{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Template_for CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94f23770-4f6c-47d4-f7ef-e4c57693f306"
      },
      "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": "9c448f73-d5f9-409e-d516-53bdf9691dc2"
      },
      "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 = \"\"\" Bellman in english- Assuming the agent chooses the optimal action, \n",
        "the utility of a state is the expected reward for the next transition plus the discounted \n",
        "utility of the next state.\"\"\"\n",
        "chapter17_2 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\"transition model tells what possible outcomes of an action\\\n",
        "probability of next state only dependent on current state, not history \\ reward function \n",
        "gives reward/penality for every step, not just terminal state. (environmental history)\\\n",
        "MDP can be sovled with expectimax--> define with a set of states, set of actions, transition\n",
        " function, reward function, initial state, (optional) terminal states. \\ solution to \n",
        " MDP = policy\\ optimal policy (pi*)- need to compute expected utility of all possible paths \n",
        " generated by it\\ prefer getting rewards now vs later in stocashtic environments --> \n",
        " discounting \\ dealing with infinite utilities - limit number of steps, discounting (gamma < 1)\\\n",
        "q-state = (state, action) pair. \\ (expectimax) value of a state- expected utility under\n",
        "optimal action; recursively: V*(s) = action with max Q*(s,a) --> expectimax is too much \n",
        "computation, states are computed multiple times (so only compute once with dynamic programming);\n",
        "can also do depth limited computation until change is small\\ value iteration- \n",
        "given vector with values and k steps remaining, update value vector with bellman equation;\n",
        "complexity of each iteration- O(s^2*a)\\ guaranteed to converge to unique optimal values\\\n",
        "V^pi(s) = value under a given policy = expected total rewards starting in s and following pi \\\n",
        "--> can calculate Vs for a given policy using a system of linear equations where the \n",
        "utilities are the variables. \\policy extration- optimal policy is action that has the \n",
        "largest q value Q*(s,a) \\ policy iteration- approach for calculating optimal values:\n",
        "step 1: policy evaluation (calculate utilites for some policy until convergence), step 2-\n",
        "policy improvement (update policy with calculated utilities as future values)- repeat until \n",
        "convergence. --> optimal! \\ value iteration vs policy iteration: value= each iteration\n",
        "updates the values and we take the max over the actions to get the policy, policy= \n",
        "several passes to get utilities from a fixed plicy (fast becuase only one action),\n",
        "then new policy is chosen (slower like value iteration) --> both are dynamic programming \n",
        "methods for solving MDPs \\ to compute optimal vals: val or policy iteration, compute \n",
        "vals for a specific policy: policy evaluation, turn vals into a policy: polixy extraction.\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",
        "\"policy\" : \"what action to take based on current state (takes in state, returns action)\",\n",
        "\"discounting\" : \"value of rewards decay explonentially over time/steps\",\n",
        "\"MDP\": \"sequential, fully observable, stocashtic transition model, additive rewards, (nondeterministic search)\",\n",
        "\"nonstationary policy\": \"depends on time left (limited number of steps)\",\n",
        "\"absorbing state\": \"guarantee that every policy will eventually reach that terminal state\",\n",
        "\"MEU principle\": \"rational agent should choose the action that maximizes expected utility\",\n",
        "\"MEU principle 2\": \"Given axioms of rationality, a utility function should exist such that\",\n",
        "\"insensitivity to monotonic transformations\": \"magnitudes have to be meaningful\",\n",
        "\"utilities\": \"functions from states to numbers that describes the agents preferneces\",\n",
        "\"lotteries\": \"scenarious with uncertain rewards/prizes\",\n",
        "\"Preference vs indiference\": \" >, ~\",\n",
        "\"certainty equivalent\": \"value an agent will accept instead of a lottery (risk-aversion)\"\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": 982
        },
        "outputId": "945aba39-5b9f-4c43-bad7-d0fe9c738056"
      },
      "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 Bellman in english- Assuming the agent chooses the optimal action, \nthe utility of a state is the expected reward for the next transition plus the discounted \nutility of the next state.\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\ntransition model tells what possible outcomes of an actionprobability of next state only dependent on current state, not history \\ reward function \ngives reward/penality for every step, not just terminal state. (environmental history)MDP can be sovled with expectimax--> define with a set of states, set of actions, transition\n function, reward function, initial state, (optional) terminal states. \\ solution to \n MDP = policy\\ optimal policy (pi*)- need to compute expected utility of all possible paths \n generated by it\\ prefer getting rewards now vs later in stocashtic environments --> \n discounting \\ dealing with infinite utilities - limit number of steps, discounting (gamma < 1)q-state = (state, action) pair. \\ (expectimax) value of a state- expected utility under\noptimal action; recursively: V*(s) = action with max Q*(s,a) --> expectimax is too much \ncomputation, states are computed multiple times (so only compute once with dynamic programming);\ncan also do depth limited computation until change is small\\ value iteration- \ngiven vector with values and k steps remaining, update value vector with bellman equation;\ncomplexity of each iteration- O(s^2*a)\\ guaranteed to converge to unique optimal valuesV^pi(s) = value under a given policy = expected total rewards starting in s and following pi --> can calculate Vs for a given policy using a system of linear equations where the \nutilities are the variables. \\policy extration- optimal policy is action that has the \nlargest q value Q*(s,a) \\ policy iteration- approach for calculating optimal values:\nstep 1: policy evaluation (calculate utilites for some policy until convergence), step 2-\npolicy improvement (update policy with calculated utilities as future values)- repeat until \nconvergence. --> optimal! \\ value iteration vs policy iteration: value= each iteration\nupdates the values and we take the max over the actions to get the policy, policy= \nseveral passes to get utilities from a fixed plicy (fast becuase only one action),\nthen new policy is chosen (slower like value iteration) --> both are dynamic programming \nmethods for solving MDPs \\ to compute optimal vals: val or policy iteration, compute \nvals for a specific policy: policy evaluation, turn vals into a policy: polixy extraction.\n \n\n## Module 8 Definitions\n* **Policy** - what action to take based on current state (takes in state, returns action)\n* **Discounting** - value of rewards decay explonentially over time/steps\n* **Mdp** - sequential, fully observable, stocashtic transition model, additive rewards, (nondeterministic search)\n* **Nonstationary policy** - depends on time left (limited number of steps)\n* **Absorbing state** - guarantee that every policy will eventually reach that terminal state\n* **Meu principle** - rational agent should choose the action that maximizes expected utility\n* **Meu principle 2** - Given axioms of rationality, a utility function should exist such that\n* **Insensitivity to monotonic transformations** - magnitudes have to be meaningful\n* **Utilities** - functions from states to numbers that describes the agents preferneces\n* **Lotteries** - scenarious with uncertain rewards/prizes\n* **Preference vs indiference** -  >, ~\n* **Certainty equivalent** - value an agent will accept instead of a lottery (risk-aversion)\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 =  \"\"\"direct utility estimation ignores that the utilities follow the bellman\n",
        "equation (connections between states), so it converges slowly becuase it searches \n",
        "for a solution in a larger hypothesis space. \"\"\"\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 = \"\"\"learning is harder than solving a known MDP, since don't\n",
        "know underlying probability distribution.\\ MDP = office, reinforcement learning = online \n",
        "(actually trying) \\ model-based learning- learn an empirical MDP model based on experience \n",
        "and solve the MDP as if the model were true. \\ model-free learning- dont construct a \n",
        "specific probability model, instead take an action and compare what was thought going\n",
        "to happen to what did happen, and adjust values accordingly (just values, no tracking \n",
        "reward or transtions)--> passive and temporal \\ direct evaluation- act according to \n",
        "a policy, take note of future rewards everytime you visit a state and average the \n",
        "value across all the samples --> pros and cons: easy to understand, doesnt require \n",
        "knowing T and R, outputs correct values, vs. wastes info about\n",
        "transitions, long time to learn because each state learnes seaprately (also difficult \n",
        "to ensure that you get to a specific state n times) \\ problems with temporal learning-\n",
        "(Def: model free method of policy evaluation), hard to turn values into policy--> need\n",
        "to learn q-values not just values--> q-value iteration \\ q-learning: sample based \n",
        "q-value iteration, learn as you go--> called off-policy learning, doesnt have to execute the policy (caveat- have to \n",
        "explore enough and make the learning rate small enough) --> in the limit, doesnt matter\n",
        "how you select your actions\\ exploration --> epsilon greedy policy (with some small\n",
        "probability episoln, act randomly, for the rest follow currency policy --> issue:\n",
        "might explore the same thing a lot --> solution: lower epsilon over time or \\ epsilon functions:\n",
        "explore areas that havent been studied, and dont explore area that are known bad -->\n",
        "takes a value estimate u and visit count n, returns an optimistic utility that can be\n",
        "used in a modified q update\\ regret - difference between expected rewards and optimal \n",
        "rewards --> minimizing this means learning faster? random exploration has higher regret\n",
        "than exploration function\\ approximate q-learning- generalize accross states, cant visit\n",
        "all states and hold values in memory. --> represent states as weighted sum of features-->\n",
        "Q(s,a) = w1f1(s,a) + w2f2(s,a).. \\ evaluation functions score non-terminal nodes\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"reward-to-go\" : \" total reward from a state onwards given a specific policy\",\n",
        "\"direct utility estimation\": \"utility of a state = the expected reward to go\",\n",
        "\"one arm bandit\": \"slot machine with one arm\",\n",
        "\"exploration-exploitation tradeoff\": \"\",\n",
        "\"sampling\": \"needing to try things repeatedly in stochastic environments\",\n",
        "\"reinforcement learning\": \"acts on environemnt and receives feedback in rewards\",\n",
        "\"passive reinforcement learning\": \"similar to policy evaluation- execute a policy and learn from experience\",\n",
        "\"temporal differnece learning\": \"updating V based on new sample instead of averaging at the end --> weighted average with learning rate alpha\",\n",
        "\"exponential moving average\": \"makes recent samples more important, converges with decreasing learning rate\",\n",
        "\"regret\": \"measuring all the mistakes made on the way to finding optimal policy\"\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        },
        "cellView": "form",
        "outputId": "bf4fb254-0213-4420-c38e-a8f0406a8508"
      },
      "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\ndirect utility estimation ignores that the utilities follow the bellman\nequation (connections between states), so it converges slowly becuase it searches \nfor a solution in a larger hypothesis space. \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\nlearning is harder than solving a known MDP, since don't\nknow underlying probability distribution.\\ MDP = office, reinforcement learning = online \n(actually trying) \\ model-based learning- learn an empirical MDP model based on experience \nand solve the MDP as if the model were true. \\ model-free learning- dont construct a \nspecific probability model, instead take an action and compare what was thought going\nto happen to what did happen, and adjust values accordingly (just values, no tracking \nreward or transtions)--> passive and temporal \\ direct evaluation- act according to \na policy, take note of future rewards everytime you visit a state and average the \nvalue across all the samples --> pros and cons: easy to understand, doesnt require \nknowing T and R, outputs correct values, vs. wastes info about\ntransitions, long time to learn because each state learnes seaprately (also difficult \nto ensure that you get to a specific state n times) \\ problems with temporal learning-\n(Def: model free method of policy evaluation), hard to turn values into policy--> need\nto learn q-values not just values--> q-value iteration \\ q-learning: sample based \nq-value iteration, learn as you go--> called off-policy learning, doesnt have to execute the policy (caveat- have to \nexplore enough and make the learning rate small enough) --> in the limit, doesnt matter\nhow you select your actions\\ exploration --> epsilon greedy policy (with some small\nprobability episoln, act randomly, for the rest follow currency policy --> issue:\nmight explore the same thing a lot --> solution: lower epsilon over time or \\ epsilon functions:\nexplore areas that havent been studied, and dont explore area that are known bad -->\ntakes a value estimate u and visit count n, returns an optimistic utility that can be\nused in a modified q update\\ regret - difference between expected rewards and optimal \nrewards --> minimizing this means learning faster? random exploration has higher regret\nthan exploration function\\ approximate q-learning- generalize accross states, cant visit\nall states and hold values in memory. --> represent states as weighted sum of features-->\nQ(s,a) = w1f1(s,a) + w2f2(s,a).. \\ evaluation functions score non-terminal nodes\n\n## Module 9 Definitions\n* **Reward-to-go** -  total reward from a state onwards given a specific policy\n* **Direct utility estimation** - utility of a state = the expected reward to go\n* **One arm bandit** - slot machine with one arm\n* **Exploration-exploitation tradeoff** - \n* **Sampling** - needing to try things repeatedly in stochastic environments\n* **Reinforcement learning** - acts on environemnt and receives feedback in rewards\n* **Passive reinforcement learning** - similar to policy evaluation- execute a policy and learn from experience\n* **Temporal differnece learning** - updating V based on new sample instead of averaging at the end --> weighted average with learning rate alpha\n* **Exponential moving average** - makes recent samples more important, converges with decreasing learning rate\n* **Regret** - measuring all the mistakes made on the way to finding optimal policy\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 = \"\"\"inference by enumeration - normalization denom = summing \n",
        "over the query variable (marginalizaton) \\ address issues with MLE using markov assumption-\n",
        "only based on previous value/state (only depends on previous k words) \"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"sample space\": \"possible worlds, which are mutually exclusive and exhaustive and each has a probability (sum =1)\",\n",
        "\"distribution\": \"list of possible values of a random variable and their probabilities\",\n",
        "\"joint distribution\": \"n variables d domain, d^n size, marginalization is summing out one of the variables\",\n",
        "\"probablistic model\": \"joint distribution over a set of random variables\",\n",
        "\"inference by enumeration\": \"query variables, hidden variables, evidence variables\",\n",
        "\"BAyes rule\": \"P(x|y) = P(y|x)/P(y) * P(x)\",\n",
        "\"maximum likelihood estimation\": \"issues - some sentences may not appear in corpus:data constraints\"\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "1097840c-80f5-47c9-bc7e-21ba3af79d58"
      },
      "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\ninference by enumeration - normalization denom = summing \nover the query variable (marginalizaton) \\ address issues with MLE using markov assumption-\nonly based on previous value/state (only depends on previous k words) \n\n## Module 10 Definitions\n* **Sample space** - possible worlds, which are mutually exclusive and exhaustive and each has a probability (sum =1)\n* **Distribution** - list of possible values of a random variable and their probabilities\n* **Joint distribution** - n variables d domain, d^n size, marginalization is summing out one of the variables\n* **Probablistic model** - joint distribution over a set of random variables\n* **Inference by enumeration** - query variables, hidden variables, evidence variables\n* **Bayes rule** - P(x|y) = P(y|x)/P(y) * P(x)\n* **Maximum likelihood estimation** - issues - some sentences may not appear in corpus:data constraints\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 = \"\"\"seeing if X and Y are independent given Z in a bayes net--> \n",
        "they are d-separated --> consider all undirected paths from x to y, no active ones = (conditional?)independence \\\n",
        "active: path is active is all triples are active --> types: causal chain A->B->C, \n",
        "b unobserved, common cause A <-B-> C, B unobserved, common effect \n",
        "A ->B <-C (v-structure) where b or one of b descendants is observed \\ if all paths inactive,\n",
        "independence guaranteed\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"conditional independence\" : \"P(x, y|z) = P(x|z)*P(y|z) or P(x|z,y)=P(x|z)\",    \n",
        "\"joint probability\": \"P(x,y,z) = P(z) *P(z,y|z)??\",\n",
        "\"common cause\": \"P(x,y,z) = P(y) * P(x|y) * P(z|y)--> x and z independent given y not guaranteed x independent of z,\",\n",
        "\"causal chain\": \"P(x,y,z) = P(x) *P(y|x)P(z|y) --> x independent of z given y P(z|x,y) = P(z|y)\",\n",
        "\"common effect\": \"x and y independent but not independent given z\",\n",
        "\"reachability\": \"undirected path between two nodes without an obstacle node\",\n",
        "\"stationary\": \"the conditional probability table does not change over time\"\n",
        "}\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "cellView": "form",
        "outputId": "c6e0f97f-db80-4601-a8c3-cb2c4a8b4dbc"
      },
      "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\nseeing if X and Y are independent given Z in a bayes net--> \nthey are d-separated --> consider all undirected paths from x to y, no active ones = (conditional?)independence active: path is active is all triples are active --> types: causal chain A->B->C, \nb unobserved, common cause A <-B-> C, B unobserved, common effect \nA ->B <-C (v-structure) where b or one of b descendants is observed \\ if all paths inactive,\nindependence guaranteed\n\n## Module 11 Definitions\n* **Conditional independence** - P(x, y|z) = P(x|z)*P(y|z) or P(x|z,y)=P(x|z)\n* **Joint probability** - P(x,y,z) = P(z) *P(z,y|z)??\n* **Common cause** - P(x,y,z) = P(y) * P(x|y) * P(z|y)--> x and z independent given y not guaranteed x independent of z,\n* **Causal chain** - P(x,y,z) = P(x) *P(y|x)P(z|y) --> x independent of z given y P(z|x,y) = P(z|y)\n* **Common effect** - x and y independent but not independent given z\n* **Reachability** - undirected path between two nodes without an obstacle node\n* **Stationary** - the conditional probability table does not change over time\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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"objective function\" : \"for learning, minimizing error on training data\",\n",
        "\"classification function\": \"computes the label based on data p(y|x)\",\n",
        "\"cross entropy loss\": \"negative log likelihood loss\"    \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": "a720610f-a35b-4ebf-beea-586eba9fd213"
      },
      "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 Your summary here \n\n## Module 12 Definitions\n* **Objective function** - for learning, minimizing error on training data\n* **Classification function** - computes the label based on data p(y|x)\n* **Cross entropy loss** - negative log likelihood loss\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": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "a5adfa6e-9f17-4722-f31c-d648a542f0e8"
      },
      "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": 20,
      "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 term-document matrix - column is document, row is term, \nvalue is number of times occurred. Compared columns to compare documents \\ term-term\nmatrix the value is how often the column word appears in a context window around the other tf-idf- how often a word occured in a doc * 1/#of documents that it occured in \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 = \"\"\" term-document matrix - column is document, row is term, \n",
        "value is number of times occurred. Compared columns to compare documents \\ term-term\n",
        "matrix the value is how often the column word appears in a context window around the other \\\n",
        "tf-idf- how often a word occured in a doc * 1/#of documents that it occured in\"\"\"\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": "48ab830c-87b5-418e-a25d-89f04cbff878"
      },
      "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": "76d23835-f701-4379-ceea-9763a4ce4b7f"
      },
      "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 Bellman in english- Assuming the agent chooses the optimal action, \nthe utility of a state is the expected reward for the next transition plus the discounted \nutility of the next state.\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\ntransition model tells what possible outcomes of an actionprobability of next state only dependent on current state, not history \\ reward function \ngives reward/penality for every step, not just terminal state. (environmental history)MDP can be sovled with expectimax--> define with a set of states, set of actions, transition\n function, reward function, initial state, (optional) terminal states. \\ solution to \n MDP = policy\\ optimal policy (pi*)- need to compute expected utility of all possible paths \n generated by it\\ prefer getting rewards now vs later in stocashtic environments --> \n discounting \\ dealing with infinite utilities - limit number of steps, discounting (gamma < 1)q-state = (state, action) pair. \\ (expectimax) value of a state- expected utility under\noptimal action; recursively: V*(s) = action with max Q*(s,a) --> expectimax is too much \ncomputation, states are computed multiple times (so only compute once with dynamic programming);\ncan also do depth limited computation until change is small\\ value iteration- \ngiven vector with values and k steps remaining, update value vector with bellman equation;\ncomplexity of each iteration- O(s^2*a)\\ guaranteed to converge to unique optimal valuesV^pi(s) = value under a given policy = expected total rewards starting in s and following pi --> can calculate Vs for a given policy using a system of linear equations where the \nutilities are the variables. \\policy extration- optimal policy is action that has the \nlargest q value Q*(s,a) \\ policy iteration- approach for calculating optimal values:\nstep 1: policy evaluation (calculate utilites for some policy until convergence), step 2-\npolicy improvement (update policy with calculated utilities as future values)- repeat until \nconvergence. --> optimal! \\ value iteration vs policy iteration: value= each iteration\nupdates the values and we take the max over the actions to get the policy, policy= \nseveral passes to get utilities from a fixed plicy (fast becuase only one action),\nthen new policy is chosen (slower like value iteration) --> both are dynamic programming \nmethods for solving MDPs \\ to compute optimal vals: val or policy iteration, compute \nvals for a specific policy: policy evaluation, turn vals into a policy: polixy extraction.\n \n\n## Module 8 Definitions\n* **Policy** - what action to take based on current state (takes in state, returns action)\n* **Discounting** - value of rewards decay explonentially over time/steps\n* **Mdp** - sequential, fully observable, stocashtic transition model, additive rewards, (nondeterministic search)\n* **Nonstationary policy** - depends on time left (limited number of steps)\n* **Absorbing state** - guarantee that every policy will eventually reach that terminal state\n* **Meu principle** - rational agent should choose the action that maximizes expected utility\n* **Meu principle 2** - Given axioms of rationality, a utility function should exist such that\n* **Insensitivity to monotonic transformations** - magnitudes have to be meaningful\n* **Utilities** - functions from states to numbers that describes the agents preferneces\n* **Lotteries** - scenarious with uncertain rewards/prizes\n* **Preference vs indiference** -  >, ~\n* **Certainty equivalent** - value an agent will accept instead of a lottery (risk-aversion)\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\ndirect utility estimation ignores that the utilities follow the bellman\nequation (connections between states), so it converges slowly becuase it searches \nfor a solution in a larger hypothesis space. \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\nlearning is harder than solving a known MDP, since don't\nknow underlying probability distribution.\\ MDP = office, reinforcement learning = online \n(actually trying) \\ model-based learning- learn an empirical MDP model based on experience \nand solve the MDP as if the model were true. \\ model-free learning- dont construct a \nspecific probability model, instead take an action and compare what was thought going\nto happen to what did happen, and adjust values accordingly (just values, no tracking \nreward or transtions)--> passive and temporal \\ direct evaluation- act according to \na policy, take note of future rewards everytime you visit a state and average the \nvalue across all the samples --> pros and cons: easy to understand, doesnt require \nknowing T and R, outputs correct values, vs. wastes info about\ntransitions, long time to learn because each state learnes seaprately (also difficult \nto ensure that you get to a specific state n times) \\ problems with temporal learning-\n(Def: model free method of policy evaluation), hard to turn values into policy--> need\nto learn q-values not just values--> q-value iteration \\ q-learning: sample based \nq-value iteration, learn as you go--> called off-policy learning, doesnt have to execute the policy (caveat- have to \nexplore enough and make the learning rate small enough) --> in the limit, doesnt matter\nhow you select your actions\\ exploration --> epsilon greedy policy (with some small\nprobability episoln, act randomly, for the rest follow currency policy --> issue:\nmight explore the same thing a lot --> solution: lower epsilon over time or \\ epsilon functions:\nexplore areas that havent been studied, and dont explore area that are known bad -->\ntakes a value estimate u and visit count n, returns an optimistic utility that can be\nused in a modified q update\\ regret - difference between expected rewards and optimal \nrewards --> minimizing this means learning faster? random exploration has higher regret\nthan exploration function\\ approximate q-learning- generalize accross states, cant visit\nall states and hold values in memory. --> represent states as weighted sum of features-->\nQ(s,a) = w1f1(s,a) + w2f2(s,a).. \\ evaluation functions score non-terminal nodes\n\n## Module 9 Definitions\n* **Reward-to-go** -  total reward from a state onwards given a specific policy\n* **Direct utility estimation** - utility of a state = the expected reward to go\n* **One arm bandit** - slot machine with one arm\n* **Exploration-exploitation tradeoff** - \n* **Sampling** - needing to try things repeatedly in stochastic environments\n* **Reinforcement learning** - acts on environemnt and receives feedback in rewards\n* **Passive reinforcement learning** - similar to policy evaluation- execute a policy and learn from experience\n* **Temporal differnece learning** - updating V based on new sample instead of averaging at the end --> weighted average with learning rate alpha\n* **Exponential moving average** - makes recent samples more important, converges with decreasing learning rate\n* **Regret** - measuring all the mistakes made on the way to finding optimal policy\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\ninference by enumeration - normalization denom = summing \nover the query variable (marginalizaton) \\ address issues with MLE using markov assumption-\nonly based on previous value/state (only depends on previous k words) \n\n## Module 10 Definitions\n* **Sample space** - possible worlds, which are mutually exclusive and exhaustive and each has a probability (sum =1)\n* **Distribution** - list of possible values of a random variable and their probabilities\n* **Joint distribution** - n variables d domain, d^n size, marginalization is summing out one of the variables\n* **Probablistic model** - joint distribution over a set of random variables\n* **Inference by enumeration** - query variables, hidden variables, evidence variables\n* **Bayes rule** - P(x|y) = P(y|x)/P(y) * P(x)\n* **Maximum likelihood estimation** - issues - some sentences may not appear in corpus:data constraints\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\nseeing if X and Y are independent given Z in a bayes net--> \nthey are d-separated --> consider all undirected paths from x to y, no active ones = (conditional?)independence active: path is active is all triples are active --> types: causal chain A->B->C, \nb unobserved, common cause A <-B-> C, B unobserved, common effect \nA ->B <-C (v-structure) where b or one of b descendants is observed \\ if all paths inactive,\nindependence guaranteed\n\n## Module 11 Definitions\n* **Conditional independence** - P(x, y|z) = P(x|z)*P(y|z) or P(x|z,y)=P(x|z)\n* **Joint probability** - P(x,y,z) = P(z) *P(z,y|z)??\n* **Common cause** - P(x,y,z) = P(y) * P(x|y) * P(z|y)--> x and z independent given y not guaranteed x independent of z,\n* **Causal chain** - P(x,y,z) = P(x) *P(y|x)P(z|y) --> x independent of z given y P(z|x,y) = P(z|y)\n* **Common effect** - x and y independent but not independent given z\n* **Reachability** - undirected path between two nodes without an obstacle node\n* **Stationary** - the conditional probability table does not change over time\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 Your summary here \n\n## Module 12 Definitions\n* **Objective function** - for learning, minimizing error on training data\n* **Classification function** - computes the label based on data p(y|x)\n* **Cross entropy loss** - negative log likelihood loss\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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