{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "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/",
          "height": 0
        },
        "outputId": "d9af8802-0667-4c88-dd57-a661b7229e5c"
      },
      "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/",
          "height": 0
        },
        "outputId": "938b5b86-4f6e-4a2e-bc8b-76dae2df2a58"
      },
      "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 = True"
      ],
      "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",
        "EU_equation = r\"\"\" $EU(a) = \\sum_s' P(\\textnormal{RESULT}(a)=s')U(s')$\"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" Utility and Preference Theory: \\n\n",
        "\n",
        "\"\"\"  + sample_equation + EU_equation\n",
        "\n",
        "chapter16_2 =  \"\"\"ORDERABILITY: Given any two lotteries, a rational agent must either prefer one or else rate them as equally preferable. \\n\n",
        "TRANSITIVITY: Given any three lotteries, if an agent prefers A to B and prefers B to C, then the agent must prefer A to C. \\n\n",
        "CONTINUITY: If A>B>C, then there is some probability p for which the rational agent will be indifferent between getting B \\\n",
        " for sure and the lottery that yields A with probability p and C with probability 1−p. \\n\n",
        "MONOTONICITY: Suppose two lotteries have the same two possible outcomes, A and B. \\\n",
        "  If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). \\n\"\"\"\n",
        "\n",
        "chapter16_3 =  \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" Markovian Transition Model : the probability of reaching \\\n",
        "s′ from s depends only on s and not on the history of earlier states. \\n\n",
        "Utility Function : Addition of Rewards of the environment history R(s,a,s') \\n\n",
        "Definition of MDP : a sequential decision problem for a fully observable, \\\n",
        "stochastic environment with a Markovian transition model and additive rewards \\n\n",
        "Solution to MDP is a Policy pi(s), optimal policy Maximizes EU of s and is stationary \\\n",
        "(does not change with time). \\n\n",
        "Utility = sum of discounted rewards \\n\n",
        "Avoid Infinite Utilities: 1. Finite horizon, Terminate episodes after a fixed T steps, this will cause non-stationary policies \\n\n",
        "2. Discounting: use 0 < gamma < 1 and utility will be bounded. 3. Absorbing state: guarantee that for every policy, a terminal state will \\\n",
        "eventually be reached. \\n\n",
        "\"\"\"\n",
        "chapter17_2 =  \"\"\" Convergence of Value Iteration: \\\n",
        "contraction is a function of one argument that, when applied to two different inputs in turn,\\\n",
        " produces two output values that are “closer together,” by at least some constant factor, than the original inputs. \\n\n",
        " Value Iteration: Init V(s) = 0; Do update V(s) for each s using bellman equations; Repeat until all V(s) converges. \\\n",
        " Complexity : O(S^2A), will converge to unique optimal values, policy may converge much earlier. \\n\n",
        " Problems of Value iter: too slow, max action rarely changes, policy can converge much earlier. \\n\n",
        " Policy Iteration: 1.  Policy evaluation: calculate utilities for some fixed policy (not\n",
        "  optimal utilities!) until convergence, 2.  Policy improvement: update policy using one-step \\\n",
        "  lookahead with resulting converged (but not optimal!) utilities as future values, 3. repeat until policy converges. \\n\n",
        " \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\" \"\"\"\n",
        "\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",
        "\"Utilities\" : \"functions from outcomes (states of the world) to real numbers that describe an agent’s preferences\",\n",
        "\"MEU\": \"a rational agent should choose the action that maximizes the agent’s expected utility\"\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": 830
        },
        "cellView": "form",
        "outputId": "cf1c8d8c-f93a-4c8b-a073-6fdcff5075e4"
      },
      "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 17.1 Sequential Decision Problems\", chapter17_1),\n",
        "  (\"AIMA Chapter 17.2 Algorithms for MDPs\", chapter17_2),\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 Utility and Preference Theory: \n\n\n $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ $EU(a) = \\sum_s' P(\\textnormal{RESULT}(a)=s')U(s')$\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nORDERABILITY: Given any two lotteries, a rational agent must either prefer one or else rate them as equally preferable. \n\nTRANSITIVITY: Given any three lotteries, if an agent prefers A to B and prefers B to C, then the agent must prefer A to C. \n\nCONTINUITY: If A>B>C, then there is some probability p for which the rational agent will be indifferent between getting B  for sure and the lottery that yields A with probability p and C with probability 1−p. \n\nMONOTONICITY: Suppose two lotteries have the same two possible outcomes, A and B.   If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). \n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Markovian Transition Model : the probability of reaching s′ from s depends only on s and not on the history of earlier states. \n\nUtility Function : Addition of Rewards of the environment history R(s,a,s') \n\nDefinition of MDP : a sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards \n\nSolution to MDP is a Policy pi(s), optimal policy Maximizes EU of s and is stationary (does not change with time). \n\nUtility = sum of discounted rewards \n\nAvoid Infinite Utilities: 1. Finite horizon, Terminate episodes after a fixed T steps, this will cause non-stationary policies \n\n2. Discounting: use 0 < gamma < 1 and utility will be bounded. 3. Absorbing state: guarantee that for every policy, a terminal state will eventually be reached. \n\n\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Convergence of Value Iteration: contraction is a function of one argument that, when applied to two different inputs in turn, produces two output values that are “closer together,” by at least some constant factor, than the original inputs. \n\n Value Iteration: Init V(s) = 0; Do update V(s) for each s using bellman equations; Repeat until all V(s) converges.  Complexity : O(S^2A), will converge to unique optimal values, policy may converge much earlier. \n\n Problems of Value iter: too slow, max action rarely changes, policy can converge much earlier. \n\n Policy Iteration: 1.  Policy evaluation: calculate utilities for some fixed policy (not\n  optimal utilities!) until convergence, 2.  Policy improvement: update policy using one-step   lookahead with resulting converged (but not optimal!) utilities as future values, 3. repeat until policy converges. \n\n \n\n## Module 8 Definitions\n* **Utilities** - functions from outcomes (states of the world) to real numbers that describe an agent’s preferences\n* **Meu** - a rational agent should choose the action that maximizes the agent’s expected utility\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 =  \"\"\" N-armed bandit problem: n levers. Behind each lever is a fixed but unknown probability distribution of winnings;\\\n",
        " each pull samples from that unknown distribution. Each lever is a MDP with only one possible action, overall problem is a MDP. \\n\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\" MDP is offline computing the optimal policy \\\n",
        "RL is actually doing the actions and learning from the rewards. \\n\n",
        "Model Based RL: learn an empirical model of MDP (learn T and R based on tried samples of a) \"\"\"\n",
        "chapter22_2 =  \"\"\" Passive RL: follow a fixed policy; find values of each s (Rewards(s) of starting from s and reach the end); \\n\n",
        "Direct Eval of Values: V(s) = Avg of empirical values of s, but need to learn each state separately, slow \\n\n",
        "Eval using policy Eval: To use policy eval to get V(s), we can use sample based T(s,a,s') and R(s,a,s'). \\n\n",
        "TD Learning: Update V(s) every time we experience a transition from s, still fixed policy. Just learning value but not updating policy.\"\"\"\n",
        "chapter22_3 =  \"\"\" Active RL: not using fixed policy, agent choose actions and learn the V(s) and optimal policy \\n\n",
        "Q-learning: sample based Q-value iteration, off-policy: converge to optimal policy even if start with non-optimal, need to explore enough actions \\n\n",
        "Forcing Exploration: e-greedy: with prob of epsilon, act randomly. \\\n",
        "Reduce epsilon over time, or use Exploration Function: Explore areas less visited. \\n\n",
        "Regret: a measure of total mistake cost: the difference between (expected) rewards, including youthful suboptimality,\\\n",
        " and optimal (expected) rewards. Eg: Random explore has a higher regret than exploration function. \\n \"\"\"\n",
        "chapter22_4 =  \"\"\" Approximate Q-learning: the state space is too large in reality. \\\n",
        "Need to train policy for a finite state space and apply it to the larger space. \\\n",
        "Represent Q-values by a weighted sum of deterministic features. for new states, we use learned weights to compute Q-values. \"\"\"\n",
        "chapter22_5 =  \"\"\" Policy Search: represent pi(s) by a set of paramterized Q-functions, select action of the highest Q-function value. \\\n",
        "policy search adjusts parameters to improve the policy and learn the Q-value along the way. \\n\n",
        "policy value: expected value when pi(s) is executed. policy gradient: gradient of Q-functions wrt parameters.\"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "cellView": "form",
        "outputId": "62a51d7b-70da-4a33-ae51-658e043aaab8"
      },
      "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",
        "]\n",
        "\n",
        "module9_markdown = format_module(9, \"Reinforcement Learning\", module9_sections, module9_definitions)\n",
        "display(Markdown(module9_markdown))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n N-armed bandit problem: n levers. Behind each lever is a fixed but unknown probability distribution of winnings; each pull samples from that unknown distribution. Each lever is a MDP with only one possible action, overall problem is a MDP. \n\n\n## AIMA Chapter 22.1 Learning from Rewards\n MDP is offline computing the optimal policy RL is actually doing the actions and learning from the rewards. \n\nModel Based RL: learn an empirical model of MDP (learn T and R based on tried samples of a) \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Passive RL: follow a fixed policy; find values of each s (Rewards(s) of starting from s and reach the end); \n\nDirect Eval of Values: V(s) = Avg of empirical values of s, but need to learn each state separately, slow \n\nEval using policy Eval: To use policy eval to get V(s), we can use sample based T(s,a,s') and R(s,a,s'). \n\nTD Learning: Update V(s) every time we experience a transition from s, still fixed policy. Just learning value but not updating policy.\n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Active RL: not using fixed policy, agent choose actions and learn the V(s) and optimal policy \n\nQ-learning: sample based Q-value iteration, off-policy: converge to optimal policy even if start with non-optimal, need to explore enough actions \n\nForcing Exploration: e-greedy: with prob of epsilon, act randomly. Reduce epsilon over time, or use Exploration Function: Explore areas less visited. \n\nRegret: a measure of total mistake cost: the difference between (expected) rewards, including youthful suboptimality, and optimal (expected) rewards. Eg: Random explore has a higher regret than exploration function. \n \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Approximate Q-learning: the state space is too large in reality. Need to train policy for a finite state space and apply it to the larger space. Represent Q-values by a weighted sum of deterministic features. for new states, we use learned weights to compute Q-values. \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 = \"\"\" probabilistic model: a joint distribution over a set of random variables \\\n",
        "Normalized so that sum to 1 \\n\n",
        "Marginal Distributions: From table of P(T,W), we can sum out and get tables of P(T) and P(W) \\n\n",
        "Probabilistic Language Modeling: Goal: compute probability of a sentence or sequence of words P(w1,w2,...,wn). \\\n",
        "Or probability of an upcoming word P(w3|w1,w2) \\n\n",
        "Computing Joint prob for LM: Use Chain Rule \\n\n",
        "Computing Conditional prob for LM: Use Markov Assumption: P(w3|w1,w2) ~= P(w3|w2), conditional prob only depends on prev k words. \\n\n",
        "N-gram models: assume depend on prev N words. But language has long-distance dependencies \\n\n",
        "N-gram Evaluation using Perplexity: inverse probability of the test set, normalized \\\n",
        "by the number of words. The higher the conditional probability of the word sequence, \\\n",
        "the lower the perplexity. Thus, minimizing perplexity is equivalent to maximizing the \\\n",
        "test set probability according to the language model.\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"possible worlds\" : \"the sample space, and they are mutually exclusive and exhaustive.\",\n",
        "\"P(w)\" : \"prob of possible world w, prob model should specify P(w) for all w and sum up to 1.\"\n",
        "}"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "cellView": "form",
        "outputId": "384c7de8-8aa9-438a-9fb0-225e88650e17"
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## Module 10 Lecture Notes\n probabilistic model: a joint distribution over a set of random variables Normalized so that sum to 1 \n\nMarginal Distributions: From table of P(T,W), we can sum out and get tables of P(T) and P(W) \n\nProbabilistic Language Modeling: Goal: compute probability of a sentence or sequence of words P(w1,w2,...,wn). Or probability of an upcoming word P(w3|w1,w2) \n\nComputing Joint prob for LM: Use Chain Rule \n\nComputing Conditional prob for LM: Use Markov Assumption: P(w3|w1,w2) ~= P(w3|w2), conditional prob only depends on prev k words. \n\nN-gram models: assume depend on prev N words. But language has long-distance dependencies \n\nN-gram Evaluation using Perplexity: inverse probability of the test set, normalized by the number of words. The higher the conditional probability of the word sequence, the lower the perplexity. Thus, minimizing perplexity is equivalent to maximizing the test set probability according to the language model.\n\n## Module 10 Definitions\n* **Possible worlds** - the sample space, and they are mutually exclusive and exhaustive.\n* **P(w)** - prob of possible world w, prob model should specify P(w) for all w and sum up to 1.\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 = \"\"\" Baye's Nets: a technique for describing complex joint \\\n",
        "distributions (models) using simple, local distributions (conditional probabilities) \\n\n",
        "D-separation algorithm: Query: is X independent of Y given (K1...Kn)? \\n\n",
        "  1. Check all (undirected!) paths between X and Y \\n\n",
        "  2. If one or more active triples/paths, then conditional independence not guaranteed \\n\n",
        "  3. if all paths are inactive, then conditional independence not guaranteed\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "cellView": "form",
        "outputId": "6b846cb2-66d6-48ec-f53b-b5a566b9d46e"
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## Module 11 Lecture Notes\n Baye's Nets: a technique for describing complex joint distributions (models) using simple, local distributions (conditional probabilities) \n\nD-separation algorithm: Query: is X independent of Y given (K1...Kn)? \n\n  1. Check all (undirected!) paths between X and Y \n\n  2. If one or more active triples/paths, then conditional independence not guaranteed \n\n  3. if all paths are inactive, then conditional independence not guaranteed\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 = \"\"\"Bag of Words Model: an unordered set of words with their position \\\n",
        "ignored, keeping only their frequency in the document. \\n\n",
        "naive Bayes assumption: conditional independence assumption that the probabilities P(Fi|c) are independent given \\\n",
        "the class c \\n\n",
        "Spam Email Model: rather than using all the words as individual features, \\\n",
        "predefine likely sets of words or phrases as features, combined with features that are not purely linguistic. \\n\n",
        "Perceptron: \\n\n",
        "Problems of Perceptron: 1. cannot deal with non-separable data (averaging weights can help) \\n\n",
        "  2. finds a “barely” separating solution \\n\n",
        "  3. Overtraining: test / held-out accuracy usually rises, then falls \\n\n",
        "SVM: Linear Separator, find the separator with max margin \\n\n",
        "Non-linear Separator: Transform data to higher dimension to make them separable\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "cellView": "form",
        "outputId": "bd8857ad-afd3-4701-acc7-bfe48761f798"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## Module 12 Lecture Notes\nBag of Words Model: an unordered set of words with their position ignored, keeping only their frequency in the document. \n\nnaive Bayes assumption: conditional independence assumption that the probabilities P(Fi|c) are independent given the class c \n\nSpam Email Model: rather than using all the words as individual features, predefine likely sets of words or phrases as features, combined with features that are not purely linguistic. \n\nPerceptron: \n\nProblems of Perceptron: 1. cannot deal with non-separable data (averaging weights can help) \n\n  2. finds a “barely” separating solution \n\n  3. Overtraining: test / held-out accuracy usually rises, then falls \n\nSVM: Linear Separator, find the separator with max margin \n\nNon-linear Separator: Transform data to higher dimension to make them separable\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 = \"\"\"Classification Components: \\n\n",
        "  1. A Feature representation of input\n",
        "  2. A classificaiton function \n",
        "  3. An objective function for minimizing loss for training data\n",
        "  4. optimization algorithm \\n\n",
        "Neural Nets: \\n\n",
        "  Forward pass: Use inputs to compute parameters and loss \\n\n",
        "  Backpropogation: Use chain rule to compute derivative of params wrt loss \\n\n",
        "If activation functions are linear, then neural nets is equavalent to single layer net \\n\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "cellView": "form",
        "outputId": "030304e1-d500-49cf-dc6e-e72c0222744b"
      },
      "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": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## Module 13 Lecture Notes\nClassification Components: \n\n  1. A Feature representation of input\n  2. A classificaiton function \n  3. An objective function for minimizing loss for training data\n  4. optimization algorithm \n\nNeural Nets: \n\n  Forward pass: Use inputs to compute parameters and loss \n\n  Backpropogation: Use chain rule to compute derivative of params wrt loss \n\nIf activation functions are linear, then neural nets is equavalent to single layer net \n\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 = \"\"\"Distributional word similarity theory: similar words will appear in similar contexts \\n\n",
        "Vector Space Models: \\n\n",
        "  1. Term-Document Vectors: Col = a document, Row = volcabulary, [r,c] = count of a word in a doc. \\\n",
        "  compare cols to get document similarity, compare rows to get word similarity. Compare use L2 Norm or Cosine distance. \\n\n",
        "  2. Term-term matrix: Col = Row = volcabulary, [r,c] = count of col_word appear in the context window of row_word. \\\n",
        "  Can use syntax dependency instead of spatial context window. \\n\n",
        "  3. Alternative to Word counts: TF-IDF reduce bias of frequent words like the. \\n\n",
        "Neural LM: \\n\n",
        "  1. Input: k-gram of words w[1:k], output: prob distribution over the next word. each word has an embedding vector. \\\n",
        "  training using cross-entropy loss. \\n\n",
        "  2. Dense embedding vectors, only associated with individual words. Good generalization and result perplexity. \\n\n",
        "Word Embeddings: \\n\n",
        "  1. Treat the target word and a neighboring context word as positive examples, \\\n",
        "  Randomly sample other words in the lexicon to get negative samples, logistic regression train a classifier to distinguish those two cases, \\\n",
        "  Use the weights as the embeddings. \\n\n",
        "  2. Embedding capture relations and analogies => men - uncle, women - aunt \\n\n",
        "  3. Skip-gram Task: Input: tuple(t,c)=targetword, contextword. Output: prob that c is a real context of t. \\\n",
        "  Each word as a vector, choose pos and neg samples of contexts. Train by logistic regression. Assume all context words are independent.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "59bcd023-c0cd-40ad-8d09-543bffd7f470"
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## Module 14 Lecture Notes\nDistributional word similarity theory: similar words will appear in similar contexts \n\nVector Space Models: \n\n  1. Term-Document Vectors: Col = a document, Row = volcabulary, [r,c] = count of a word in a doc.   compare cols to get document similarity, compare rows to get word similarity. Compare use L2 Norm or Cosine distance. \n\n  2. Term-term matrix: Col = Row = volcabulary, [r,c] = count of col_word appear in the context window of row_word.   Can use syntax dependency instead of spatial context window. \n\n  3. Alternative to Word counts: TF-IDF reduce bias of frequent words like the. \n\nNeural LM: \n\n  1. Input: k-gram of words w[1:k], output: prob distribution over the next word. each word has an embedding vector.   training using cross-entropy loss. \n\n  2. Dense embedding vectors, only associated with individual words. Good generalization and result perplexity. \n\nWord Embeddings: \n\n  1. Treat the target word and a neighboring context word as positive examples,   Randomly sample other words in the lexicon to get negative samples, logistic regression train a classifier to distinguish those two cases,   Use the weights as the embeddings. \n\n  2. Embedding capture relations and analogies => men - uncle, women - aunt \n\n  3. Skip-gram Task: Input: tuple(t,c)=targetword, contextword. Output: prob that c is a real context of t.   Each word as a vector, choose pos and neg samples of contexts. Train by logistic regression. Assume all context words are independent.\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": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "b6ab268d-13d8-43c4-fcae-c721b89da1de"
      },
      "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": 22,
      "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 Utility and Preference Theory: \n\n\n $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ $EU(a) = \\sum_s' P(\\textnormal{RESULT}(a)=s')U(s')$\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nORDERABILITY: Given any two lotteries, a rational agent must either prefer one or else rate them as equally preferable. \n\nTRANSITIVITY: Given any three lotteries, if an agent prefers A to B and prefers B to C, then the agent must prefer A to C. \n\nCONTINUITY: If A>B>C, then there is some probability p for which the rational agent will be indifferent between getting B  for sure and the lottery that yields A with probability p and C with probability 1−p. \n\nMONOTONICITY: Suppose two lotteries have the same two possible outcomes, A and B.   If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). \n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Markovian Transition Model : the probability of reaching s′ from s depends only on s and not on the history of earlier states. \n\nUtility Function : Addition of Rewards of the environment history R(s,a,s') \n\nDefinition of MDP : a sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards \n\nSolution to MDP is a Policy pi(s), optimal policy Maximizes EU of s and is stationary (does not change with time). \n\nUtility = sum of discounted rewards \n\nAvoid Infinite Utilities: 1. Finite horizon, Terminate episodes after a fixed T steps, this will cause non-stationary policies \n\n2. Discounting: use 0 < gamma < 1 and utility will be bounded. 3. Absorbing state: guarantee that for every policy, a terminal state will eventually be reached. \n\n\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Convergence of Value Iteration: contraction is a function of one argument that, when applied to two different inputs in turn, produces two output values that are “closer together,” by at least some constant factor, than the original inputs. \n\n Value Iteration: Init V(s) = 0; Do update V(s) for each s using bellman equations; Repeat until all V(s) converges.  Complexity : O(S^2A), will converge to unique optimal values, policy may converge much earlier. \n\n Problems of Value iter: too slow, max action rarely changes, policy can converge much earlier. \n\n Policy Iteration: 1.  Policy evaluation: calculate utilities for some fixed policy (not\n  optimal utilities!) until convergence, 2.  Policy improvement: update policy using one-step   lookahead with resulting converged (but not optimal!) utilities as future values, 3. repeat until policy converges. \n\n \n\n## Module 8 Definitions\n* **Utilities** - functions from outcomes (states of the world) to real numbers that describe an agent’s preferences\n* **Meu** - a rational agent should choose the action that maximizes the agent’s expected utility\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n N-armed bandit problem: n levers. Behind each lever is a fixed but unknown probability distribution of winnings; each pull samples from that unknown distribution. Each lever is a MDP with only one possible action, overall problem is a MDP. \n\n\n## AIMA Chapter 22.1 Learning from Rewards\n MDP is offline computing the optimal policy RL is actually doing the actions and learning from the rewards. \n\nModel Based RL: learn an empirical model of MDP (learn T and R based on tried samples of a) \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Passive RL: follow a fixed policy; find values of each s (Rewards(s) of starting from s and reach the end); \n\nDirect Eval of Values: V(s) = Avg of empirical values of s, but need to learn each state separately, slow \n\nEval using policy Eval: To use policy eval to get V(s), we can use sample based T(s,a,s') and R(s,a,s'). \n\nTD Learning: Update V(s) every time we experience a transition from s, still fixed policy. Just learning value but not updating policy.\n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Active RL: not using fixed policy, agent choose actions and learn the V(s) and optimal policy \n\nQ-learning: sample based Q-value iteration, off-policy: converge to optimal policy even if start with non-optimal, need to explore enough actions \n\nForcing Exploration: e-greedy: with prob of epsilon, act randomly. Reduce epsilon over time, or use Exploration Function: Explore areas less visited. \n\nRegret: a measure of total mistake cost: the difference between (expected) rewards, including youthful suboptimality, and optimal (expected) rewards. Eg: Random explore has a higher regret than exploration function. \n \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Approximate Q-learning: the state space is too large in reality. Need to train policy for a finite state space and apply it to the larger space. Represent Q-values by a weighted sum of deterministic features. for new states, we use learned weights to compute Q-values. \n\n## Module 9 Definitions\n* **Term** - your definition here\n\n\n# Module 10: Probabilies and Language Models\n## Module 10 Lecture Notes\n probabilistic model: a joint distribution over a set of random variables Normalized so that sum to 1 \n\nMarginal Distributions: From table of P(T,W), we can sum out and get tables of P(T) and P(W) \n\nProbabilistic Language Modeling: Goal: compute probability of a sentence or sequence of words P(w1,w2,...,wn). Or probability of an upcoming word P(w3|w1,w2) \n\nComputing Joint prob for LM: Use Chain Rule \n\nComputing Conditional prob for LM: Use Markov Assumption: P(w3|w1,w2) ~= P(w3|w2), conditional prob only depends on prev k words. \n\nN-gram models: assume depend on prev N words. But language has long-distance dependencies \n\nN-gram Evaluation using Perplexity: inverse probability of the test set, normalized by the number of words. The higher the conditional probability of the word sequence, the lower the perplexity. Thus, minimizing perplexity is equivalent to maximizing the test set probability according to the language model.\n\n## Module 10 Definitions\n* **Possible worlds** - the sample space, and they are mutually exclusive and exhaustive.\n* **P(w)** - prob of possible world w, prob model should specify P(w) for all w and sum up to 1.\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## Module 11 Lecture Notes\n Baye's Nets: a technique for describing complex joint distributions (models) using simple, local distributions (conditional probabilities) \n\nD-separation algorithm: Query: is X independent of Y given (K1...Kn)? \n\n  1. Check all (undirected!) paths between X and Y \n\n  2. If one or more active triples/paths, then conditional independence not guaranteed \n\n  3. if all paths are inactive, then conditional independence not guaranteed\n\n## Module 11 Definitions\n* **Concept** - definition\n\n\n# Module 12: Naive Bayes and Perceptrons\n## Module 12 Lecture Notes\nBag of Words Model: an unordered set of words with their position ignored, keeping only their frequency in the document. \n\nnaive Bayes assumption: conditional independence assumption that the probabilities P(Fi|c) are independent given the class c \n\nSpam Email Model: rather than using all the words as individual features, predefine likely sets of words or phrases as features, combined with features that are not purely linguistic. \n\nPerceptron: \n\nProblems of Perceptron: 1. cannot deal with non-separable data (averaging weights can help) \n\n  2. finds a “barely” separating solution \n\n  3. Overtraining: test / held-out accuracy usually rises, then falls \n\nSVM: Linear Separator, find the separator with max margin \n\nNon-linear Separator: Transform data to higher dimension to make them separable\n\n\n## Module 12 Definitions\n* **Concept** - definition\n\n\n# Module 13: Neural Networks\n## Module 13 Lecture Notes\nClassification Components: \n\n  1. A Feature representation of input\n  2. A classificaiton function \n  3. An objective function for minimizing loss for training data\n  4. optimization algorithm \n\nNeural Nets: \n\n  Forward pass: Use inputs to compute parameters and loss \n\n  Backpropogation: Use chain rule to compute derivative of params wrt loss \n\nIf activation functions are linear, then neural nets is equavalent to single layer net \n\n\n\n## Module 13 Definitions\n* **Concept** - definition\n\n\n# Module 14: Natural Language Processing\n## Module 14 Lecture Notes\nDistributional word similarity theory: similar words will appear in similar contexts \n\nVector Space Models: \n\n  1. Term-Document Vectors: Col = a document, Row = volcabulary, [r,c] = count of a word in a doc.   compare cols to get document similarity, compare rows to get word similarity. Compare use L2 Norm or Cosine distance. \n\n  2. Term-term matrix: Col = Row = volcabulary, [r,c] = count of col_word appear in the context window of row_word.   Can use syntax dependency instead of spatial context window. \n\n  3. Alternative to Word counts: TF-IDF reduce bias of frequent words like the. \n\nNeural LM: \n\n  1. Input: k-gram of words w[1:k], output: prob distribution over the next word. each word has an embedding vector.   training using cross-entropy loss. \n\n  2. Dense embedding vectors, only associated with individual words. Good generalization and result perplexity. \n\nWord Embeddings: \n\n  1. Treat the target word and a neighboring context word as positive examples,   Randomly sample other words in the lexicon to get negative samples, logistic regression train a classifier to distinguish those two cases,   Use the weights as the embeddings. \n\n  2. Embedding capture relations and analogies => men - uncle, women - aunt \n\n  3. Skip-gram Task: Input: tuple(t,c)=targetword, contextword. Output: prob that c is a real context of t.   Each word as a vector, choose pos and neg samples of contexts. Train by logistic regression. Assume all context words are independent.\n\n## Module 14 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
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}