{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "198feb29-1e1a-49de-d8b5-9e92159cc65b"
      },
      "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": "625ce59c-91aa-470d-c5f1-76658f73a2be"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = True"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 16, “Making Simple Decisions” (16.1-16.3)\n",
        "\n",
        "sample_equation = r\"\"\" $$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$$ \"\"\"\n",
        "utility_function = r\"\"\" $$ \\textnormal{policy function:} \\pi ^*(s)=argmax_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$ \"\"\"\n",
        "bellman = r\"\"\" $$ \\textnormal{bellman equation:} U(s) =max_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$ \"\"\"\n",
        "qvalue = r\"\"\" $$ \\textnormal{Q-value function:} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma \\ max_{a'}Q(s', a')]$$ \"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" Chapter 16.1 summary. \n",
        "A Markov decision process is a sequential decision problem for a fully observable, stochastic environment with a Markovian trans model and additive awards. \\\\\n",
        "\n",
        "Algorithms for MDP:\\\\\n",
        " the following generate exact solutions: value iteration, policy iteration, linear programming\n",
        " Monte Carlo planning is an approximate algorithm.\\\\\n",
        "\n",
        " Bellman equation is the basis for value iteration, if there are n possible states there are n Bellman equations, one for each state. We assign arbitrary initial values for utilities then iterate through the states applying the Bellman equation.  We repeat until we reach an equilibrium.\\\\\n",
        "\n",
        " Policy iteration: given a policy $\\pi_i$ calculate the utility of each state if $\\pi_i$ were to be executed.  Then improve the policy using one step look ahead based on U_i (as in the policy function) \n",
        "\"\"\"  + sample_equation  + utility_function + bellman + qvalue\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 = \"\"\" Your summary here \"\"\"\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",
        "\"sequential decision processes\" : \"an agent's utility depends on a sequence of decisions.\",\n",
        "\"transition model\" : \"describes the outcome of each action in each state, here the outcome is stochastic\",\n",
        "\"Markovian transitions\": \"the probability of reaching s' from s depends only on s and not on the history of earlier states\",\n",
        "\"Policy\": \"a solution to an MDP, that specifies what the agent would do for any state that the agent might reach (pi)\",\n",
        "\"Optimal Policy\" : \"a policy that yields the highest expected utility\",\n",
        "\"discount factor\" : \"is a number between 1 and 0, describes the preference of an agent for current rewards over future rewards, 0=current, 1=long term\",\n",
        "\"proper policy\" : \"a policy that is guaranteed to reach a terminal state\",\n",
        "\"Q function\": \"the expected utility of taking a given action in a given state, aka action-utility function\"\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": 894
        },
        "outputId": "3649568b-d99a-471e-9c67-8255c85243b4",
        "cellView": "form"
      },
      "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. \nA Markov decision process is a sequential decision problem for a fully observable, stochastic environment with a Markovian trans model and additive awards. \\\n\nAlgorithms for MDP:\\\n the following generate exact solutions: value iteration, policy iteration, linear programming\n Monte Carlo planning is an approximate algorithm.\\\n\n Bellman equation is the basis for value iteration, if there are n possible states there are n Bellman equations, one for each state. We assign arbitrary initial values for utilities then iterate through the states applying the Bellman equation.  We repeat until we reach an equilibrium.\\\n\n Policy iteration: given a policy $\\pi_i$ calculate the utility of each state if $\\pi_i$ were to be executed.  Then improve the policy using one step look ahead based on U_i (as in the policy function) \n $$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$$  $$ \\textnormal{policy function:} \\pi ^*(s)=argmax_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$  $$ \\textnormal{bellman equation:} U(s) =max_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$  $$ \\textnormal{Q-value function:} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma \\ max_{a'}Q(s', a')]$$ \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 Your summary here \n\n## Module 8 Definitions\n* **Sequential decision processes** - an agent's utility depends on a sequence of decisions.\n* **Transition model** - describes the outcome of each action in each state, here the outcome is stochastic\n* **Markovian transitions** - the probability of reaching s' from s depends only on s and not on the history of earlier states\n* **Policy** - a solution to an MDP, that specifies what the agent would do for any state that the agent might reach (pi)\n* **Optimal policy** - a policy that yields the highest expected utility\n* **Discount factor** - is a number between 1 and 0, describes the preference of an agent for current rewards over future rewards, 0=current, 1=long term\n* **Proper policy** - a policy that is guaranteed to reach a terminal state\n* **Q function** - the expected utility of taking a given action in a given state, aka action-utility function\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 = \"\"\" Reinforcement Learning: \\\\ \n",
        "1. Exploration: you have to try unknown actions to get info \\\\\n",
        "2. Exploitation: eventually, you have to use what you know \\\\\n",
        "3. Regret: even if you learn intelligently, you make mistakes\\\\\n",
        "4. Sampling: because of chance, you have to try things repeatedly \\\\\n",
        "5. Difficulty: learning can be mush harder that solving a known MDP\\\\\n",
        "\n",
        "Like an MDP but we don't know transition function or reward function (MDP is offline, RL is online)\n",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "cellView": "form",
        "outputId": "e4cd2c27-178f-4dc0-f259-3e47abb5dba1"
      },
      "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 Reinforcement Learning: \\ \n1. Exploration: you have to try unknown actions to get info \\\n2. Exploitation: eventually, you have to use what you know \\\n3. Regret: even if you learn intelligently, you make mistakes\\\n4. Sampling: because of chance, you have to try things repeatedly \\\n5. Difficulty: learning can be mush harder that solving a known MDP\\\n\nLike an MDP but we don't know transition function or reward function (MDP is offline, RL is online)\n \n\n## Module 9 Definitions\n* **Term** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 10 - Probabilities and Language Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 12 “Quantifying Uncertainty” (Sections 12.1-12.7)\n",
        "chapter12_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_6 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Jurafsky and Martin, Speech and Language Processing Chapter 3 “N-Gram Language Models”  (Sections 3.1-3.5,3.7)\n",
        "SLP_chapter3_1 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" \n",
        "Probabilistic model: A joint distribution over a set of random variables. \\\\\n",
        "Event: Set of outcomes in a probabilistic model, IE probability that it is hot and sunny\\\\\n",
        "Marginal distribution: sub tables of a probabilistic model which eliminate variables.\\\\\n",
        "Conditional distribution: probability distributions over some variables given fixed values of others.\\\\\n",
        "Conditional probability: Probability of A given B = Prob of A and B divided by the probablity of B \\\\\n",
        "Product rule: $P(y)P(x|y) = P(x,y)$ \\\\\n",
        "Chain rule: $P(x1, x2, x3) = P(x1)P(x2|x1)P(x3|x1,x2)$ \\\\ \"\"\" + r\"\"\" Bayes rule: $P(x,y) = P(x|y)P(y) = P(y|x)P(x)$  \\\\ \n",
        "or we could write it as $P(x|y) = \\frac{P(y|x)}{P(y)}P(x)$ \\\\\n",
        "Probabilistic language models: goal is to assign a probability to a sentence.\\\\\n",
        "MLE = maximum likelihood estimation.\\\\\n",
        "Markov assumption: the probability of seeing any word only depends on the previous k words not the whole context.\\\\\n",
        "Problems for MLE: out of vocab items.  Avoid zeros with smoothing. \\\\\n",
        "Do everything in log space.  \n",
        "\n",
        "Markov property says that given the present state, the future and past are independent.\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"unigram\" : \"no history\",\n",
        "\"bigram\" : \"1 word as history\",\n",
        "\"trigram\" : \"2 words as history\",\n",
        "\"4-gram\" : \"3 words as history\",\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "5ee56a80-a502-4a4a-b296-f034dfbda2c4"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module10_sections = [\n",
        "  (\"AIMA Chapter 12.1 Acting under Uncertainty\", chapter12_1),\n",
        "  (\"AIMA Chapter 12.2 Basic Probability Notation\", chapter12_2),\n",
        "  (\"AIMA Chapter 12.3 Inference Using Full Joint Distributions\", chapter12_3),\n",
        "  (\"AIMA Chapter 12.4 Independence\", chapter12_4),\n",
        "  (\"AIMA Chapter 12.5 Bayes’ Rule and Its Use\", chapter12_5),\n",
        "  (\"AIMA Chapter 12.6 Naive Bayes Models\", chapter12_6),\n",
        "  (\"AIMA Chapter 12.7 The Wumpus World Revisited\", chapter12_7),\n",
        "  (\"SLP Chapter 3.1 N-Grams\", SLP_chapter3_1),\n",
        "  (\"SLP Chapter 3.2 Evaluating Language Models\", SLP_chapter3_2),\n",
        "  (\"SLP Chapter 3.3 Sampling sentences from a language model\", SLP_chapter3_3),\n",
        "  (\"SLP Chapter 3.4 Generalization and Zeros\", SLP_chapter3_4),\n",
        "  (\"SLP Chapter 3.5 Smoothing\", SLP_chapter3_5),\n",
        "  (\"SLP Chapter 3.7 Huge Language Models and Stupid Backoff\", SLP_chapter3_7),\n",
        "  (\"Module 10 Lecture Notes\", module10_lecture_notes),\n",
        "]\n",
        "\n",
        "module10_markdown = format_module(10, \"Probabilies and Language Models\", module10_sections, module10_definitions)\n",
        "display(Markdown(module10_markdown))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n \nProbabilistic model: A joint distribution over a set of random variables. \\\nEvent: Set of outcomes in a probabilistic model, IE probability that it is hot and sunny\\\nMarginal distribution: sub tables of a probabilistic model which eliminate variables.\\\nConditional distribution: probability distributions over some variables given fixed values of others.\\\nConditional probability: Probability of A given B = Prob of A and B divided by the probablity of B \\\nProduct rule: $P(y)P(x|y) = P(x,y)$ \\\nChain rule: $P(x1, x2, x3) = P(x1)P(x2|x1)P(x3|x1,x2)$ \\  Bayes rule: $P(x,y) = P(x|y)P(y) = P(y|x)P(x)$  \\\\ \nor we could write it as $P(x|y) = \\frac{P(y|x)}{P(y)}P(x)$ \\\\\nProbabilistic language models: goal is to assign a probability to a sentence.\\\\\nMLE = maximum likelihood estimation.\\\\\nMarkov assumption: the probability of seeing any word only depends on the previous k words not the whole context.\\\\\nProblems for MLE: out of vocab items.  Avoid zeros with smoothing. \\\\\nDo everything in log space.  \n\nMarkov property says that given the present state, the future and past are independent.\n\n\n\n## Module 10 Definitions\n* **Unigram** - no history\n* **Bigram** - 1 word as history\n* **Trigram** - 2 words as history\n* **4-gram** - 3 words as history\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, Russell and Norvig, AIMA Chapter 13 “Probabilistic Reasoning” (13.1, 13.2, 13.3)\n",
        "chapter13_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\" \n",
        "Conditional independence:     \n",
        "x and y are independent if $P(x, y) = P(x)P(y)$ , written as $x \\coprod y$     \n",
        "x and y are conditionally independent given z if $P(x,y|z) = P(x|z)P(y|z)$, or equivalently $P(x|z,y) = P(x|z)$ written as $x \\coprod y | z$   \n",
        "Deseparation:  \n",
        "Causal Chains: $P(x,y,z) = P(x)P(y|x)P(z|y)$     \n",
        "Common Cause: $P(x,y,z) = P(y)P(x|y)P(z|y)$    \n",
        "Common Effect $P(x,y,z) = P(x)P(y)P(z|x,y)$     \n",
        "\n",
        "Are two variables in a Bayes Net independent?\n",
        "Active/Inactive paths. Are x and y conditionally independent given evidence variable z?  Yes, if x an y are \"d-separated\" by z.   \n",
        "\n",
        "D-Separation:   \n",
        "Is $x_i \\coprod x_j |{x_{k1},..., x_{kn}}$ ?   \n",
        "Consider all (undirected) paths from x to y, no active paths=independence.   \n",
        "A path is active if each triple is active.  All it takes to block a path is a single inactive segment. \n",
        "Check all undirected paths between Xi and Xj, if one or more active, then independence is not guaranteed.  Otherwise, if all paths are inactive then independence is guaranteed.  \n",
        "A path is different than a three node segment, a path is made of 3 node segments.  If one PATH is active, no independence, however 1 inactive SEGMENT makes the PATH inactive.\n",
        "\"\"\"\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": 707
        },
        "cellView": "form",
        "outputId": "cb2cc6b2-0a34-4188-f439-1573c21a170b"
      },
      "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 \nConditional independence:     \nx and y are independent if $P(x, y) = P(x)P(y)$ , written as $x \\coprod y$     \nx and y are conditionally independent given z if $P(x,y|z) = P(x|z)P(y|z)$, or equivalently $P(x|z,y) = P(x|z)$ written as $x \\coprod y | z$   \nDeseparation:  \nCausal Chains: $P(x,y,z) = P(x)P(y|x)P(z|y)$     \nCommon Cause: $P(x,y,z) = P(y)P(x|y)P(z|y)$    \nCommon Effect $P(x,y,z) = P(x)P(y)P(z|x,y)$     \n\nAre two variables in a Bayes Net independent?\nActive/Inactive paths. Are x and y conditionally independent given evidence variable z?  Yes, if x an y are \"d-separated\" by z.   \n\nD-Separation:   \nIs $x_i \\coprod x_j |{x_{k1},..., x_{kn}}$ ?   \nConsider all (undirected) paths from x to y, no active paths=independence.   \nA path is active if each triple is active.  All it takes to block a path is a single inactive segment. \nCheck all undirected paths between Xi and Xj, if one or more active, then independence is not guaranteed.  Otherwise, if all paths are inactive then independence is guaranteed.  \nA path is different than a three node segment, a path is made of 3 node segments.  If one PATH is active, no independence, however 1 inactive SEGMENT makes the PATH inactive.\n\n\n## Module 11 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 12 - Naive Bayes and Perceptrons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 19 “Learning from Examples” (Sections 19.1-19.6)\n",
        "\n",
        "chapter19_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Speech and Language Processing Chapter 4 “Naive Bayes and Sentiment Classification”\n",
        "slp_chapter4_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_7 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_9 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_10 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\" Naive Bayes.  Assume all features are independent effects of the label. $P(Y, F_1 ... F_n) = P(Y) \\Pi_i P(F_i|Y)$   \n",
        "Naive Bayes for text.  Bag-of-words.  Features: Fi is the word at position i, each Fi is identically distributed.  \n",
        "\n",
        "\n",
        "Trainig and Testing:  \n",
        "Training data, Held out set, test set.  Learn parameters on training, tune hyperparameters on held-out set, compute accuracy on test set.   \n",
        "\n",
        "Generalization and Overfitting:  \n",
        "Relative frequency parameters will overfit the training data.  To generalize better we need to smooth and regularize the estimates.  \n",
        "Laplace smoothing, pretend you saw every outcome once more than you actually did.\n",
        "\n",
        "Perceptrons:  \n",
        "Separability: true if some parameters get the training set perfectly correct.  \n",
        "Convergence: if the training is separable, perceptron will eventually converge (binary case).  \n",
        "Mistake bound: the max number of mistakes (binary case) related to margin or degree of separability.\n",
        "\n",
        "Classification comparison:  \n",
        "Naive Bayes: builds a model from training data, gives prediction probabilities, strong independence assumptions, one pass through data (counting) \n",
        "Perceptrons: Makes less assumptions about data, mistake-driven learning, multiple passes through data (prediction), often more accurate\n",
        "\"\"\"\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": "0d72ee1b-928f-4089-d2b0-bc3a146020b9"
      },
      "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.  Assume all features are independent effects of the label. $P(Y, F_1 ... F_n) = P(Y) \\Pi_i P(F_i|Y)$   \nNaive Bayes for text.  Bag-of-words.  Features: Fi is the word at position i, each Fi is identically distributed.  \n\n\nTrainig and Testing:  \nTraining data, Held out set, test set.  Learn parameters on training, tune hyperparameters on held-out set, compute accuracy on test set.   \n\nGeneralization and Overfitting:  \nRelative frequency parameters will overfit the training data.  To generalize better we need to smooth and regularize the estimates.  \nLaplace smoothing, pretend you saw every outcome once more than you actually did.\n\nPerceptrons:  \nSeparability: true if some parameters get the training set perfectly correct.  \nConvergence: if the training is separable, perceptron will eventually converge (binary case).  \nMistake bound: the max number of mistakes (binary case) related to margin or degree of separability.\n\nClassification comparison:  \nNaive Bayes: builds a model from training data, gives prediction probabilities, strong independence assumptions, one pass through data (counting) \nPerceptrons: Makes less assumptions about data, mistake-driven learning, multiple passes through data (prediction), often more accurate\n\n\n## Module 12 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" \n",
        "Logistic Regression:  \n",
        "Like Perceptron uses a vector of weights and a bias term $z = \\sum_i w_i x_i + b$ this can also be written as a dot product $z = w \\cdot x + b$   \n",
        "Instead of outputting z directly, logistic regression transforms it with the sigmoid function.  The problem with a step function (Perceptron) is a small change in the weights or bias of any single perceptron in the network\n",
        "can cause the output to flip completely from 0 to 1.  \n",
        "\n",
        "Sigmoid function:  \"\"\" + r\"\"\"$\\sigma (z) = \\frac{1}{1 + e^{-z}}$, Sigmoid neuron output = \"\"\" + r\"\"\"$\\sigma (w \\cdot x + b)$\"\"\" +\"\"\"\n",
        "\n",
        "The smoothing is crucial, smoothness of $\\sigma$ means that small changes in the weights and bias will produce small changes in the output of the neuron.  \n",
        "  \n",
        "Activation Functions:  \n",
        "Instead of outputing $z= w \\cdot x + b$ which is a linear function of x, neuron untis apply a non-linear function $f$ to z.\n",
        "The output of this function is called the activation value for the unit.  \n",
        "3 most common non-linear functions for $f$: sigmoid, tanh, relu.  \n",
        "\n",
        "Types of Neural Networks:\n",
        "Feed-Forward:  \n",
        "Simplest kind, all units are usually fully-connected, and no cycles.  \n",
        "\n",
        "Training neural nets:  \n",
        "We need a lost function (cross-entropy loss), an optimization algorithm (gradient descent), a way of computing the gradient of the loss function (error propagation).\n",
        "\n",
        "\n",
        "Convolution image kernel, multiply each value of the kernel with each corresponding image value then sum to get the output.\n",
        "\"\"\"\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": "d81d9d32-5736-4c9a-eded-e2169c2b16f5"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module13_sections = [\n",
        "  (\"SLP Chapter 5.1 Classification: the sigmoid\", slp_chapter5_1),\n",
        "  (\"SLP Chapter 5.2 Learning in Logistic Regression\", slp_chapter5_2),\n",
        "  (\"SLP Chapter 5.3 The cross-entropy loss function\", slp_chapter5_3),\n",
        "  (\"SLP Chapter 5.4 Gradient Descent\", slp_chapter5_4),\n",
        "  (\"SLP Chapter 5.5 Regularization\", slp_chapter5_5),\n",
        "  (\"SLP Chapter 5.6 Multinomial logistic regression\", slp_chapter5_6),\n",
        "\n",
        "  (\"SLP Chapter 7.1 Units\", slp_chapter7_1),\n",
        "  (\"SLP Chapter 7.2 The XOR problem\", slp_chapter7_2),\n",
        "  (\"SLP Chapter 7.3 Feedforward Neural Networks\", slp_chapter7_3),\n",
        "  (\"SLP Chapter 7.4 Feedforward networks for NLP: Classification\", slp_chapter7_4),\n",
        "  (\"SLP Chapter 7.5 Feedforward Neural Language Modeling\", slp_chapter7_5),\n",
        "  (\"SLP Chapter 7.6 Training Neural Nets\", slp_chapter7_6),\n",
        "  (\"SLP Chapter 7.7 Training the neural language model\", slp_chapter7_7),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module13_markdown = format_module(13, \"Neural Networks\", \n",
        "                         module13_sections, \n",
        "                         module13_definitions)\n",
        "display(Markdown(module13_markdown))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n \nLogistic Regression:  \nLike Perceptron uses a vector of weights and a bias term $z = \\sum_i w_i x_i + b$ this can also be written as a dot product $z = w \\cdot x + b$   \nInstead of outputting z directly, logistic regression transforms it with the sigmoid function.  The problem with a step function (Perceptron) is a small change in the weights or bias of any single perceptron in the network\ncan cause the output to flip completely from 0 to 1.  \n\nSigmoid function:  $\\sigma (z) = \\frac{1}{1 + e^{-z}}$, Sigmoid neuron output = $\\sigma (w \\cdot x + b)$\n\nThe smoothing is crucial, smoothness of $\\sigma$ means that small changes in the weights and bias will produce small changes in the output of the neuron.  \n  \nActivation Functions:  \nInstead of outputing $z= w \\cdot x + b$ which is a linear function of x, neuron untis apply a non-linear function $f$ to z.\nThe output of this function is called the activation value for the unit.  \n3 most common non-linear functions for $f$: sigmoid, tanh, relu.  \n\nTypes of Neural Networks:\nFeed-Forward:  \nSimplest kind, all units are usually fully-connected, and no cycles.  \n\nTraining neural nets:  \nWe need a lost function (cross-entropy loss), an optimization algorithm (gradient descent), a way of computing the gradient of the loss function (error propagation).\n\n\nConvolution image kernel, multiply each value of the kernel with each corresponding image value then sum to get the output.\n\n\n## Module 13 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 6 “Vector Semantics and Embeddings” (6.1-6.4, 6.8, 6.10-6.12)\n",
        "\n",
        "slp_chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\"\n",
        "Term-Document Matrix:  \n",
        "Each column represents a document   \n",
        "Each row represents a term   \n",
        "The value in each cell is based on how often that term occurred in that document   \n",
        "The number of rows (length of document vectors) is the size of the vocabulary   \n",
        "We can measure how similar two documents are by comparing their column vectors   \n",
        "  \n",
        "Term-Term Matrix\n",
        "Rows and columns are terms   \n",
        "The value in each cell indicates how often the columnar word appears in a context windo surrounding the row word.  \n",
        "  \n",
        "Context-Windows:  \n",
        "A context-window of +/- 2 words, for each word would keep track of how many times each term is immediately preceding it, immediately following it, plus one on each side.  \n",
        "  \n",
        "Instead of raw counts its common to transform vectors using TF-IDF or PPMI.  \n",
        "TF-IDF = Term frequency * inverse document frequency.  IE how often a word occurred in a document * 1 divided by the number of documents it occured in  \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"Polysemy\" : \"A word that has multiple meanings, each aspect being a word sense\", \n",
        "\"Term-Document Matrix\" :\"a matrix where each column represents a document, each row a word, each cell has the count for that word in each document\"  \n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a107ad55-7ac7-4f9b-cda5-df1ddabbf687",
        "cellView": "form"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module14_sections = [\n",
        "  (\"SLP Chapter 9.1 Language Models Revisited\", slp_chapter9_1),\n",
        "  (\"SLP Chapter 9.2 Recurrent Neural Networks\", slp_chapter9_2),\n",
        "  (\"SLP Chapter 9.3 RNNs as Language Models\", slp_chapter9_3),\n",
        "\n",
        "  (\"SLP Chapter 6.1 Lexical Semantics\", slp_chapter6_1),\n",
        "  (\"SLP Chapter 6.2 Vector Semantics\", slp_chapter6_2),\n",
        "  (\"SLP Chapter 6.3 Words and Vectors\", slp_chapter6_3),\n",
        "  (\"SLP Chapter 6.4 Cosine for measuring similarity\", slp_chapter6_4),\n",
        "  (\"SLP Chapter 6.8 Word2vec\", slp_chapter6_8),\n",
        "  (\"SLP Chapter 6.10 Semantic properties of embeddings\", slp_chapter6_10),\n",
        "  (\"SLP Chapter 6.11 Bias and Embeddings\", slp_chapter6_11),\n",
        "  (\"SLP Chapter 6.12 Evaluating Vector Models\", slp_chapter6_12),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module14_markdown = format_module(14, \"Natural Language Processing\", \n",
        "                         module14_sections, \n",
        "                         module14_definitions)\n",
        "display(Markdown(module14_markdown))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n \nLogistic Regression:  \nLike Perceptron uses a vector of weights and a bias term $z = \\sum_i w_i x_i + b$ this can also be written as a dot product $z = w \\cdot x + b$   \nInstead of outputting z directly, logistic regression transforms it with the sigmoid function.  The problem with a step function (Perceptron) is a small change in the weights or bias of any single perceptron in the network\ncan cause the output to flip completely from 0 to 1.  \n\nSigmoid function:  $\\sigma (z) = \\frac{1}{1 + e^{-z}}$, Sigmoid neuron output = $\\sigma (w \\cdot x + b)$\n\nThe smoothing is crucial, smoothness of $\\sigma$ means that small changes in the weights and bias will produce small changes in the output of the neuron.  \n  \nActivation Functions:  \nInstead of outputing $z= w \\cdot x + b$ which is a linear function of x, neuron untis apply a non-linear function $f$ to z.\nThe output of this function is called the activation value for the unit.  \n3 most common non-linear functions for $f$: sigmoid, tanh, relu.  \n\nTypes of Neural Networks:\nFeed-Forward:  \nSimplest kind, all units are usually fully-connected, and no cycles.  \n\nTraining neural nets:  \nWe need a lost function (cross-entropy loss), an optimization algorithm (gradient descent), a way of computing the gradient of the loss function (error propagation).\n\n\nConvolution image kernel, multiply each value of the kernel with each corresponding image value then sum to get the output.\n\n\n## Module 14 Definitions\n* **Polysemy** - A word that has multiple meanings, each aspect being a word sense\n* **Term-document matrix** - a matrix where each column represents a document, each row a word, each cell has the count for that word in each document\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": "30fcc988-383b-4da1-c9a1-67b85cef80fa"
      },
      "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. \nA Markov decision process is a sequential decision problem for a fully observable, stochastic environment with a Markovian trans model and additive awards. \\\n\nAlgorithms for MDP:\\\n the following generate exact solutions: value iteration, policy iteration, linear programming\n Monte Carlo planning is an approximate algorithm.\\\n\n Bellman equation is the basis for value iteration, if there are n possible states there are n Bellman equations, one for each state. We assign arbitrary initial values for utilities then iterate through the states applying the Bellman equation.  We repeat until we reach an equilibrium.\\\n\n Policy iteration: given a policy $\\pi_i$ calculate the utility of each state if $\\pi_i$ were to be executed.  Then improve the policy using one step look ahead based on U_i (as in the policy function) \n $$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$$  $$ \\textnormal{policy function:} \\pi ^*(s)=argmax_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$  $$ \\textnormal{bellman equation:} U(s) =max_{a \\in A(s)} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma U(s')]$$  $$ \\textnormal{Q-value function:} \\sum_{s'} P(s' | s, a)[R(s, a, s') + \\gamma \\ max_{a'}Q(s', a')]$$ \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 Your summary here \n\n## Module 8 Definitions\n* **Sequential decision processes** - an agent's utility depends on a sequence of decisions.\n* **Transition model** - describes the outcome of each action in each state, here the outcome is stochastic\n* **Markovian transitions** - the probability of reaching s' from s depends only on s and not on the history of earlier states\n* **Policy** - a solution to an MDP, that specifies what the agent would do for any state that the agent might reach (pi)\n* **Optimal policy** - a policy that yields the highest expected utility\n* **Discount factor** - is a number between 1 and 0, describes the preference of an agent for current rewards over future rewards, 0=current, 1=long term\n* **Proper policy** - a policy that is guaranteed to reach a terminal state\n* **Q function** - the expected utility of taking a given action in a given state, aka action-utility function\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 Reinforcement Learning: \\ \n1. Exploration: you have to try unknown actions to get info \\\n2. Exploitation: eventually, you have to use what you know \\\n3. Regret: even if you learn intelligently, you make mistakes\\\n4. Sampling: because of chance, you have to try things repeatedly \\\n5. Difficulty: learning can be mush harder that solving a known MDP\\\n\nLike an MDP but we don't know transition function or reward function (MDP is offline, RL is online)\n \n\n## Module 9 Definitions\n* **Term** - your definition here\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n \nProbabilistic model: A joint distribution over a set of random variables. \\\nEvent: Set of outcomes in a probabilistic model, IE probability that it is hot and sunny\\\nMarginal distribution: sub tables of a probabilistic model which eliminate variables.\\\nConditional distribution: probability distributions over some variables given fixed values of others.\\\nConditional probability: Probability of A given B = Prob of A and B divided by the probablity of B \\\nProduct rule: $P(y)P(x|y) = P(x,y)$ \\\nChain rule: $P(x1, x2, x3) = P(x1)P(x2|x1)P(x3|x1,x2)$ \\  Bayes rule: $P(x,y) = P(x|y)P(y) = P(y|x)P(x)$  \\\\ \nor we could write it as $P(x|y) = \\frac{P(y|x)}{P(y)}P(x)$ \\\\\nProbabilistic language models: goal is to assign a probability to a sentence.\\\\\nMLE = maximum likelihood estimation.\\\\\nMarkov assumption: the probability of seeing any word only depends on the previous k words not the whole context.\\\\\nProblems for MLE: out of vocab items.  Avoid zeros with smoothing. \\\\\nDo everything in log space.  \n\nMarkov property says that given the present state, the future and past are independent.\n\n\n\n## Module 10 Definitions\n* **Unigram** - no history\n* **Bigram** - 1 word as history\n* **Trigram** - 2 words as history\n* **4-gram** - 3 words as history\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 \nConditional independence:     \nx and y are independent if $P(x, y) = P(x)P(y)$ , written as $x \\coprod y$     \nx and y are conditionally independent given z if $P(x,y|z) = P(x|z)P(y|z)$, or equivalently $P(x|z,y) = P(x|z)$ written as $x \\coprod y | z$   \nDeseparation:  \nCausal Chains: $P(x,y,z) = P(x)P(y|x)P(z|y)$     \nCommon Cause: $P(x,y,z) = P(y)P(x|y)P(z|y)$    \nCommon Effect $P(x,y,z) = P(x)P(y)P(z|x,y)$     \n\nAre two variables in a Bayes Net independent?\nActive/Inactive paths. Are x and y conditionally independent given evidence variable z?  Yes, if x an y are \"d-separated\" by z.   \n\nD-Separation:   \nIs $x_i \\coprod x_j |{x_{k1},..., x_{kn}}$ ?   \nConsider all (undirected) paths from x to y, no active paths=independence.   \nA path is active if each triple is active.  All it takes to block a path is a single inactive segment. \nCheck all undirected paths between Xi and Xj, if one or more active, then independence is not guaranteed.  Otherwise, if all paths are inactive then independence is guaranteed.  \nA path is different than a three node segment, a path is made of 3 node segments.  If one PATH is active, no independence, however 1 inactive SEGMENT makes the PATH inactive.\n\n\n## Module 11 Definitions\n* **Concept** - definition\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n Naive Bayes.  Assume all features are independent effects of the label. $P(Y, F_1 ... F_n) = P(Y) \\Pi_i P(F_i|Y)$   \nNaive Bayes for text.  Bag-of-words.  Features: Fi is the word at position i, each Fi is identically distributed.  \n\n\nTrainig and Testing:  \nTraining data, Held out set, test set.  Learn parameters on training, tune hyperparameters on held-out set, compute accuracy on test set.   \n\nGeneralization and Overfitting:  \nRelative frequency parameters will overfit the training data.  To generalize better we need to smooth and regularize the estimates.  \nLaplace smoothing, pretend you saw every outcome once more than you actually did.\n\nPerceptrons:  \nSeparability: true if some parameters get the training set perfectly correct.  \nConvergence: if the training is separable, perceptron will eventually converge (binary case).  \nMistake bound: the max number of mistakes (binary case) related to margin or degree of separability.\n\nClassification comparison:  \nNaive Bayes: builds a model from training data, gives prediction probabilities, strong independence assumptions, one pass through data (counting) \nPerceptrons: Makes less assumptions about data, mistake-driven learning, multiple passes through data (prediction), often more accurate\n\n\n## Module 12 Definitions\n* **Concept** - definition\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n \nLogistic Regression:  \nLike Perceptron uses a vector of weights and a bias term $z = \\sum_i w_i x_i + b$ this can also be written as a dot product $z = w \\cdot x + b$   \nInstead of outputting z directly, logistic regression transforms it with the sigmoid function.  The problem with a step function (Perceptron) is a small change in the weights or bias of any single perceptron in the network\ncan cause the output to flip completely from 0 to 1.  \n\nSigmoid function:  $\\sigma (z) = \\frac{1}{1 + e^{-z}}$, Sigmoid neuron output = $\\sigma (w \\cdot x + b)$\n\nThe smoothing is crucial, smoothness of $\\sigma$ means that small changes in the weights and bias will produce small changes in the output of the neuron.  \n  \nActivation Functions:  \nInstead of outputing $z= w \\cdot x + b$ which is a linear function of x, neuron untis apply a non-linear function $f$ to z.\nThe output of this function is called the activation value for the unit.  \n3 most common non-linear functions for $f$: sigmoid, tanh, relu.  \n\nTypes of Neural Networks:\nFeed-Forward:  \nSimplest kind, all units are usually fully-connected, and no cycles.  \n\nTraining neural nets:  \nWe need a lost function (cross-entropy loss), an optimization algorithm (gradient descent), a way of computing the gradient of the loss function (error propagation).\n\n\nConvolution image kernel, multiply each value of the kernel with each corresponding image value then sum to get the output.\n\n\n## Module 13 Definitions\n* **Concept** - definition\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n \nLogistic Regression:  \nLike Perceptron uses a vector of weights and a bias term $z = \\sum_i w_i x_i + b$ this can also be written as a dot product $z = w \\cdot x + b$   \nInstead of outputting z directly, logistic regression transforms it with the sigmoid function.  The problem with a step function (Perceptron) is a small change in the weights or bias of any single perceptron in the network\ncan cause the output to flip completely from 0 to 1.  \n\nSigmoid function:  $\\sigma (z) = \\frac{1}{1 + e^{-z}}$, Sigmoid neuron output = $\\sigma (w \\cdot x + b)$\n\nThe smoothing is crucial, smoothness of $\\sigma$ means that small changes in the weights and bias will produce small changes in the output of the neuron.  \n  \nActivation Functions:  \nInstead of outputing $z= w \\cdot x + b$ which is a linear function of x, neuron untis apply a non-linear function $f$ to z.\nThe output of this function is called the activation value for the unit.  \n3 most common non-linear functions for $f$: sigmoid, tanh, relu.  \n\nTypes of Neural Networks:\nFeed-Forward:  \nSimplest kind, all units are usually fully-connected, and no cycles.  \n\nTraining neural nets:  \nWe need a lost function (cross-entropy loss), an optimization algorithm (gradient descent), a way of computing the gradient of the loss function (error propagation).\n\n\nConvolution image kernel, multiply each value of the kernel with each corresponding image value then sum to get the output.\n\n\n## Module 14 Definitions\n* **Polysemy** - A word that has multiple meanings, each aspect being a word sense\n* **Term-document matrix** - a matrix where each column represents a document, each row a word, each cell has the count for that word in each document\n",
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