z absolute independence NO, conditional independence YES\n* **Common cause** - Y -> X, Y -> Z; X and Z absolute independence NO, conditional independence YES\n* **Common effect** - X -> Z, Y -> Z; X and Y absolute independence YES, X and Y given Z conditional independence NO\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 12 - Naive Bayes and Perceptrons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 19 “Learning from Examples” (Sections 19.1-19.6)\n",
        "\n",
        "chapter19_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Speech and Language Processing Chapter 4 “Naive Bayes and Sentiment Classification”\n",
        "slp_chapter4_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_7 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_9 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_10 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\"\n",
        "In the general Naive Bayes model, number of parameters is linear in number of features, \\\n",
        "and we specify how each feature depends on the class.\\\\\n",
        "To do inference for naive Bayes, 1) get joint probability of label and evidence for each label, \\\n",
        "2) sum to get prob of evidence, 3) normalize by dividing step 1 by step 2.\\\\\n",
        "Note that the local conditioanl tables in step 1 come from machine learning.\\\\\n",
        "Procedure for machine learning: 1) learn parameters on training set, 2) tune hyperparameters on held-out set, \\\n",
        "3) evaluate accuracy (fraction of instances predicted correctly) on test set.\\\\\n",
        "Maximum likelihood estimates fail to account for unseen events.\\\\\n",
        "Increasing k in Laplace smoothing makes the probabilty closer to equi-probable.\\\\\n",
        "In binary perceptron, initialize weights=0, if $y=y*$ do no change, else adjust weight vector.\\\\\n",
        "In multiclass perceptron, initialize weights=0, predict with current weights and use the highest score, \\\n",
        "if correct do no change, else lower weight for wrong answer and raise weight for correct answer.\\\\\n",
        "Problems with perceptron: noise (not separable), mediocre generalization, overtraining (kind of overfitting).\\\\\n",
        "Perceptrons make less assumptions, is mistake-driven and does multiple passes through data to predict.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"bag-of-words model\": \"insensitive to ordering, each position is identically distributed and share the same cond prob.\",\n",
        "\"confidence\": \"$max_y P(y|x)$, represents how sure the classifier is\",\n",
        "\"convergence\": \"if separable, perceptron will eventually converge\",\n",
        "\"Laplace smoothing\": \"pretend saw every outcome k extra times\",\n",
        "\"linear classifiers\": \"activation fn is the dot product of feature values (inputs) and weights\",\n",
        "\"maximum likelihood\": \"estimate that maximizes likelihood of the data by looking at the empirical rate of the values\",\n",
        "\"MIRA\": \"choose an update size that fixes the current mistake but minimizes changes to w\",\n",
        "\"mistake bound\": \"max number of mistakes related to the margin or degree of separability\",\n",
        "\"naive Bayes\" : \"assume all features are independence effects of the label (ie, F1 and F2 cond. indp. given Y)\",\n",
        "\"overfitting\": \"fitting the training data too closely but not generalizing well\",\n",
        "\"separability\": \"true if some parameters get the training set perfectly correct\",\n",
        "\"SVM\": \"find the separator with max margin\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "48e81217-38c8-40df-c66d-8104118af63b"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module12_sections = [\n",
        "  (\"AIMA Chapter 5.1 Game Theory\", chapter19_1),\n",
        "  (\"AIMA Chapter 5.2 Optimal Decisions in Games\", chapter19_2),\n",
        "  (\"AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\", chapter19_3),\n",
        "  (\"AIMA Chapter 5.5 Stochastic Games\", chapter19_4),\n",
        "  (\"AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\", chapter19_5),\n",
        "  (\"AIMA Chapter 16.2 The Basis of Utility Theory\", chapter19_6),\n",
        "  (\"SLP Chapter 4.1 Naive Bayes Classifiers\", slp_chapter4_1),\n",
        "  (\"SLP Chapter 4.2 Training the Naive Bayes Classifier\", slp_chapter4_2),\n",
        "  (\"SLP Chapter 4.3 Worked Example\", slp_chapter4_3),\n",
        "  (\"SLP Chapter 4.4 Optimizing for Sentiment Analysis\", slp_chapter4_4),\n",
        "  (\"SLP Chapter 4.5 Naive Bayes for other text classification tasks\", slp_chapter4_5),\n",
        "  (\"SLP Chapter 4.6 Naive Bayes as a Language Model\", slp_chapter4_6),\n",
        "  (\"SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\", slp_chapter4_7),\n",
        "  (\"SLP Chapter 4.8 Test sets and Cross-validation\", slp_chapter4_8),\n",
        "  (\"SLP Chapter 4.9 Statistical Significance Testing\", slp_chapter4_9),\n",
        "  (\"SLP Chapter 4.10 Avoiding Harms in Classification\", slp_chapter4_10),\n",
        "  (\"Module 12 Lecture Notes\", module12_lecture_notes),\n",
        "]\n",
        "\n",
        "module12_markdown = format_module(12, \"Naive Bayes and Perceptrons\", \n",
        "                         module12_sections, \n",
        "                         module12_definitions)\n",
        "display(Markdown(module12_markdown))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n\nIn the general Naive Bayes model, number of parameters is linear in number of features, and we specify how each feature depends on the class.\\\nTo do inference for naive Bayes, 1) get joint probability of label and evidence for each label, 2) sum to get prob of evidence, 3) normalize by dividing step 1 by step 2.\\\nNote that the local conditioanl tables in step 1 come from machine learning.\\\nProcedure for machine learning: 1) learn parameters on training set, 2) tune hyperparameters on held-out set, 3) evaluate accuracy (fraction of instances predicted correctly) on test set.\\\nMaximum likelihood estimates fail to account for unseen events.\\\nIncreasing k in Laplace smoothing makes the probabilty closer to equi-probable.\\\nIn binary perceptron, initialize weights=0, if $y=y*$ do no change, else adjust weight vector.\\\nIn multiclass perceptron, initialize weights=0, predict with current weights and use the highest score, if correct do no change, else lower weight for wrong answer and raise weight for correct answer.\\\nProblems with perceptron: noise (not separable), mediocre generalization, overtraining (kind of overfitting).\\\nPerceptrons make less assumptions, is mistake-driven and does multiple passes through data to predict.\n\n\n## Module 12 Definitions\n* **Bag-of-words model** - insensitive to ordering, each position is identically distributed and share the same cond prob.\n* **Confidence** - $max_y P(y|x)$, represents how sure the classifier is\n* **Convergence** - if separable, perceptron will eventually converge\n* **Laplace smoothing** - pretend saw every outcome k extra times\n* **Linear classifiers** - activation fn is the dot product of feature values (inputs) and weights\n* **Maximum likelihood** - estimate that maximizes likelihood of the data by looking at the empirical rate of the values\n* **Mira** - choose an update size that fixes the current mistake but minimizes changes to w\n* **Mistake bound** - max number of mistakes related to the margin or degree of separability\n* **Naive bayes** - assume all features are independence effects of the label (ie, F1 and F2 cond. indp. given Y)\n* **Overfitting** - fitting the training data too closely but not generalizing well\n* **Separability** - true if some parameters get the training set perfectly correct\n* **Svm** - find the separator with max margin\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\"\n",
        "Logistic regression learns from a set of weights and a bias term then convert to a proper probability through sigmoid function.\\\\\n",
        "CE loss is lower if prediction is correct, and higher if prediction is incorrect.\\\\\n",
        "The goal of stochastic gradient descent is to find the θ that minimizes CE loss across all training data.\\\\\n",
        "Gradient descent starting from any point is guaranteed to find the minimum.\\\\\n",
        "The magnitude of the amount to move in gradient descent is the value of the slope weighted by learning rate. \\\n",
        "Higher learning rate -> move w more on each step.\\\\\n",
        "To train neural nets, we need 1) cross-entry loss function, 2) gradient descent to minimze loss, 3) error propagation to \\\n",
        "compute descent.\\\\\n",
        "Using logistic regression to calculate descent only gives correct updates to the last layer, need to do error propagation \\\n",
        "to update all previous layers.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"cross-entropy loss\": \"mathematically the negative log likelihood loss\",\n",
        "\"feed-forward neural network\": \"multi-layer network, fully connected units, no cycles\",\n",
        "\"gradient descent\": \"finds which direction the function's slope is rising the most steeply and move in the opposite direction\",\n",
        "\"loss function\" : \"distance between system output and gold output\",\n",
        "\"regularization\": \"penalize large weights; helps reduce overfitting\",\n",
        "\"softmax function\": \"a generalization of the sigmoid function used in multinomial logistic regression\",\n",
        "}\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "42b4b2a8-a901-4b8e-9d5a-b603ffc822f7"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module13_sections = [\n",
        "  (\"SLP Chapter 5.1 Classification: the sigmoid\", slp_chapter5_1),\n",
        "  (\"SLP Chapter 5.2 Learning in Logistic Regression\", slp_chapter5_2),\n",
        "  (\"SLP Chapter 5.3 The cross-entropy loss function\", slp_chapter5_3),\n",
        "  (\"SLP Chapter 5.4 Gradient Descent\", slp_chapter5_4),\n",
        "  (\"SLP Chapter 5.5 Regularization\", slp_chapter5_5),\n",
        "  (\"SLP Chapter 5.6 Multinomial logistic regression\", slp_chapter5_6),\n",
        "\n",
        "  (\"SLP Chapter 7.1 Units\", slp_chapter7_1),\n",
        "  (\"SLP Chapter 7.2 The XOR problem\", slp_chapter7_2),\n",
        "  (\"SLP Chapter 7.3 Feedforward Neural Networks\", slp_chapter7_3),\n",
        "  (\"SLP Chapter 7.4 Feedforward networks for NLP: Classification\", slp_chapter7_4),\n",
        "  (\"SLP Chapter 7.5 Feedforward Neural Language Modeling\", slp_chapter7_5),\n",
        "  (\"SLP Chapter 7.6 Training Neural Nets\", slp_chapter7_6),\n",
        "  (\"SLP Chapter 7.7 Training the neural language model\", slp_chapter7_7),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module13_markdown = format_module(13, \"Neural Networks\", \n",
        "                         module13_sections, \n",
        "                         module13_definitions)\n",
        "display(Markdown(module13_markdown))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n\nLogistic regression learns from a set of weights and a bias term then convert to a proper probability through sigmoid function.\\\nCE loss is lower if prediction is correct, and higher if prediction is incorrect.\\\nThe goal of stochastic gradient descent is to find the θ that minimizes CE loss across all training data.\\\nGradient descent starting from any point is guaranteed to find the minimum.\\\nThe magnitude of the amount to move in gradient descent is the value of the slope weighted by learning rate. Higher learning rate -> move w more on each step.\\\nTo train neural nets, we need 1) cross-entry loss function, 2) gradient descent to minimze loss, 3) error propagation to compute descent.\\\nUsing logistic regression to calculate descent only gives correct updates to the last layer, need to do error propagation to update all previous layers.\n\n\n## Module 13 Definitions\n* **Cross-entropy loss** - mathematically the negative log likelihood loss\n* **Feed-forward neural network** - multi-layer network, fully connected units, no cycles\n* **Gradient descent** - finds which direction the function's slope is rising the most steeply and move in the opposite direction\n* **Loss function** - distance between system output and gold output\n* **Regularization** - penalize large weights; helps reduce overfitting\n* **Softmax function** - a generalization of the sigmoid function used in multinomial logistic regression\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 6 “Vector Semantics and Embeddings” (6.1-6.4, 6.8, 6.10-6.12)\n",
        "\n",
        "slp_chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\"\n",
        "In term-document matrix, each cell shows frequency of the term occuring in that document.\\\\\n",
        "Document vectors are often sparse (most values are 0).\\\\\n",
        "By comparing their column vectors, we can measure how similar two documents are. \\\n",
        "By comparing their row vectors, can calculate similarity of words based on documents.\\\\\n",
        "In term-term matrix, each cell indicates how often w1 appears in a context window surrounding w2.\\\\\n",
        "Co-occurence matrices produce long and sparse vectors, whereas perceptron/neural networks \\\n",
        "produce short and dense vectors.\\\\\n",
        "To get dense vectors, use logistic regression to train a classifier and use weights as embeddings.\\\\\n",
        "In Word2vec, we train a binary classifier to predict if a word likely to show up near target word, \\\n",
        "and use the classifier weights as the word embeddings.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"L2 normalization\" : \"normalizes based on length of document (# of words)\",\n",
        "\"TF-IDF\": \"term frequency * inverse document frequency\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3191fd2f-5403-4882-870e-f0e4e25a5ad1"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module14_sections = [\n",
        "  (\"SLP Chapter 9.1 Language Models Revisited\", slp_chapter9_1),\n",
        "  (\"SLP Chapter 9.2 Recurrent Neural Networks\", slp_chapter9_2),\n",
        "  (\"SLP Chapter 9.3 RNNs as Language Models\", slp_chapter9_3),\n",
        "\n",
        "  (\"SLP Chapter 6.1 Lexical Semantics\", slp_chapter6_1),\n",
        "  (\"SLP Chapter 6.2 Vector Semantics\", slp_chapter6_2),\n",
        "  (\"SLP Chapter 6.3 Words and Vectors\", slp_chapter6_3),\n",
        "  (\"SLP Chapter 6.4 Cosine for measuring similarity\", slp_chapter6_4),\n",
        "  (\"SLP Chapter 6.8 Word2vec\", slp_chapter6_8),\n",
        "  (\"SLP Chapter 6.10 Semantic properties of embeddings\", slp_chapter6_10),\n",
        "  (\"SLP Chapter 6.11 Bias and Embeddings\", slp_chapter6_11),\n",
        "  (\"SLP Chapter 6.12 Evaluating Vector Models\", slp_chapter6_12),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module14_markdown = format_module(14, \"Natural Language Processing\", \n",
        "                         module14_sections, \n",
        "                         module14_definitions)\n",
        "display(Markdown(module14_markdown))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n\nLogistic regression learns from a set of weights and a bias term then convert to a proper probability through sigmoid function.\\\nCE loss is lower if prediction is correct, and higher if prediction is incorrect.\\\nThe goal of stochastic gradient descent is to find the θ that minimizes CE loss across all training data.\\\nGradient descent starting from any point is guaranteed to find the minimum.\\\nThe magnitude of the amount to move in gradient descent is the value of the slope weighted by learning rate. Higher learning rate -> move w more on each step.\\\nTo train neural nets, we need 1) cross-entry loss function, 2) gradient descent to minimze loss, 3) error propagation to compute descent.\\\nUsing logistic regression to calculate descent only gives correct updates to the last layer, need to do error propagation to update all previous layers.\n\n\n## Module 14 Definitions\n* **L2 normalization** - normalizes based on length of document (# of words)\n* **Tf-idf** - term frequency * inverse document frequency\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "ee1443a6-c70d-42ed-998a-77ed631a7c0b"
      },
      "source": [
        "# This will generate the full set of notes that you can use for your exam.\n",
        "full_markdown = \"\\n\\n\".join([module8_markdown, \n",
        "                             module9_markdown, \n",
        "                             module10_markdown, \n",
        "                             module11_markdown, \n",
        "                             module12_markdown,\n",
        "                             module13_markdown,\n",
        "                             module14_markdown,]\n",
        "                       )\n",
        "display(Markdown(full_markdown))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary. \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Your summary here \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n\nStochastic transition function is the probabilty of arriving in state s' given state s and selecting action a: $p(s' | s, a)$.\\\nMDP is a sequential decision problem for a fully observable environment with a stochastic transition model that has additive rewards.\\\nComponents of MDP: states, actions, transition fn, reward fn, initial state, terminal states.\\\nValue (utility) of a state is the expected utility starting in s and acting optimally.\\\nValue of a q-state is the expected utility starting out having taken action a from state s and thereafter acting optimally.\\\nIn value iteration, start at $V_0=0$, do one ply of expectimax from each state and repeat until convergence.\\\nUtility of a state under a fixed policy is the expected total discounted rewards starting in s and following π.\\\nPolicy iteration is better than value iteration because: 1) faster to converges, 2) more efficient $O(s^2)$.\\\nIn policy iteration, do 1) policy evaluation: calculate utilities for some fixed policy (doesn't have to be optimal) until convergence, then 2) policy improvement: update policy using one-step look-ahead (policy extraction) with resulting converged utilities as future values, and repeat until policy converges.\\\nIn value iteration, each iteration updates explicitly the values and implicitly the policy but we don't track the policy.\\\nIn policy iteration, each iteration considers one action under a fixed policy, the policy is evalulated and better one is chosen (improved).\\\nIn worst-case minimax reasoning, only ordering matters; in average-case expectimax, magnitudes matter.\\\nAny rational preferences can be summarized as a utility function.\\\nWith uncertainty, outcomes depend on utility and probability.\\\nUnder MEU principle, agent choose the action that maximizes expected utility.\\\nUtility of having money: people are generally risk averse but risk-prone when deep in debt.\n\n\n## Module 8 Definitions\n* **Absorbing state** - guarantees a terminal state will eventually be reached for every policy\n* **Axiom of transitivity** - if A>B and B>C, then A>C\n* **Bellman equations** - gives a one-step lookahead relationship amontst optimal utility values\n* **Discounting** - values of rewards decay exponentially; built into utility fn\n* **Environmental history** - sequence of (state, action) pairs\n* **Expected utility** - sum of all possible paths generated by the policy\n* **Insensitivity to monotonic transformation** - only ordering matters, magnitude does not\n* **Markovian** - probability of arriving in s' depends on s and not the history of earlier states\n* **Maximum expected utility (meu)** - rational agent should choose the action that maximizes its expected utility given its knowledge\n* **Policy (π)** - maps from a state to the best action which depends on the reward\n* **Policy extraction** - extract the optimal policy given a set of optimal (q-)values\n* **Rational preferences** - imply behaviors describable as maximazation of expected utility\n* **Utilities** - functions from outcomes to real numbers that describe an agent's preferences\n* **Utility function** - sum of rewards from a sequence of state and actions\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Your summary here \n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Your summary here \n\n## Module 9 Lecture Notes\n\nReinforcement learning involves receiving feedback in the form rewards and must learn to act so as to maximized expected rewards, where all learning is based on observed samples of outcomes.\\\nIn reinforcement learning, transition model and reward function is unknown.\\\nIn model-based learning, 1) learn empirical MDP model by counting outcomes then 2) solve the learned MDP.\\\nPassive reinforcement learning with a fixed policy is still active.\\\nIn TD learning, need to learn q-values not values in order to get optimal policy.\\\nIn modified Q-update, when visit count n is small, a bonus is given to explore this state.\\\nFeature representation enables us to write a q-fn for any state using a few weights.\\\nIn approx. Q-learning, adjust weights of active features and disprefer states by lowering weights.\n\n\n## Module 9 Definitions\n* **Approx. q-learning** - Q-learning with feature representation\n* **Direct evaluation** - compute values for each state under fixed policy and average observed sample values\n* **Exploitation** - usse what you know/have previous learned\n* **Exploration** - try unknown actions to get information\n* **Exploration function** - takes a value estimate and a visit count, and return an optimistic utility\n* **Model-based learning** - an approximate model is learned based on experiences\n* **Off-policy learning** - Q-learning converges even if acting suboptimally\n* **Regret** - mistake cost - difference between expected and optimal rewards\n* **Temporal difference learning** - update V(s) each time; likely outcomes will update more often\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n\nProbabilistic reasoning gives a framework to use evidence to reason about how likely particular outcome is.\\\nAxioms of probability: MECE.\\\nSize of join distribution with n variables and each domain size d: $d^n$.\\\nRandom variables are categoized into 3 types: 1) evidence variables (known), 2) query variable (unknown), 3) hidden variables (part of join prob. but no impact).\\\nTo do inference by enumeration - P(Q|e1...ek): 1) select the entires consistent with evidence, 2) sum out H to get joint of Q and E, 3) normalize.\\\nBayes' rule allows us to calculate conditional from its reverse.\n\n\n## Module 10 Definitions\n* **Conditional probability** - prob of A given B\n* **Evidence** - observed variables\n* **Markov assumption** - current word depends on the previous k words, not the whole context\n* **Probabilistic inference** - to compute a desired probability from other known probs.\n* **Probabilistic model** - join distribution over a set of random variables\n* **Probabilisitc language models** - computes prob of a sentence or sequence of words\n* **States** - unobserved variables\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n\nAbsolute independence doesn't help us to make inferences about other variables.\\\nEach arc in Bayes' net represents a conditional probability (local interaction).\\\nBayes' net is comprised of a topology (graph) and local conditional probabilities.\\\nBayes' net implicitly encode joint distributon as the product of local conditional distributions.\\\nSize of joint distribution tables: $2^N$; size of N-node net with up to k parents: $O(N*2^{k+1})$.\\\nCause chain: evidence along the chain \"blocks\" the influence.\\\nCommon cause: observing the cause blocks influence between effects.\\\nCommon effect: observing an effect activates influence between possible causes.\\\nD-separation: 1) if one or more path is active, indenpendence is not guaranteed.2) if all paths are inactive then independence is guaranteed.3) a path is active if each triple is active.\n\n\n## Module 11 Definitions\n* **Active path** - \n* **Bayes' net** - a technique for describing complex joint distributions (models) using local distributions (cond prob)\n* **Causal chain** - X -> Y -> Z; X and Z absolute independence NO, conditional independence YES\n* **Common cause** - Y -> X, Y -> Z; X and Z absolute independence NO, conditional independence YES\n* **Common effect** - X -> Z, Y -> Z; X and Y absolute independence YES, X and Y given Z conditional independence NO\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n\nIn the general Naive Bayes model, number of parameters is linear in number of features, and we specify how each feature depends on the class.\\\nTo do inference for naive Bayes, 1) get joint probability of label and evidence for each label, 2) sum to get prob of evidence, 3) normalize by dividing step 1 by step 2.\\\nNote that the local conditioanl tables in step 1 come from machine learning.\\\nProcedure for machine learning: 1) learn parameters on training set, 2) tune hyperparameters on held-out set, 3) evaluate accuracy (fraction of instances predicted correctly) on test set.\\\nMaximum likelihood estimates fail to account for unseen events.\\\nIncreasing k in Laplace smoothing makes the probabilty closer to equi-probable.\\\nIn binary perceptron, initialize weights=0, if $y=y*$ do no change, else adjust weight vector.\\\nIn multiclass perceptron, initialize weights=0, predict with current weights and use the highest score, if correct do no change, else lower weight for wrong answer and raise weight for correct answer.\\\nProblems with perceptron: noise (not separable), mediocre generalization, overtraining (kind of overfitting).\\\nPerceptrons make less assumptions, is mistake-driven and does multiple passes through data to predict.\n\n\n## Module 12 Definitions\n* **Bag-of-words model** - insensitive to ordering, each position is identically distributed and share the same cond prob.\n* **Confidence** - $max_y P(y|x)$, represents how sure the classifier is\n* **Convergence** - if separable, perceptron will eventually converge\n* **Laplace smoothing** - pretend saw every outcome k extra times\n* **Linear classifiers** - activation fn is the dot product of feature values (inputs) and weights\n* **Maximum likelihood** - estimate that maximizes likelihood of the data by looking at the empirical rate of the values\n* **Mira** - choose an update size that fixes the current mistake but minimizes changes to w\n* **Mistake bound** - max number of mistakes related to the margin or degree of separability\n* **Naive bayes** - assume all features are independence effects of the label (ie, F1 and F2 cond. indp. given Y)\n* **Overfitting** - fitting the training data too closely but not generalizing well\n* **Separability** - true if some parameters get the training set perfectly correct\n* **Svm** - find the separator with max margin\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n\nLogistic regression learns from a set of weights and a bias term then convert to a proper probability through sigmoid function.\\\nCE loss is lower if prediction is correct, and higher if prediction is incorrect.\\\nThe goal of stochastic gradient descent is to find the θ that minimizes CE loss across all training data.\\\nGradient descent starting from any point is guaranteed to find the minimum.\\\nThe magnitude of the amount to move in gradient descent is the value of the slope weighted by learning rate. Higher learning rate -> move w more on each step.\\\nTo train neural nets, we need 1) cross-entry loss function, 2) gradient descent to minimze loss, 3) error propagation to compute descent.\\\nUsing logistic regression to calculate descent only gives correct updates to the last layer, need to do error propagation to update all previous layers.\n\n\n## Module 13 Definitions\n* **Cross-entropy loss** - mathematically the negative log likelihood loss\n* **Feed-forward neural network** - multi-layer network, fully connected units, no cycles\n* **Gradient descent** - finds which direction the function's slope is rising the most steeply and move in the opposite direction\n* **Loss function** - distance between system output and gold output\n* **Regularization** - penalize large weights; helps reduce overfitting\n* **Softmax function** - a generalization of the sigmoid function used in multinomial logistic regression\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n\nLogistic regression learns from a set of weights and a bias term then convert to a proper probability through sigmoid function.\\\nCE loss is lower if prediction is correct, and higher if prediction is incorrect.\\\nThe goal of stochastic gradient descent is to find the θ that minimizes CE loss across all training data.\\\nGradient descent starting from any point is guaranteed to find the minimum.\\\nThe magnitude of the amount to move in gradient descent is the value of the slope weighted by learning rate. Higher learning rate -> move w more on each step.\\\nTo train neural nets, we need 1) cross-entry loss function, 2) gradient descent to minimze loss, 3) error propagation to compute descent.\\\nUsing logistic regression to calculate descent only gives correct updates to the last layer, need to do error propagation to update all previous layers.\n\n\n## Module 14 Definitions\n* **L2 normalization** - normalizes based on length of document (# of words)\n* **Tf-idf** - term frequency * inverse document frequency\n",
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