{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Template_for CIS521_midterm_2_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": "e727216e-db93-464d-fdee-3d93d91129b2"
      },
      "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": null,
      "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": "ca588c1c-071e-465d-f132-49bb2ca4f78c"
      },
      "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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29c16614-03f1-4b8f-8ed9-f5cce2b757ed"
      },
      "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. \"\"\"  + sample_equation\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 = \"\"\" Solving MDP using expectimax.  \n",
        "A transition function T(s, a, s’):    \n",
        "Probability that executing action a in s will lead to s’ P(s’ | s, a).   \n",
        "The performance of an agent in an MDP is the sum\n",
        "of the rewards for the transitions it takes.  \n",
        "Each time we descend a level, we\n",
        "multiply in the discount once. \n",
        "If we assume stationary preferences: then additive utility vs. discounted utility. \n",
        "Infinite utility problem: solved by limited horizon (similar to depth limited search)  \n",
        "or discounting.  \n",
        "Markov decision processes:  \n",
        "  • Set of states S.   \n",
        "  • Start state s0.   \n",
        "  • Set of actions A.  \n",
        "  • Transitions P(s’|s,a) (or T(s,a,s’)).   \n",
        "  • Rewards R(s,a,s’) (and discount.). \n",
        "\n",
        "MDP quantities so far:  \n",
        " • Policy = Choice of action for each state. \n",
        " • Utility = sum of (discounted) rewards. \n",
        "V*(s) = expected utility starting in s and\n",
        "acting optimally. \n",
        "Q*(s,a) = expected utility starting out\n",
        "having taken action a from state s and\n",
        "(thereafter) acting optimally. \n",
        "\n",
        "PI*(s) = optimal action from state s.   \n",
        "Time-limited values: Define Vk\n",
        "(s) to be the optimal value of s if the game\n",
        "ends in k more time steps. \n",
        "Value iteration: Complexity of each iteration: O(S2A).   \n",
        "Basic idea: approximations get refined towards optimal values. \n",
        "Policy may converge long before values do.   \n",
        "Policy Evaluation: O(S^2) per iteration. \n",
        "Problems of value iteration:  \n",
        " Problem 1: It’s slow – O(S2A) per iteration. \n",
        "Problem 2: The “max” at each state rarely changes. \n",
        "Problem 3: The policy often converges long before the values. \n",
        "policy iteration:  \n",
        "Step 1: Policy evaluation: calculate utilities for some fixed policy (not\n",
        "optimal utilities!) until convergence. \n",
        "Step 2: Policy improvement: update policy using one-step lookahead with resulting converged (but not optimal!) utilities as future\n",
        "values. \n",
        "Repeat steps until policy converges. \n",
        "Principle of maximum expected utility:  \n",
        "▪ A rational agent should choose the action that maximizes its expected utility,\n",
        "given its knowledge. \n",
        "\n",
        " \"\"\"\n",
        "![picture](https://drive.google.com/uc?/d/1to-2y12c1ykvMlNwLrXQbp8qKDB8XpCM/view?usp=sharing)\n",
        "\n",
        "# Definitions of concepts from Module 8.\n",
        "# You can define any term in the textbook or lecture, \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",
        "\"utility function\" : \"Your definition here\",\n",
        "\"expected utility\" : \"your definition here\",\n",
        "\"etc.\": \"your definition here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 0: syntax error near unexpected token `https://drive.google.com/uc?/d/1to-2y12c1ykvMlNwLrXQbp8qKDB8XpCM/view?usp=sharing'\n",
            "/bin/bash: -c: line 0: `[picture](https://drive.google.com/uc?/d/1to-2y12c1ykvMlNwLrXQbp8qKDB8XpCM/view?usp=sharing)'\n"
          ]
        }
      ]
    },
    {
      "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": 1011
        },
        "outputId": "9805697b-517f-4f96-f81c-aa15d116598e"
      },
      "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": null,
      "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.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \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 Solving MDP using expectimax.  \nA transition function T(s, a, s’):    \nProbability that executing action a in s will lead to s’ P(s’ | s, a).   \nThe performance of an agent in an MDP is the sum\nof the rewards for the transitions it takes.  \nEach time we descend a level, we\nmultiply in the discount once. \nIf we assume stationary preferences: then additive utility vs. discounted utility. \nInfinite utility problem: solved by limited horizon (similar to depth limited search)  \nor discounting.  \nMarkov decision processes:  \n  • Set of states S.   \n  • Start state s0.   \n  • Set of actions A.  \n  • Transitions P(s’|s,a) (or T(s,a,s’)).   \n  • Rewards R(s,a,s’) (and discount.). \n\nMDP quantities so far:  \n • Policy = Choice of action for each state. \n • Utility = sum of (discounted) rewards. \nV*(s) = expected utility starting in s and\nacting optimally. \nQ*(s,a) = expected utility starting out\nhaving taken action a from state s and\n(thereafter) acting optimally. \n\nPI*(s) = optimal action from state s.   \nTime-limited values: Define Vk\n(s) to be the optimal value of s if the game\nends in k more time steps. \nValue iteration: Complexity of each iteration: O(S2A).   \nBasic idea: approximations get refined towards optimal values. \nPolicy may converge long before values do.   \nPolicy Evaluation: O(S^2) per iteration. \nProblems of value iteration:  \n Problem 1: It’s slow – O(S2A) per iteration. \nProblem 2: The “max” at each state rarely changes. \nProblem 3: The policy often converges long before the values. \npolicy iteration:  \nStep 1: Policy evaluation: calculate utilities for some fixed policy (not\noptimal utilities!) until convergence. \nStep 2: Policy improvement: update policy using one-step lookahead with resulting converged (but not optimal!) utilities as future\nvalues. \nRepeat steps until policy converges. \nPrinciple of maximum expected utility:  \n▪ A rational agent should choose the action that maximizes its expected utility,\ngiven its knowledge. \n\n \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\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 = \"\"\" Exploration: you have to try unknown actions to get information. \n",
        "Exploitation: eventually, you have to use what you know. \n",
        "Regret: even if you learn intelligently, you make mistakes. \n",
        "Sampling: because of chance, you have to try things repeatedly. \n",
        "Difficulty: learning can be much harder than solving a known MDP. \n",
        "Model-based learning:  \n",
        "Count outcomes s’ for each s, a.   \n",
        "Normalize to give an estimate of T(s, a, s'). \n",
        "Discover each R(s, a, s') when we experience (s, a, s’).   \n",
        "Step 2: Solve the learned MDP. \n",
        "Passive Reinforcement learning: policy evaluation. \n",
        "Direct Evaluation : Act according to π\n",
        "Every time you visit a state, write down what the\n",
        "sum of discounted rewards turned out to be\n",
        "Average those samples. \n",
        "Temporal Difference Learning: formula on hand written sheet.  \n",
        "However, if we want to turn values into a (new) policy, we’re sunk.  \n",
        "Learn Q-values, not values!  \n",
        "Q value iteration: see hand written notes.  \n",
        "Q Learning: see hand written notes.  Off policy learning.  \n",
        "Exploration: epsilon greedy, exploration function: Takes a value estimate u and a visit count n, and\n",
        "returns an optimistic utility. \n",
        "Modified Q-update: see hand written notes.  \n",
        "Approximate Q learning: linear value functions. \n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "outputId": "bc5fa28d-c979-4c5d-acc1-2a46b17180cd"
      },
      "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": null,
      "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 Exploration: you have to try unknown actions to get information. \nExploitation: eventually, you have to use what you know. \nRegret: even if you learn intelligently, you make mistakes. \nSampling: because of chance, you have to try things repeatedly. \nDifficulty: learning can be much harder than solving a known MDP. \nModel-based learning:  \nCount outcomes s’ for each s, a.   \nNormalize to give an estimate of T(s, a, s'). \nDiscover each R(s, a, s') when we experience (s, a, s’).   \nStep 2: Solve the learned MDP. \nPassive Reinforcement learning: policy evaluation. \nDirect Evaluation : Act according to π\nEvery time you visit a state, write down what the\nsum of discounted rewards turned out to be\nAverage those samples. \nTemporal Difference Learning: formula on hand written sheet.  \nHowever, if we want to turn values into a (new) policy, we’re sunk.  \nLearn Q-values, not values!  \nQ value iteration: see hand written notes.  \nQ Learning: see hand written notes.  Off policy learning.  \nExploration: epsilon greedy, exploration function: Takes a value estimate u and a visit count n, and\nreturns an optimistic utility. \nModified Q-update: see hand written notes.  \nApproximate Q learning: linear value functions. \n\n\n\n## Module 9 Definitions\n* **Term** - your definition here\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 = \"\"\" A probabilistic model is a joint distribution\n",
        "over a set of random variables. \n",
        "Probabilistic models:  \n",
        "• (Random) variables with domains. \n",
        "• Assignments are called outcomes. \n",
        "• Joint distributions: say whether\n",
        "assignments (outcomes) are likely. \n",
        "• Normalized: sum to 1.0. \n",
        "• Ideally: only certain variables directly\n",
        "interact. \n",
        "Marginal distributions are sub-tables which eliminate variables. \n",
        "Conditional Probablity: P(a|b) = P(a,b)/P(b). \n",
        "Conditional distributions are probability distributions\n",
        "over some variables given fixed values of others. \n",
        "The product rule: P(y)P(x|y)  = P(x,y). \n",
        "Chain rule: P(x1,x2,x3) = P(x1)*P(x2|x1)*P(x3|x1,x2). \n",
        "Bayes Rule: P(x|y) = P(y|x)P(x)/P(y). \n",
        "Markov assumption for language model. \n",
        "problem for MLE Out of vocabulary items (OOV). \n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1148
        },
        "cellView": "form",
        "outputId": "987311ae-eb90-4acd-9be8-57bd9bbe6352"
      },
      "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": null,
      "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 A probabilistic model is a joint distribution\nover a set of random variables. \nProbabilistic models:  \n• (Random) variables with domains. \n• Assignments are called outcomes. \n• Joint distributions: say whether\nassignments (outcomes) are likely. \n• Normalized: sum to 1.0. \n• Ideally: only certain variables directly\ninteract. \nMarginal distributions are sub-tables which eliminate variables. \nConditional Probablity: P(a|b) = P(a,b)/P(b). \nConditional distributions are probability distributions\nover some variables given fixed values of others. \nThe product rule: P(y)P(x|y)  = P(x,y). \nChain rule: P(x1,x2,x3) = P(x1)*P(x2|x1)*P(x3|x1,x2). \nBayes Rule: P(x|y) = P(y|x)P(x)/P(y). \nMarkov assumption for language model. \nproblem for MLE Out of vocabulary items (OOV). \n\n\n\n## Module 10 Definitions\n* **Term** - your definition here\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 = \"\"\" Independence: for all x, y, P(x, y) = P(x) * P(y). \n",
        "Conditional independence is our most basic and robust form\n",
        "of knowledge about uncertain environments.  \n",
        "Conditional independence requirement: see hand written notes.  \n",
        "Bayes’ nets: a technique for describing complex joint\n",
        "distributions (models) using simple, local\n",
        "distributions (conditional probabilities). \n",
        "Nodes: variables (with domains):  \n",
        "Can be assigned (observed) or unassigned\n",
        "(unobserved). \n",
        "Arcs: interactions:  \n",
        "Indicate “direct influence” between variables. \n",
        "Formally: encode conditional independence.(more later). \n",
        "Bayes Net semantics:\n",
        " A set of nodes, one per variable X. \n",
        "§ A directed, acyclic graph. \n",
        "§ A conditional distribution for each node. \n",
        "To see what probability a BN gives to a full assignment, multiply all the\n",
        "relevant conditionals together: see hand written sheet for formula.   \n",
        "Question: Are X and Y conditionally independent given\n",
        "evidence variables {Z}?  \n",
        "§ Yes, if X and Y “d-separated” by Z. \n",
        "§ Consider all (undirected) paths from X to Y. \n",
        "§ No active paths = independence!  \n",
        "A path is active if each triple is active:  \n",
        "§ Causal chain A ® B ® C where B is unobserved (either direction). \n",
        "§ Common cause A ¬ B ® C where B is unobserved. \n",
        "§ Common effect (aka v-structure). \n",
        "A ® B ¬ C where B or one of its descendents is observed. \n",
        "Bayes nets compactly encode joint distributions. \n",
        "Guaranteed independencies of distributions can be\n",
        "deduced from BN graph structure. \n",
        "D-separation gives precise conditional independence\n",
        "guarantees from graph alone. \n",
        "\n",
        "\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "cellView": "form",
        "outputId": "5a82adf9-da2f-42fe-dafb-8091113e34a9"
      },
      "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": null,
      "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 Independence: for all x, y, P(x, y) = P(x) * P(y). \nConditional independence is our most basic and robust form\nof knowledge about uncertain environments.  \nConditional independence requirement: see hand written notes.  \nBayes’ nets: a technique for describing complex joint\ndistributions (models) using simple, local\ndistributions (conditional probabilities). \nNodes: variables (with domains):  \nCan be assigned (observed) or unassigned\n(unobserved). \nArcs: interactions:  \nIndicate “direct influence” between variables. \nFormally: encode conditional independence.(more later). \nBayes Net semantics:\n A set of nodes, one per variable X. \n§ A directed, acyclic graph. \n§ A conditional distribution for each node. \nTo see what probability a BN gives to a full assignment, multiply all the\nrelevant conditionals together: see hand written sheet for formula.   \nQuestion: Are X and Y conditionally independent given\nevidence variables {Z}?  \n§ Yes, if X and Y “d-separated” by Z. \n§ Consider all (undirected) paths from X to Y. \n§ No active paths = independence!  \nA path is active if each triple is active:  \n§ Causal chain A ® B ® C where B is unobserved (either direction). \n§ Common cause A ¬ B ® C where B is unobserved. \n§ Common effect (aka v-structure). \nA ® B ¬ C where B or one of its descendents is observed. \nBayes nets compactly encode joint distributions. \nGuaranteed independencies of distributions can be\ndeduced from BN graph structure. \nD-separation gives precise conditional independence\nguarantees from graph alone. \n\n\n \n\n## Module 11 Definitions\n* **Concept** - definition\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 = \"\"\" Model-based approach machine learning:  \n",
        "• Build a model (e.g. Bayes’ net) where both the label and\n",
        "features are random variables. \n",
        "• Instantiate any observed features. \n",
        "• Query for the distribution of the\n",
        "label conditioned on the features. \n",
        "Naïve Bayes: Assume all features are independent effects of the label. \n",
        "Experimentation cycle:  \n",
        "• Learn parameters (e.g. model probabilities) on training set. \n",
        "• (Tune hyperparameters on held-out set). \n",
        "• Compute accuracy of test set. \n",
        "• Very important: never “peek” at the test set!  \n",
        "Relative frequencies are the maximum likelihood estimates. \n",
        "Laplace’s estimate: • Pretend you saw every outcome\n",
        "once more than you actually did. \n",
        "Another option: linear interpolation\n",
        "• Also get the empirical P(X) from the data\n",
        "• Make sure the estimate of P(X|Y) isn’t too different from the\n",
        "empirical P(X). \n",
        "The confidence of a probabilistic classifier (formula see hand written notes):  \n",
        "Represents how sure the classifier is of\n",
        "the classification. \n",
        "• Any probabilistic model will have\n",
        "confidences. \n",
        "• No guarantee confidence is correct. \n",
        "Linear Classifiers: Inputs are feature values\n",
        "Each feature has a weight\n",
        "Sum is the activation. \n",
        "Properties of Perceptron:  \n",
        "Separability: true if some parameters get the training\n",
        "set perfectly correct. \n",
        "Convergence: if the training is separable, perceptron\n",
        "will eventually converge (binary case). \n",
        "Mistake Bound: the maximum number of mistakes\n",
        "(binary case) related to the margin or degree of\n",
        "separability. \n",
        "Maximizing the margin: good according to intuition, theory, practice\n",
        "Only support vectors matter; other training examples are ignorable\n",
        "Support vector machines (SVMs) find the separator with max\n",
        "margin. \n",
        "Naïve Bayes. \n",
        "• Builds a model training data. \n",
        "• Gives prediction probabilities. \n",
        "• Strong assumptions about feature independence. \n",
        "• One pass through data (counting). \n",
        "Perceptrons / MIRA:  \n",
        "• Makes less assumptions about data. \n",
        "• Mistake-driven learning. \n",
        "• Multiple passes through data (prediction). \n",
        "• Often more accurate. \n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1539
        },
        "cellView": "form",
        "outputId": "46a035ef-58d4-4f15-b625-5c123b6ee8ca"
      },
      "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": null,
      "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 Model-based approach machine learning:  \n• Build a model (e.g. Bayes’ net) where both the label and\nfeatures are random variables. \n• Instantiate any observed features. \n• Query for the distribution of the\nlabel conditioned on the features. \nNaïve Bayes: Assume all features are independent effects of the label. \nExperimentation cycle:  \n• Learn parameters (e.g. model probabilities) on training set. \n• (Tune hyperparameters on held-out set). \n• Compute accuracy of test set. \n• Very important: never “peek” at the test set!  \nRelative frequencies are the maximum likelihood estimates. \nLaplace’s estimate: • Pretend you saw every outcome\nonce more than you actually did. \nAnother option: linear interpolation\n• Also get the empirical P(X) from the data\n• Make sure the estimate of P(X|Y) isn’t too different from the\nempirical P(X). \nThe confidence of a probabilistic classifier (formula see hand written notes):  \nRepresents how sure the classifier is of\nthe classification. \n• Any probabilistic model will have\nconfidences. \n• No guarantee confidence is correct. \nLinear Classifiers: Inputs are feature values\nEach feature has a weight\nSum is the activation. \nProperties of Perceptron:  \nSeparability: true if some parameters get the training\nset perfectly correct. \nConvergence: if the training is separable, perceptron\nwill eventually converge (binary case). \nMistake Bound: the maximum number of mistakes\n(binary case) related to the margin or degree of\nseparability. \nMaximizing the margin: good according to intuition, theory, practice\nOnly support vectors matter; other training examples are ignorable\nSupport vector machines (SVMs) find the separator with max\nmargin. \nNaïve Bayes. \n• Builds a model training data. \n• Gives prediction probabilities. \n• Strong assumptions about feature independence. \n• One pass through data (counting). \nPerceptrons / MIRA:  \n• Makes less assumptions about data. \n• Mistake-driven learning. \n• Multiple passes through data (prediction). \n• Often more accurate. \n \n\n## Module 12 Definitions\n* **Concept** - definition\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 = \"\"\" 1. An objective function or loss function that tells us distance between\n",
        "the system output and the gold output. We will use cross-entropy\n",
        "loss.  \n",
        "2. An algorithm for optimizing the objective function. We will use\n",
        "stochastic gradient descent to minimize the loss function.   \n",
        "We use gradient descent to find good settings for our weights and bias by minimizing\n",
        "the loss function.  \n",
        "Gradient descent is a method that finds a minimum of a function by figuring out in\n",
        "which direction (in the space of the parameters θ) the function’s slope is rising the most\n",
        "steeply, and moving in the opposite direction.  \n",
        "Gradient Descent formula: see hand written notes. \n",
        "An alternate is batch training, which computes the gradient over the entire dataset.\n",
        "This gives a much better estimate of which direction to move the weights but takes a\n",
        "long time to compute.  \n",
        "A commonly used compromise is mini-batch training, where we train on a small\n",
        "batch. The batch size can be 512 or 1024, often selected based on computational\n",
        "resources, so that all examples in the mini-batch can be processed in parallel. The loss\n",
        "is then accumulated.  \n",
        "The simplest kind of NN is the Feed-Forward Neural Network. \n",
        "Multilayer network, all units are usually fully-connected, and no cycles.  \n",
        "The outputs from each layer are passed to units in the next higher layer, and no\n",
        "outputs are passed back to lower layers.  \n",
        "    \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1260
        },
        "cellView": "form",
        "outputId": "68064f03-1009-4439-a728-0d867f376adb"
      },
      "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": null,
      "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 1. An objective function or loss function that tells us distance between\nthe system output and the gold output. We will use cross-entropy\nloss.  \n2. An algorithm for optimizing the objective function. We will use\nstochastic gradient descent to minimize the loss function.   \nWe use gradient descent to find good settings for our weights and bias by minimizing\nthe loss function.  \nGradient descent is a method that finds a minimum of a function by figuring out in\nwhich direction (in the space of the parameters θ) the function’s slope is rising the most\nsteeply, and moving in the opposite direction.  \nGradient Descent formula: see hand written notes. \nAn alternate is batch training, which computes the gradient over the entire dataset.\nThis gives a much better estimate of which direction to move the weights but takes a\nlong time to compute.  \nA commonly used compromise is mini-batch training, where we train on a small\nbatch. The batch size can be 512 or 1024, often selected based on computational\nresources, so that all examples in the mini-batch can be processed in parallel. The loss\nis then accumulated.  \nThe simplest kind of NN is the Feed-Forward Neural Network. \nMultilayer network, all units are usually fully-connected, and no cycles.  \nThe outputs from each layer are passed to units in the next higher layer, and no\noutputs are passed back to lower layers.  \n    \n\n## Module 13 Definitions\n* **Concept** - definition\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 = \"\"\" Vector L2 normalization: divide each vector by its l2 length. \n",
        "What happens when we have a new (unseen) combination of n words?  \n",
        "1. Back-off. \n",
        "2. Smoothing / interpolation. \n",
        "We are basically just stitching together short sequences of observed\n",
        "words.  \n",
        "Neural Language models have several advantages over n-gram LMs:   \n",
        "1. They don’t need smoothing. \n",
        "2. They can handle much longer histories.  \n",
        "3. They can generalize over contexts of similar words.  \n",
        "4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs.  \n",
        "1. Associate each word in the vocabulary with a vector-representation, thereby\n",
        "creating a notion of similarity between words.  \n",
        "2. Express the joint probability function of a word sequence in terms of the word\n",
        "vectors for the words in that sequence.  \n",
        "3. Simultaneously learn the word vectors and the parameters of the function. \n",
        "Better results. They achieve better preplexity scores than SOTA n-gram LMs.  \n",
        "Larger N. NN LMs can scale to much larger orders of n. This is achievable because\n",
        "parameters are associated only with individual words, and not with n-grams.  \n",
        "They generalize across contexts. For example, by observing that the words blue,\n",
        "green, red, black, etc. appear in similar contexts, the model will be able to assign a\n",
        "reasonable score to the green car even though it never observed in training, because it\n",
        "did observe blue car and red car.  \n",
        "A by-product of training are word embeddings. \n",
        "How to get dense vectors:   \n",
        "1. Treat the target word and a neighboring context word as positive examples.  \n",
        "2. Randomly sample other words in the lexicon to get negative samples.  \n",
        "3. Use logistic regression to train a classifier to distinguish those two cases.  \n",
        "4. Use the weights as the embeddings.  \n",
        "  \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1289
        },
        "outputId": "027e9e85-db60-4960-d758-2e7a80b48049"
      },
      "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 14 Lecture Notes\", module14_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": null,
      "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 14 Lecture Notes\n Vector L2 normalization: divide each vector by its l2 length. \nWhat happens when we have a new (unseen) combination of n words?  \n1. Back-off. \n2. Smoothing / interpolation. \nWe are basically just stitching together short sequences of observed\nwords.  \nNeural Language models have several advantages over n-gram LMs:   \n1. They don’t need smoothing. \n2. They can handle much longer histories.  \n3. They can generalize over contexts of similar words.  \n4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs.  \n1. Associate each word in the vocabulary with a vector-representation, thereby\ncreating a notion of similarity between words.  \n2. Express the joint probability function of a word sequence in terms of the word\nvectors for the words in that sequence.  \n3. Simultaneously learn the word vectors and the parameters of the function. \nBetter results. They achieve better preplexity scores than SOTA n-gram LMs.  \nLarger N. NN LMs can scale to much larger orders of n. This is achievable because\nparameters are associated only with individual words, and not with n-grams.  \nThey generalize across contexts. For example, by observing that the words blue,\ngreen, red, black, etc. appear in similar contexts, the model will be able to assign a\nreasonable score to the green car even though it never observed in training, because it\ndid observe blue car and red car.  \nA by-product of training are word embeddings. \nHow to get dense vectors:   \n1. Treat the target word and a neighboring context word as positive examples.  \n2. Randomly sample other words in the lexicon to get negative samples.  \n3. Use logistic regression to train a classifier to distinguish those two cases.  \n4. Use the weights as the embeddings.  \n  \n\n## Module 14 Definitions\n* **Concept** - definition\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": 8289
        },
        "id": "T3cm1WV542_a",
        "outputId": "21962598-d0b3-44c3-dad3-1b7878ea3065"
      },
      "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": null,
      "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.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \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 Solving MDP using expectimax.  \nA transition function T(s, a, s’):    \nProbability that executing action a in s will lead to s’ P(s’ | s, a).   \nThe performance of an agent in an MDP is the sum\nof the rewards for the transitions it takes.  \nEach time we descend a level, we\nmultiply in the discount once. \nIf we assume stationary preferences: then additive utility vs. discounted utility. \nInfinite utility problem: solved by limited horizon (similar to depth limited search)  \nor discounting.  \nMarkov decision processes:  \n  • Set of states S.   \n  • Start state s0.   \n  • Set of actions A.  \n  • Transitions P(s’|s,a) (or T(s,a,s’)).   \n  • Rewards R(s,a,s’) (and discount.). \n\nMDP quantities so far:  \n • Policy = Choice of action for each state. \n • Utility = sum of (discounted) rewards. \nV*(s) = expected utility starting in s and\nacting optimally. \nQ*(s,a) = expected utility starting out\nhaving taken action a from state s and\n(thereafter) acting optimally. \n\nPI*(s) = optimal action from state s.   \nTime-limited values: Define Vk\n(s) to be the optimal value of s if the game\nends in k more time steps. \nValue iteration: Complexity of each iteration: O(S2A).   \nBasic idea: approximations get refined towards optimal values. \nPolicy may converge long before values do.   \nPolicy Evaluation: O(S^2) per iteration. \nProblems of value iteration:  \n Problem 1: It’s slow – O(S2A) per iteration. \nProblem 2: The “max” at each state rarely changes. \nProblem 3: The policy often converges long before the values. \npolicy iteration:  \nStep 1: Policy evaluation: calculate utilities for some fixed policy (not\noptimal utilities!) until convergence. \nStep 2: Policy improvement: update policy using one-step lookahead with resulting converged (but not optimal!) utilities as future\nvalues. \nRepeat steps until policy converges. \nPrinciple of maximum expected utility:  \n▪ A rational agent should choose the action that maximizes its expected utility,\ngiven its knowledge. \n\n \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\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 Exploration: you have to try unknown actions to get information. \nExploitation: eventually, you have to use what you know. \nRegret: even if you learn intelligently, you make mistakes. \nSampling: because of chance, you have to try things repeatedly. \nDifficulty: learning can be much harder than solving a known MDP. \nModel-based learning:  \nCount outcomes s’ for each s, a.   \nNormalize to give an estimate of T(s, a, s'). \nDiscover each R(s, a, s') when we experience (s, a, s’).   \nStep 2: Solve the learned MDP. \nPassive Reinforcement learning: policy evaluation. \nDirect Evaluation : Act according to π\nEvery time you visit a state, write down what the\nsum of discounted rewards turned out to be\nAverage those samples. \nTemporal Difference Learning: formula on hand written sheet.  \nHowever, if we want to turn values into a (new) policy, we’re sunk.  \nLearn Q-values, not values!  \nQ value iteration: see hand written notes.  \nQ Learning: see hand written notes.  Off policy learning.  \nExploration: epsilon greedy, exploration function: Takes a value estimate u and a visit count n, and\nreturns an optimistic utility. \nModified Q-update: see hand written notes.  \nApproximate Q learning: linear value functions. \n\n\n\n## Module 9 Definitions\n* **Term** - your definition here\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 A probabilistic model is a joint distribution\nover a set of random variables. \nProbabilistic models:  \n• (Random) variables with domains. \n• Assignments are called outcomes. \n• Joint distributions: say whether\nassignments (outcomes) are likely. \n• Normalized: sum to 1.0. \n• Ideally: only certain variables directly\ninteract. \nMarginal distributions are sub-tables which eliminate variables. \nConditional Probablity: P(a|b) = P(a,b)/P(b). \nConditional distributions are probability distributions\nover some variables given fixed values of others. \nThe product rule: P(y)P(x|y)  = P(x,y). \nChain rule: P(x1,x2,x3) = P(x1)*P(x2|x1)*P(x3|x1,x2). \nBayes Rule: P(x|y) = P(y|x)P(x)/P(y). \nMarkov assumption for language model. \nproblem for MLE Out of vocabulary items (OOV). \n\n\n\n## Module 10 Definitions\n* **Term** - your definition here\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 Independence: for all x, y, P(x, y) = P(x) * P(y). \nConditional independence is our most basic and robust form\nof knowledge about uncertain environments.  \nConditional independence requirement: see hand written notes.  \nBayes’ nets: a technique for describing complex joint\ndistributions (models) using simple, local\ndistributions (conditional probabilities). \nNodes: variables (with domains):  \nCan be assigned (observed) or unassigned\n(unobserved). \nArcs: interactions:  \nIndicate “direct influence” between variables. \nFormally: encode conditional independence.(more later). \nBayes Net semantics:\n A set of nodes, one per variable X. \n§ A directed, acyclic graph. \n§ A conditional distribution for each node. \nTo see what probability a BN gives to a full assignment, multiply all the\nrelevant conditionals together: see hand written sheet for formula.   \nQuestion: Are X and Y conditionally independent given\nevidence variables {Z}?  \n§ Yes, if X and Y “d-separated” by Z. \n§ Consider all (undirected) paths from X to Y. \n§ No active paths = independence!  \nA path is active if each triple is active:  \n§ Causal chain A ® B ® C where B is unobserved (either direction). \n§ Common cause A ¬ B ® C where B is unobserved. \n§ Common effect (aka v-structure). \nA ® B ¬ C where B or one of its descendents is observed. \nBayes nets compactly encode joint distributions. \nGuaranteed independencies of distributions can be\ndeduced from BN graph structure. \nD-separation gives precise conditional independence\nguarantees from graph alone. \n\n\n \n\n## Module 11 Definitions\n* **Concept** - definition\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 Model-based approach machine learning:  \n• Build a model (e.g. Bayes’ net) where both the label and\nfeatures are random variables. \n• Instantiate any observed features. \n• Query for the distribution of the\nlabel conditioned on the features. \nNaïve Bayes: Assume all features are independent effects of the label. \nExperimentation cycle:  \n• Learn parameters (e.g. model probabilities) on training set. \n• (Tune hyperparameters on held-out set). \n• Compute accuracy of test set. \n• Very important: never “peek” at the test set!  \nRelative frequencies are the maximum likelihood estimates. \nLaplace’s estimate: • Pretend you saw every outcome\nonce more than you actually did. \nAnother option: linear interpolation\n• Also get the empirical P(X) from the data\n• Make sure the estimate of P(X|Y) isn’t too different from the\nempirical P(X). \nThe confidence of a probabilistic classifier (formula see hand written notes):  \nRepresents how sure the classifier is of\nthe classification. \n• Any probabilistic model will have\nconfidences. \n• No guarantee confidence is correct. \nLinear Classifiers: Inputs are feature values\nEach feature has a weight\nSum is the activation. \nProperties of Perceptron:  \nSeparability: true if some parameters get the training\nset perfectly correct. \nConvergence: if the training is separable, perceptron\nwill eventually converge (binary case). \nMistake Bound: the maximum number of mistakes\n(binary case) related to the margin or degree of\nseparability. \nMaximizing the margin: good according to intuition, theory, practice\nOnly support vectors matter; other training examples are ignorable\nSupport vector machines (SVMs) find the separator with max\nmargin. \nNaïve Bayes. \n• Builds a model training data. \n• Gives prediction probabilities. \n• Strong assumptions about feature independence. \n• One pass through data (counting). \nPerceptrons / MIRA:  \n• Makes less assumptions about data. \n• Mistake-driven learning. \n• Multiple passes through data (prediction). \n• Often more accurate. \n \n\n## Module 12 Definitions\n* **Concept** - definition\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 1. An objective function or loss function that tells us distance between\nthe system output and the gold output. We will use cross-entropy\nloss.  \n2. An algorithm for optimizing the objective function. We will use\nstochastic gradient descent to minimize the loss function.   \nWe use gradient descent to find good settings for our weights and bias by minimizing\nthe loss function.  \nGradient descent is a method that finds a minimum of a function by figuring out in\nwhich direction (in the space of the parameters θ) the function’s slope is rising the most\nsteeply, and moving in the opposite direction.  \nGradient Descent formula: see hand written notes. \nAn alternate is batch training, which computes the gradient over the entire dataset.\nThis gives a much better estimate of which direction to move the weights but takes a\nlong time to compute.  \nA commonly used compromise is mini-batch training, where we train on a small\nbatch. The batch size can be 512 or 1024, often selected based on computational\nresources, so that all examples in the mini-batch can be processed in parallel. The loss\nis then accumulated.  \nThe simplest kind of NN is the Feed-Forward Neural Network. \nMultilayer network, all units are usually fully-connected, and no cycles.  \nThe outputs from each layer are passed to units in the next higher layer, and no\noutputs are passed back to lower layers.  \n    \n\n## Module 13 Definitions\n* **Concept** - definition\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 14 Lecture Notes\n Vector L2 normalization: divide each vector by its l2 length. \nWhat happens when we have a new (unseen) combination of n words?  \n1. Back-off. \n2. Smoothing / interpolation. \nWe are basically just stitching together short sequences of observed\nwords.  \nNeural Language models have several advantages over n-gram LMs:   \n1. They don’t need smoothing. \n2. They can handle much longer histories.  \n3. They can generalize over contexts of similar words.  \n4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs.  \n1. Associate each word in the vocabulary with a vector-representation, thereby\ncreating a notion of similarity between words.  \n2. Express the joint probability function of a word sequence in terms of the word\nvectors for the words in that sequence.  \n3. Simultaneously learn the word vectors and the parameters of the function. \nBetter results. They achieve better preplexity scores than SOTA n-gram LMs.  \nLarger N. NN LMs can scale to much larger orders of n. This is achievable because\nparameters are associated only with individual words, and not with n-grams.  \nThey generalize across contexts. For example, by observing that the words blue,\ngreen, red, black, etc. appear in similar contexts, the model will be able to assign a\nreasonable score to the green car even though it never observed in training, because it\ndid observe blue car and red car.  \nA by-product of training are word embeddings. \nHow to get dense vectors:   \n1. Treat the target word and a neighboring context word as positive examples.  \n2. Randomly sample other words in the lexicon to get negative samples.  \n3. Use logistic regression to train a classifier to distinguish those two cases.  \n4. Use the weights as the embeddings.  \n  \n\n## Module 14 Definitions\n* **Concept** - definition\n",
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