{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "(Reeda) 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": "68c48aae-c39d-4f9f-8b9c-c57a431fb1cd"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26f24a63-ed28-47b1-8776-cac81a113b3b"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 16, “Making Simple Decisions” (16.1-16.3)\n",
        "\n",
        "sample_equation = r\"\"\" $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" Chapter 16.1 summary. \"\"\"  + 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 = \"\"\"\\nIn markov decision processes the prob of ending up in a state is p(s'|s,a) where s' is the next state and s is current state and a is action\\n\n",
        "\\nMarkovian is the prob of ending in s' depending only on s \n",
        "and not previous states.\\n\\nIn MDPs we get a reward at each state which is R(s, s', a), and the rewards are additiive, can be positive or negative. The total reward added is called the environmental history, and sum of rewards at each step is total utility\\n\n",
        "\\nMDPs are sequential, fully observable, stochastic, additive rewards. They have a set of states S, set of actions A, transition function T(s, a, s') where prob of each state is s'P(s'|s,a), as well as initial and temrinal states\n",
        "\\n\\n the Solution to an MDP is a policy and a policy maps from a state onto the action to take if the agent is in the state. \\n\\n In MDPs we want to find the optimal policy\\n\\n\n",
        "So we want to maximize the expected utility and maximize that for the possible paths generated by a policy\n",
        "\\n\\n The expected utility is shaped by the reward function. \\n\\n The performance of an agent is the sum of rewards for the states we choose.\n",
        "\\n\\n We are going to penalize rewards in the future by discounting it by gamma, this allows us to solve the problem if we prefer rewards sooner or later\n",
        "\\n\\n So for discounting, first state we get all reward, second state we get gamma times reward and third we get gamma squared times reward \n",
        "\\n\\n If we have stationary preferences and prefer a to b meaning [a1, a2, ..] > (curly brace) [b1, b2, ...] then if we add a new element the same relationship also \n",
        "holds so. Therefore, there are only two ways to define utility: \\n \\n Additive utility: U([r0, r1, r2, ...]) = r0 + r1 + r2 + ...\\n\\n\n",
        "Discounted Utility: U([r0, r1, r2, ...]) = r0 + (gamma)r1 + (gamma)^2 r2 + ...\\n\\n\n",
        "To prevent infinite loops where we bounce around and get infinite rewards we can set a FINITE HORIZON, similar to depth limited search \\n Another \n",
        "solution is doing discounting so sum(gamma^t * rt) <= R_max / (1-gamma) \\n\\n\n",
        "Each MDP acts like an expectimax-like search tree. \\n\\n \n",
        "The expected utility starting in s and acting optimally is V*(s) \\n\\n \n",
        "The value of a q state is Q*(s,a) which is the expected utility starting out having taken action a from state s and acting optimally. \\n\\n\n",
        "The optimal policy is pi*(s) = optimal action from state s. \\n\\n\n",
        "We can compute the expectimax value by doing V*(s) = maxQ*(s, a) which says choose the best sequence of states recrusvively, and Q*(s, a) = sum(T(s, a, s') * (R(s,a,s') + gamma * V*(s')))\n",
        "where T is the weight at each edge or the probability. \\n\\n\n",
        "The issue with this definition is the tree goes on forever and get very big\\n\\n\n",
        "Value Iteration: start with V0(s) and no time steps left means an expected reward sum of zero, where we keep track of the time by number of states\\n\\n\n",
        "In value iteration, repeat the recursion step until convergence and complexity is O(S^2A). \\n\\n \n",
        "For value iteration, start at the lowest state and go up. \\n\\n If the discount is less than one than it will conveerge. \\n\\n\n",
        "For utilities of fixed policies we have the same equation as above except instead of a max now we have the policy function giving us which one to take. \\n\\n\n",
        "Policy Evaluation: If we start at the bottom and build up recursively we will have O(S^2) iterations \\n\\n \n",
        "For policy extraction where we are finding policy from values, we need to find the argmax of the previous equation \\n\\n \n",
        "If we have optimal q values it is very easy to decide what to do. \\n\\n \n",
        "Actions are easier to select from q values than values. \\n\\n \n",
        "For policy iteration (to find optimal values): \\n \n",
        "Step 1: \\n \n",
        "Policy Evaluation:\n",
        "Calculate utilites for some fixed policy, not optimal, until convergence. \\n\n",
        "Step 2: \\n \n",
        "Policy Improvement: \n",
        "Update policy using one step look ahead where utilities are future values. \\n\\n\n",
        "Repeat these steps until we see convergence \\n\\n\n",
        "POLICY ITERATION IS OPTIMAL ALGORITHM - dynamic programming\\n\\n\n",
        "Maximum Expected Utility: \\n\\n\n",
        "A rational agent should choose action that maximizes its expected utility \\n\\n \n",
        "For worst case minimax algorithm we can apply transformations and get same result. - insensitivity to monotonic transformations\\n\\n\n",
        "For average case expectimax reasoning, we need the magnitudes to be meaningful. \\n\\n \n",
        "We have prizes A, B for preferences and we also have lotteries L = [p, A, (1-p), B] where A has a prob of p and B has a prob of (1-p). \\n\\n\n",
        "A > B means A prefered to be while A ~ B means indifferent.\\n\\n \n",
        "Axiom of Transitivity: \\n\\n\n",
        "(A > B) and (B > C) -> (A > C) \\n\\n\n",
        "Orderability: \\n\\n\n",
        "(A > B) or (B > A) -> (A ~ B) \\n\\n\n",
        "Continuity: \\n\\n\n",
        " A > B > C -> Ep[p, A, 1-p, C] ~ B (meaning there exists a value in between A and C such that we are indifferent to B)\\n\\n\n",
        "Substitutability: \\n\\n\n",
        " A ~ B -> [p, A, 1-p, C] ~ [p, B, 1-p, C]\n",
        "Monotonicity: \\n\\n\n",
        " A > B -> (p >= q <-> [p, A, 1-p, B] >= [q, A, 1-q, B]) (meaning that if we prefer A to B and p >= q then we always want more p to q) \\n\\n\n",
        " If you follow all these axioms then you are a rational agent \\n\\n\n",
        " If you choose actions that maximize expected utility then we are following the MEU principle \\n\\n\n",
        " \n",
        "\"\"\"\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",
        "\"utility function\" : \"Your definition here\",\n",
        "\"expected utility\" : \"your definition here\",\n",
        "\"etc.\": \"your definition here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elCCgY64spuq"
      },
      "source": [
        "## Preview of your notes for Module 8\n",
        "\n",
        "Here's a preview of your how your notes will be displayed.  At the end of this Python notebook, we'll aggregate your notes for all modules together into a single display.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYeNcdM_maE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b8970770-cf72-469a-b591-f4577e47bfb7"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "from IPython.display import display, Markdown, Latex\n",
        "\n",
        "module8_sections = [\n",
        "  (\"AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\", chapter16_1),\n",
        "  (\"AIMA Chapter 16.2 The Basis of Utility Theory\", chapter16_2),\n",
        "  (\"AIMA Chapter 16.3 Utility Functions\", chapter16_3),\n",
        "  (\"AIMA Chapter 17.1 Sequential Decision Problems\", chapter17_1),\n",
        "  (\"AIMA Chapter 17.2 Algorithms for MDPs\", chapter17_2),\n",
        "  (\"Module 8 Lecture Notes\", module8_lecture_notes),\n",
        "]\n",
        "\n",
        "\n",
        "def length_check(sections):\n",
        "  warnings = \"\"\n",
        "  for section, summary in sections:\n",
        "    max_word_length = 500\n",
        "    max_character_length = 3000\n",
        "    if \"lecture\" in section.lower():\n",
        "      # It's OK to do longer summaries of the lectures\n",
        "      max_word_length = 2000\n",
        "      max_character_length = 12000  \n",
        "    if (len(summary) > max_character_length or len(summary.split(' ')) > max_word_length):\n",
        "      warnings += \"WARNING: Your summary of '{section}' is too long!\\n\".format(section=section)\n",
        "  return warnings\n",
        "\n",
        "def length_check_definitions(definitions):\n",
        "  warnings = \"\"\n",
        "  for concept, definition in definitions.items():\n",
        "    if(len(definition.split(' ')) > 25):\n",
        "      warnings += \"WARNING: Your definition of '{concept}' is too long!\\n\".format(concept=concept)\n",
        "  return warnings\n",
        "\n",
        "\n",
        "def format_module(module_num, title, sections, definitions, show_placeholder=False):\n",
        "  markdown = \"\"\n",
        "\n",
        "  # Check summary lengths\n",
        "  warnings = length_check(sections)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  # Check definition lengths\n",
        "  warnings = length_check_definitions(definitions)\n",
        "  if warnings != \"\":\n",
        "    markdown += \"## \" + warnings\n",
        "\n",
        "  markdown += \"# Module {num}: {title}\\n\".format(num=module_num, title=title)\n",
        "\n",
        "\n",
        "  for section, summary in sections:\n",
        "    if show_placeholder and summary == \" Your summary here \":\n",
        "      summary = placeholder\n",
        "    markdown += \"## {section}\\n{summary}\\n\\n\".format(section=section, summary=summary)\n",
        "  \n",
        "  markdown += \"## Module {num} Definitions\\n\".format(num=module_num)\n",
        "  for concept, definition in definitions.items():\n",
        "    markdown += \"* **{concept}** - {definition}\\n\".format(concept=concept.capitalize(), definition=definition)\n",
        "  return markdown\n",
        "\n",
        "\n",
        "module8_markdown = format_module(8, \"Markov Decision Processes\", module8_sections, module8_definitions)\n",
        "display(Markdown(module8_markdown))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \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\nIn markov decision processes the prob of ending up in a state is p(s'|s,a) where s' is the next state and s is current state and a is action\n\n\nMarkovian is the prob of ending in s' depending only on s \nand not previous states.\n\nIn MDPs we get a reward at each state which is R(s, s', a), and the rewards are additiive, can be positive or negative. The total reward added is called the environmental history, and sum of rewards at each step is total utility\n\n\nMDPs are sequential, fully observable, stochastic, additive rewards. They have a set of states S, set of actions A, transition function T(s, a, s') where prob of each state is s'P(s'|s,a), as well as initial and temrinal states\n\n\n the Solution to an MDP is a policy and a policy maps from a state onto the action to take if the agent is in the state. \n\n In MDPs we want to find the optimal policy\n\n\nSo we want to maximize the expected utility and maximize that for the possible paths generated by a policy\n\n\n The expected utility is shaped by the reward function. \n\n The performance of an agent is the sum of rewards for the states we choose.\n\n\n We are going to penalize rewards in the future by discounting it by gamma, this allows us to solve the problem if we prefer rewards sooner or later\n\n\n So for discounting, first state we get all reward, second state we get gamma times reward and third we get gamma squared times reward \n\n\n If we have stationary preferences and prefer a to b meaning [a1, a2, ..] > (curly brace) [b1, b2, ...] then if we add a new element the same relationship also \nholds so. Therefore, there are only two ways to define utility: \n \n Additive utility: U([r0, r1, r2, ...]) = r0 + r1 + r2 + ...\n\n\nDiscounted Utility: U([r0, r1, r2, ...]) = r0 + (gamma)r1 + (gamma)^2 r2 + ...\n\n\nTo prevent infinite loops where we bounce around and get infinite rewards we can set a FINITE HORIZON, similar to depth limited search \n Another \nsolution is doing discounting so sum(gamma^t * rt) <= R_max / (1-gamma) \n\n\nEach MDP acts like an expectimax-like search tree. \n\n \nThe expected utility starting in s and acting optimally is V*(s) \n\n \nThe value of a q state is Q*(s,a) which is the expected utility starting out having taken action a from state s and acting optimally. \n\n\nThe optimal policy is pi*(s) = optimal action from state s. \n\n\nWe can compute the expectimax value by doing V*(s) = maxQ*(s, a) which says choose the best sequence of states recrusvively, and Q*(s, a) = sum(T(s, a, s') * (R(s,a,s') + gamma * V*(s')))\nwhere T is the weight at each edge or the probability. \n\n\nThe issue with this definition is the tree goes on forever and get very big\n\n\nValue Iteration: start with V0(s) and no time steps left means an expected reward sum of zero, where we keep track of the time by number of states\n\n\nIn value iteration, repeat the recursion step until convergence and complexity is O(S^2A). \n\n \nFor value iteration, start at the lowest state and go up. \n\n If the discount is less than one than it will conveerge. \n\n\nFor utilities of fixed policies we have the same equation as above except instead of a max now we have the policy function giving us which one to take. \n\n\nPolicy Evaluation: If we start at the bottom and build up recursively we will have O(S^2) iterations \n\n \nFor policy extraction where we are finding policy from values, we need to find the argmax of the previous equation \n\n \nIf we have optimal q values it is very easy to decide what to do. \n\n \nActions are easier to select from q values than values. \n\n \nFor policy iteration (to find optimal values): \n \nStep 1: \n \nPolicy Evaluation:\nCalculate utilites for some fixed policy, not optimal, until convergence. \n\nStep 2: \n \nPolicy Improvement: \nUpdate policy using one step look ahead where utilities are future values. \n\n\nRepeat these steps until we see convergence \n\n\nPOLICY ITERATION IS OPTIMAL ALGORITHM - dynamic programming\n\n\nMaximum Expected Utility: \n\n\nA rational agent should choose action that maximizes its expected utility \n\n \nFor worst case minimax algorithm we can apply transformations and get same result. - insensitivity to monotonic transformations\n\n\nFor average case expectimax reasoning, we need the magnitudes to be meaningful. \n\n \nWe have prizes A, B for preferences and we also have lotteries L = [p, A, (1-p), B] where A has a prob of p and B has a prob of (1-p). \n\n\nA > B means A prefered to be while A ~ B means indifferent.\n\n \nAxiom of Transitivity: \n\n\n(A > B) and (B > C) -> (A > C) \n\n\nOrderability: \n\n\n(A > B) or (B > A) -> (A ~ B) \n\n\nContinuity: \n\n\n A > B > C -> Ep[p, A, 1-p, C] ~ B (meaning there exists a value in between A and C such that we are indifferent to B)\n\n\nSubstitutability: \n\n\n A ~ B -> [p, A, 1-p, C] ~ [p, B, 1-p, C]\nMonotonicity: \n\n\n A > B -> (p >= q <-> [p, A, 1-p, B] >= [q, A, 1-q, B]) (meaning that if we prefer A to B and p >= q then we always want more p to q) \n\n\n If you follow all these axioms then you are a rational agent \n\n\n If you choose actions that maximize expected utility then we are following the MEU principle \n\n\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 = \"\"\" \\n\\n Reinforcement learning is when you dont know probabilities, but based on MDP. You start trying things out and adjust your\n",
        "model accordignly \\n\\n In reinforcement learning you \\n\\n\n",
        "It is still modeled as a MDP but we do not know T or R. \\n\\n \n",
        "In model based learning we have to find estimates of T and R, by training the model using chosen policies. -> giving us a way to estimate the reward and weights on graph. \\n\\n\n",
        "In model free, we pick an action and compare result for what values of states to what we had and adjust model accordingly. \\n\\n \n",
        "In passive learning, we are given a policy and we execute the policy and see what happens. We then average sum of rewards to get the value of being in a state. \\n\\n\n",
        "In direct evaluation, we run the policy and get values of each node by checking sum of weights till end starting from each node \\n\\n\n",
        "In sample based policy evaluation, we take sample of outcomes of following a policy and check what reward we got and what state we ended up in, then\n",
        "we take more samples, and average all those together to get good estimate of value of being at S. -> Issue is no guarantee well be in S n times. \\n\\n\n",
        "Q Value Iteration: \\n\\n\n",
        "In Q value iteration the max is in the inside in the equation for MDP instead of outside. In Q Learning, Q(s, a) = (1-a)Q(s, a) + a(sample) where we keep updating \n",
        "the Q values in a running average. \\n\\n\n",
        "We will converge towards optimal policy in Q learning EVEN when we are acting sub optimally -> off policy learning. \\n\\n\n",
        "To force exploration we can use an epsilon greedy approach, where with probability epsilon we explore (act randomly) and with probability 1-e we act on the \n",
        "current policy \\n\\n\n",
        "You can make epsilon smaller and smaller as we move through. \n",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"Exploration\" : \"trying unknown actions to get information\",\n",
        "\"Exploitation\" : \"eventuallt you have to use what you know\",\n",
        "\"Regret\" : \"even if you learn intelligently you will make mistakes\",\n",
        "\"Sampling\" : \"because of chance you have to try things repeatdely\",\n",
        "\"difficulty\" : \"learning can be much harder than solving a known MDP\",\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "cellView": "form",
        "outputId": "fa9feaef-88f8-4ba9-fbeb-cb014fe284ae"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "\n",
        "module9_sections = [\n",
        "  (\"AIMA Chapter 17.3 Bandit Problems\", chapter17_3),\n",
        "  (\"AIMA Chapter 22.1 Learning from Rewards\", chapter22_1),\n",
        "  (\"AIMA Chapter 22.2 Passive Reinforcement Learning\", chapter22_2),\n",
        "  (\"AIMA Chapter 22.3 Active Reinforcement Learning\", chapter22_3),\n",
        "  (\"AIMA Chapter 22.4 Generalization in Reinforcement Learning\", chapter22_4),\n",
        "  (\"Module 9 Lecture Notes\", module9_lecture_notes),\n",
        "]\n",
        "\n",
        "module9_markdown = format_module(9, \"Reinforcement Learning\", module9_sections, module9_definitions)\n",
        "display(Markdown(module9_markdown))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Your summary here \n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Your summary here \n\n## Module 9 Lecture Notes\n \n\n Reinforcement learning is when you dont know probabilities, but based on MDP. You start trying things out and adjust your\nmodel accordignly \n\n In reinforcement learning you \n\n\nIt is still modeled as a MDP but we do not know T or R. \n\n \nIn model based learning we have to find estimates of T and R, by training the model using chosen policies. -> giving us a way to estimate the reward and weights on graph. \n\n\nIn model free, we pick an action and compare result for what values of states to what we had and adjust model accordingly. \n\n \nIn passive learning, we are given a policy and we execute the policy and see what happens. We then average sum of rewards to get the value of being in a state. \n\n\nIn direct evaluation, we run the policy and get values of each node by checking sum of weights till end starting from each node \n\n\nIn sample based policy evaluation, we take sample of outcomes of following a policy and check what reward we got and what state we ended up in, then\nwe take more samples, and average all those together to get good estimate of value of being at S. -> Issue is no guarantee well be in S n times. \n\n\nQ Value Iteration: \n\n\nIn Q value iteration the max is in the inside in the equation for MDP instead of outside. In Q Learning, Q(s, a) = (1-a)Q(s, a) + a(sample) where we keep updating \nthe Q values in a running average. \n\n\nWe will converge towards optimal policy in Q learning EVEN when we are acting sub optimally -> off policy learning. \n\n\nTo force exploration we can use an epsilon greedy approach, where with probability epsilon we explore (act randomly) and with probability 1-e we act on the \ncurrent policy \n\n\nYou can make epsilon smaller and smaller as we move through. \n \n\n## Module 9 Definitions\n* **Exploration** - trying unknown actions to get information\n* **Exploitation** - eventuallt you have to use what you know\n* **Regret** - even if you learn intelligently you will make mistakes\n* **Sampling** - because of chance you have to try things repeatdely\n* **Difficulty** - learning can be much harder than solving a known MDP\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 = \"\"\" p(a|b) = p(a intersect b) / p(b). \\n\\n\n",
        "Bayes Rule: \\n\\n\n",
        "P(x|y) = P(x) * P(y/x) / P(y)\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "a63cef8b-9168-4686-eb95-be43113a0a5e"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module10_sections = [\n",
        "  (\"AIMA Chapter 12.1 Acting under Uncertainty\", chapter12_1),\n",
        "  (\"AIMA Chapter 12.2 Basic Probability Notation\", chapter12_2),\n",
        "  (\"AIMA Chapter 12.3 Inference Using Full Joint Distributions\", chapter12_3),\n",
        "  (\"AIMA Chapter 12.4 Independence\", chapter12_4),\n",
        "  (\"AIMA Chapter 12.5 Bayes’ Rule and Its Use\", chapter12_5),\n",
        "  (\"AIMA Chapter 12.6 Naive Bayes Models\", chapter12_6),\n",
        "  (\"AIMA Chapter 12.7 The Wumpus World Revisited\", chapter12_7),\n",
        "  (\"SLP Chapter 3.1 N-Grams\", SLP_chapter3_1),\n",
        "  (\"SLP Chapter 3.2 Evaluating Language Models\", SLP_chapter3_2),\n",
        "  (\"SLP Chapter 3.3 Sampling sentences from a language model\", SLP_chapter3_3),\n",
        "  (\"SLP Chapter 3.4 Generalization and Zeros\", SLP_chapter3_4),\n",
        "  (\"SLP Chapter 3.5 Smoothing\", SLP_chapter3_5),\n",
        "  (\"SLP Chapter 3.7 Huge Language Models and Stupid Backoff\", SLP_chapter3_7),\n",
        "  (\"Module 10 Lecture Notes\", module10_lecture_notes),\n",
        "]\n",
        "\n",
        "module10_markdown = format_module(10, \"Probabilies and Language Models\", module10_sections, module10_definitions)\n",
        "display(Markdown(module10_markdown))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n p(a|b) = p(a intersect b) / p(b). \n\n\nBayes Rule: \n\n\nP(x|y) = P(x) * P(y/x) / P(y)\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 = \"\"\" =| is independence \\n\\n\n",
        "P(a and y | z) = P(x|z)P(y|z) \\n\\n\n",
        "P(x|z and y) = P(x|z) \\n\\n\n",
        "P(+y| +x) = 1, P(-y| -x) = 1, P(+z|+y) = 1, P(-z|-y) = 1 \\n\\n\n",
        "To check if conditionally indepdent To see if X is indepdnet of Z given Y we start with P(z and x|y) and work from there using \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "cellView": "form",
        "outputId": "8c5ddf3d-8b0d-4a92-eeaa-ef80d13f0108"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module11_sections = [\n",
        "  (\"AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\", chapter13_1),\n",
        "  (\"AIMA Chapter 13.2 The Semantics of Bayesian Networks\", chapter13_2),\n",
        "  (\"AIMA 13.3 Exact Inference in Bayesian Networks\", chapter13_3),\n",
        "  (\"Module 11 Lecture Notes\", module11_lecture_notes),\n",
        "]\n",
        "\n",
        "module11_markdown = format_module(11, \"Probabilistic Reasoning and Bayes’ Nets\", \n",
        "                         module11_sections, \n",
        "                         module11_definitions)\n",
        "display(Markdown(module11_markdown))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n =| is independence \n\n\nP(a and y | z) = P(x|z)P(y|z) \n\n\nP(x|z and y) = P(x|z) \n\n\nP(+y| +x) = 1, P(-y| -x) = 1, P(+z|+y) = 1, P(-z|-y) = 1 \n\n\nTo check if conditionally indepdent To see if X is indepdnet of Z given Y we start with P(z and x|y) and work from there using \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 = \"\"\" Naive bayes is where features are all independent of each other. \\n\\n\n",
        "So naive bayes says what is probability we get something given this set of features. All probability parameters are denoted by theta \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "02357d7f-5c0f-46b9-f644-9d30ad1be2f3"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module12_sections = [\n",
        "  (\"AIMA Chapter 5.1 Game Theory\", chapter19_1),\n",
        "  (\"AIMA Chapter 5.2 Optimal Decisions in Games\", chapter19_2),\n",
        "  (\"AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\", chapter19_3),\n",
        "  (\"AIMA Chapter 5.5 Stochastic Games\", chapter19_4),\n",
        "  (\"AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\", chapter19_5),\n",
        "  (\"AIMA Chapter 16.2 The Basis of Utility Theory\", chapter19_6),\n",
        "  (\"SLP Chapter 4.1 Naive Bayes Classifiers\", slp_chapter4_1),\n",
        "  (\"SLP Chapter 4.2 Training the Naive Bayes Classifier\", slp_chapter4_2),\n",
        "  (\"SLP Chapter 4.3 Worked Example\", slp_chapter4_3),\n",
        "  (\"SLP Chapter 4.4 Optimizing for Sentiment Analysis\", slp_chapter4_4),\n",
        "  (\"SLP Chapter 4.5 Naive Bayes for other text classification tasks\", slp_chapter4_5),\n",
        "  (\"SLP Chapter 4.6 Naive Bayes as a Language Model\", slp_chapter4_6),\n",
        "  (\"SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\", slp_chapter4_7),\n",
        "  (\"SLP Chapter 4.8 Test sets and Cross-validation\", slp_chapter4_8),\n",
        "  (\"SLP Chapter 4.9 Statistical Significance Testing\", slp_chapter4_9),\n",
        "  (\"SLP Chapter 4.10 Avoiding Harms in Classification\", slp_chapter4_10),\n",
        "  (\"Module 12 Lecture Notes\", module12_lecture_notes),\n",
        "]\n",
        "\n",
        "module12_markdown = format_module(12, \"Naive Bayes and Perceptrons\", \n",
        "                         module12_sections, \n",
        "                         module12_definitions)\n",
        "display(Markdown(module12_markdown))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n Naive bayes is where features are all independent of each other. \n\n\nSo naive bayes says what is probability we get something given this set of features. All probability parameters are denoted by theta \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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "9672e249-2f9e-417c-c913-23afadaf7382"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module13_sections = [\n",
        "  (\"SLP Chapter 5.1 Classification: the sigmoid\", slp_chapter5_1),\n",
        "  (\"SLP Chapter 5.2 Learning in Logistic Regression\", slp_chapter5_2),\n",
        "  (\"SLP Chapter 5.3 The cross-entropy loss function\", slp_chapter5_3),\n",
        "  (\"SLP Chapter 5.4 Gradient Descent\", slp_chapter5_4),\n",
        "  (\"SLP Chapter 5.5 Regularization\", slp_chapter5_5),\n",
        "  (\"SLP Chapter 5.6 Multinomial logistic regression\", slp_chapter5_6),\n",
        "\n",
        "  (\"SLP Chapter 7.1 Units\", slp_chapter7_1),\n",
        "  (\"SLP Chapter 7.2 The XOR problem\", slp_chapter7_2),\n",
        "  (\"SLP Chapter 7.3 Feedforward Neural Networks\", slp_chapter7_3),\n",
        "  (\"SLP Chapter 7.4 Feedforward networks for NLP: Classification\", slp_chapter7_4),\n",
        "  (\"SLP Chapter 7.5 Feedforward Neural Language Modeling\", slp_chapter7_5),\n",
        "  (\"SLP Chapter 7.6 Training Neural Nets\", slp_chapter7_6),\n",
        "  (\"SLP Chapter 7.7 Training the neural language model\", slp_chapter7_7),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module13_markdown = format_module(13, \"Neural Networks\", \n",
        "                         module13_sections, \n",
        "                         module13_definitions)\n",
        "display(Markdown(module13_markdown))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n Your summary here \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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "df27b239-6610-4b95-ffe0-698bf413e94c"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module14_sections = [\n",
        "  (\"SLP Chapter 9.1 Language Models Revisited\", slp_chapter9_1),\n",
        "  (\"SLP Chapter 9.2 Recurrent Neural Networks\", slp_chapter9_2),\n",
        "  (\"SLP Chapter 9.3 RNNs as Language Models\", slp_chapter9_3),\n",
        "\n",
        "  (\"SLP Chapter 6.1 Lexical Semantics\", slp_chapter6_1),\n",
        "  (\"SLP Chapter 6.2 Vector Semantics\", slp_chapter6_2),\n",
        "  (\"SLP Chapter 6.3 Words and Vectors\", slp_chapter6_3),\n",
        "  (\"SLP Chapter 6.4 Cosine for measuring similarity\", slp_chapter6_4),\n",
        "  (\"SLP Chapter 6.8 Word2vec\", slp_chapter6_8),\n",
        "  (\"SLP Chapter 6.10 Semantic properties of embeddings\", slp_chapter6_10),\n",
        "  (\"SLP Chapter 6.11 Bias and Embeddings\", slp_chapter6_11),\n",
        "  (\"SLP Chapter 6.12 Evaluating Vector Models\", slp_chapter6_12),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module14_markdown = format_module(14, \"Natural Language Processing\", \n",
        "                         module14_sections, \n",
        "                         module14_definitions)\n",
        "display(Markdown(module14_markdown))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n Your summary here \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": "17b7795c-482a-4fe6-e40d-9d60f83642a0"
      },
      "source": [
        "# This will generate the full set of notes that you can use for your exam.\n",
        "full_markdown = \"\\n\\n\".join([module8_markdown, \n",
        "                             module9_markdown, \n",
        "                             module10_markdown, \n",
        "                             module11_markdown, \n",
        "                             module12_markdown,\n",
        "                             module13_markdown,\n",
        "                             module14_markdown,]\n",
        "                       )\n",
        "display(Markdown(full_markdown))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \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\nIn markov decision processes the prob of ending up in a state is p(s'|s,a) where s' is the next state and s is current state and a is action\n\n\nMarkovian is the prob of ending in s' depending only on s \nand not previous states.\n\nIn MDPs we get a reward at each state which is R(s, s', a), and the rewards are additiive, can be positive or negative. The total reward added is called the environmental history, and sum of rewards at each step is total utility\n\n\nMDPs are sequential, fully observable, stochastic, additive rewards. They have a set of states S, set of actions A, transition function T(s, a, s') where prob of each state is s'P(s'|s,a), as well as initial and temrinal states\n\n\n the Solution to an MDP is a policy and a policy maps from a state onto the action to take if the agent is in the state. \n\n In MDPs we want to find the optimal policy\n\n\nSo we want to maximize the expected utility and maximize that for the possible paths generated by a policy\n\n\n The expected utility is shaped by the reward function. \n\n The performance of an agent is the sum of rewards for the states we choose.\n\n\n We are going to penalize rewards in the future by discounting it by gamma, this allows us to solve the problem if we prefer rewards sooner or later\n\n\n So for discounting, first state we get all reward, second state we get gamma times reward and third we get gamma squared times reward \n\n\n If we have stationary preferences and prefer a to b meaning [a1, a2, ..] > (curly brace) [b1, b2, ...] then if we add a new element the same relationship also \nholds so. Therefore, there are only two ways to define utility: \n \n Additive utility: U([r0, r1, r2, ...]) = r0 + r1 + r2 + ...\n\n\nDiscounted Utility: U([r0, r1, r2, ...]) = r0 + (gamma)r1 + (gamma)^2 r2 + ...\n\n\nTo prevent infinite loops where we bounce around and get infinite rewards we can set a FINITE HORIZON, similar to depth limited search \n Another \nsolution is doing discounting so sum(gamma^t * rt) <= R_max / (1-gamma) \n\n\nEach MDP acts like an expectimax-like search tree. \n\n \nThe expected utility starting in s and acting optimally is V*(s) \n\n \nThe value of a q state is Q*(s,a) which is the expected utility starting out having taken action a from state s and acting optimally. \n\n\nThe optimal policy is pi*(s) = optimal action from state s. \n\n\nWe can compute the expectimax value by doing V*(s) = maxQ*(s, a) which says choose the best sequence of states recrusvively, and Q*(s, a) = sum(T(s, a, s') * (R(s,a,s') + gamma * V*(s')))\nwhere T is the weight at each edge or the probability. \n\n\nThe issue with this definition is the tree goes on forever and get very big\n\n\nValue Iteration: start with V0(s) and no time steps left means an expected reward sum of zero, where we keep track of the time by number of states\n\n\nIn value iteration, repeat the recursion step until convergence and complexity is O(S^2A). \n\n \nFor value iteration, start at the lowest state and go up. \n\n If the discount is less than one than it will conveerge. \n\n\nFor utilities of fixed policies we have the same equation as above except instead of a max now we have the policy function giving us which one to take. \n\n\nPolicy Evaluation: If we start at the bottom and build up recursively we will have O(S^2) iterations \n\n \nFor policy extraction where we are finding policy from values, we need to find the argmax of the previous equation \n\n \nIf we have optimal q values it is very easy to decide what to do. \n\n \nActions are easier to select from q values than values. \n\n \nFor policy iteration (to find optimal values): \n \nStep 1: \n \nPolicy Evaluation:\nCalculate utilites for some fixed policy, not optimal, until convergence. \n\nStep 2: \n \nPolicy Improvement: \nUpdate policy using one step look ahead where utilities are future values. \n\n\nRepeat these steps until we see convergence \n\n\nPOLICY ITERATION IS OPTIMAL ALGORITHM - dynamic programming\n\n\nMaximum Expected Utility: \n\n\nA rational agent should choose action that maximizes its expected utility \n\n \nFor worst case minimax algorithm we can apply transformations and get same result. - insensitivity to monotonic transformations\n\n\nFor average case expectimax reasoning, we need the magnitudes to be meaningful. \n\n \nWe have prizes A, B for preferences and we also have lotteries L = [p, A, (1-p), B] where A has a prob of p and B has a prob of (1-p). \n\n\nA > B means A prefered to be while A ~ B means indifferent.\n\n \nAxiom of Transitivity: \n\n\n(A > B) and (B > C) -> (A > C) \n\n\nOrderability: \n\n\n(A > B) or (B > A) -> (A ~ B) \n\n\nContinuity: \n\n\n A > B > C -> Ep[p, A, 1-p, C] ~ B (meaning there exists a value in between A and C such that we are indifferent to B)\n\n\nSubstitutability: \n\n\n A ~ B -> [p, A, 1-p, C] ~ [p, B, 1-p, C]\nMonotonicity: \n\n\n A > B -> (p >= q <-> [p, A, 1-p, B] >= [q, A, 1-q, B]) (meaning that if we prefer A to B and p >= q then we always want more p to q) \n\n\n If you follow all these axioms then you are a rational agent \n\n\n If you choose actions that maximize expected utility then we are following the MEU principle \n\n\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 \n\n Reinforcement learning is when you dont know probabilities, but based on MDP. You start trying things out and adjust your\nmodel accordignly \n\n In reinforcement learning you \n\n\nIt is still modeled as a MDP but we do not know T or R. \n\n \nIn model based learning we have to find estimates of T and R, by training the model using chosen policies. -> giving us a way to estimate the reward and weights on graph. \n\n\nIn model free, we pick an action and compare result for what values of states to what we had and adjust model accordingly. \n\n \nIn passive learning, we are given a policy and we execute the policy and see what happens. We then average sum of rewards to get the value of being in a state. \n\n\nIn direct evaluation, we run the policy and get values of each node by checking sum of weights till end starting from each node \n\n\nIn sample based policy evaluation, we take sample of outcomes of following a policy and check what reward we got and what state we ended up in, then\nwe take more samples, and average all those together to get good estimate of value of being at S. -> Issue is no guarantee well be in S n times. \n\n\nQ Value Iteration: \n\n\nIn Q value iteration the max is in the inside in the equation for MDP instead of outside. In Q Learning, Q(s, a) = (1-a)Q(s, a) + a(sample) where we keep updating \nthe Q values in a running average. \n\n\nWe will converge towards optimal policy in Q learning EVEN when we are acting sub optimally -> off policy learning. \n\n\nTo force exploration we can use an epsilon greedy approach, where with probability epsilon we explore (act randomly) and with probability 1-e we act on the \ncurrent policy \n\n\nYou can make epsilon smaller and smaller as we move through. \n \n\n## Module 9 Definitions\n* **Exploration** - trying unknown actions to get information\n* **Exploitation** - eventuallt you have to use what you know\n* **Regret** - even if you learn intelligently you will make mistakes\n* **Sampling** - because of chance you have to try things repeatdely\n* **Difficulty** - learning can be much harder than solving a known MDP\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 p(a|b) = p(a intersect b) / p(b). \n\n\nBayes Rule: \n\n\nP(x|y) = P(x) * P(y/x) / P(y)\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 =| is independence \n\n\nP(a and y | z) = P(x|z)P(y|z) \n\n\nP(x|z and y) = P(x|z) \n\n\nP(+y| +x) = 1, P(-y| -x) = 1, P(+z|+y) = 1, P(-z|-y) = 1 \n\n\nTo check if conditionally indepdent To see if X is indepdnet of Z given Y we start with P(z and x|y) and work from there using \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 Naive bayes is where features are all independent of each other. \n\n\nSo naive bayes says what is probability we get something given this set of features. All probability parameters are denoted by theta \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 Your summary here \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 13 Lecture Notes\n Your summary here \n\n## Module 14 Definitions\n* **Concept** - definition\n",
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