{
  "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": "ae69b4fd-628f-48cb-a73d-1677b01cb77d"
      },
      "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": "4862c9f2-510a-498c-d47e-a58952e1cd22"
      },
      "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 =  \"\"\" Axioms of utility theory will be followed by a rational agent. Markovw models dont care about past. Score of an agent is the sum of all the sequential scores. Discounting is a punishmnt for many steps, values\n",
        "of further rewards decay exponentially, this will bound our rewards. Deal with infinity by setting max number of steps or discounting with gamma or guarantee an absorbing - terminal state\n",
        "\n",
        "ACTIONS ARE EASIER TO CHOOSE FROM VALUES THAN VICE VERSA\n",
        "\n",
        "\n",
        "Policy iteration: Faster than value iteration (where actions converge much faster than the values) and it is optimal. We are given a fixed policy and then we try to improve it \"\"\"\n",
        "chapter16_3 =  \"\"\" We set utility function by fixing the utilities of any perticular outcome (notmalize utilities use 1 for success, 0 for fail) \"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" There is a reward in each state. \"\"\"\n",
        "chapter17_2 =  \"\"\" Value itaration calculated a an optimal policy. Utility of a state is the states reward + expected utility of its neigbors.\n",
        "Bellman update will reach equilibrium.\n",
        "\n",
        "Value iteration algo:\n",
        "Complexity O(s^2A), it ia guaranteed to converge \"\"\"\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",
        "\"decision theory\" : \"deals with choosing among actions based on the desirability of their immediate outcomes\",\n",
        "\"utility function\" : \"assigns a single number to express the desirability of a state.\",\n",
        "\"lottery\": \"an action is a tiquet and lotery L has different outcomes s1...sn. L = [p1,S1;...;pn,Sn]\",\n",
        "\"orderability\": \"given two lotteries an agent must prefer one or the other, or state both as equal (A>B, B>A, A==B)\",\n",
        "\"transitivity\": \"given three loteries. If A > C and C > B, then A > B\",\n",
        "\"continuity\" : \"If some lottery B is between A and C in preference, then there is some probability p for which the rational agent will be indifferent between getting B for sure and the lottery that yields A with probability p and C with probability 1 − p.\",\n",
        "\"sustainability\" : \"A ∼ B ⇒ [p, A; 1 − p, C] ∼ [p, B; 1 − p, C]\",\n",
        "\"monotonicity\" : \"Suppose two lotteries have the same two possible outcomes, A and B. If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). A ' B ⇒ (p>q ⇔ [p, A; 1 − p, B] ' [q, A; 1 − q,B])\",\n",
        "\"Preference elicitation\" : \"\"\"involves presenting choices to the agent and using the observes preferences to determine the utility function\"\"\",\n",
        "\"micromort\" : \"a one in a million chance of death\",\n",
        "\"monotonic preference\" : \"agent prefers more money than less\",\n",
        "\"unbiased\" : \"the expected value of the error, E(EU!(a|e) − EU (a|e))), is zero, which means it makes sense to choose the action with the higest expected utility\",\n",
        "\"normative theory\" : \"describes how rational agents should act\",\n",
        "\"stochaistic dominance\" : \"probability dominance of outcomes\",\n",
        "\"transition model\" : \"P(s' | s, a)\",\n",
        "\"policy\" : \"A solution, pi(s) is the action recommended by the policy at state s\",\n",
        "\"optimal policy\" : \"highesr expected utility\",\n",
        "\"discount factor\" : \"between 0 and 1 describes prefernece of current over future rewards\",\n",
        "\"proper policy\" : \"guaranteed to reach a final state\",\n",
        "\"contraction\" : \"everytime you apply a change to 2 values, they come closer together\",\n",
        "\"stationary preferences\" : \"Sequence preferences do not vary if a constant is added to both sequences\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elCCgY64spuq"
      },
      "source": [
        "## Preview of your notes for Module 8\n",
        "\n",
        "Here's a preview of your how your notes will be displayed.  At the end of this Python notebook, we'll aggregate your notes for all modules together into a single display.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYeNcdM_maE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "384f153e-5d8b-4456-9854-bccf210c6f5e"
      },
      "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": "## WARNING: Your definition of 'continuity' is too long!\nWARNING: Your definition of 'monotonicity' is too long!\nWARNING: Your definition of 'unbiased' is too long!\n# 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 Axioms of utility theory will be followed by a rational agent. Markovw models dont care about past. Score of an agent is the sum of all the sequential scores. Discounting is a punishmnt for many steps, values\nof further rewards decay exponentially, this will bound our rewards. Deal with infinity by setting max number of steps or discounting with gamma or guarantee an absorbing - terminal state\n\nACTIONS ARE EASIER TO CHOOSE FROM VALUES THAN VICE VERSA\n\n\nPolicy iteration: Faster than value iteration (where actions converge much faster than the values) and it is optimal. We are given a fixed policy and then we try to improve it \n\n## AIMA Chapter 16.3 Utility Functions\n We set utility function by fixing the utilities of any perticular outcome (notmalize utilities use 1 for success, 0 for fail) \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n There is a reward in each state. \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Value itaration calculated a an optimal policy. Utility of a state is the states reward + expected utility of its neigbors.\nBellman update will reach equilibrium.\n\nValue iteration algo:\nComplexity O(s^2A), it ia guaranteed to converge \n\n## Module 8 Lecture Notes\n Your summary here \n\n## Module 8 Definitions\n* **Decision theory** - deals with choosing among actions based on the desirability of their immediate outcomes\n* **Utility function** - assigns a single number to express the desirability of a state.\n* **Lottery** - an action is a tiquet and lotery L has different outcomes s1...sn. L = [p1,S1;...;pn,Sn]\n* **Orderability** - given two lotteries an agent must prefer one or the other, or state both as equal (A>B, B>A, A==B)\n* **Transitivity** - given three loteries. If A > C and C > B, then A > B\n* **Continuity** - If some lottery B is between A and C in preference, then there is some probability p for which the rational agent will be indifferent between getting B for sure and the lottery that yields A with probability p and C with probability 1 − p.\n* **Sustainability** - A ∼ B ⇒ [p, A; 1 − p, C] ∼ [p, B; 1 − p, C]\n* **Monotonicity** - Suppose two lotteries have the same two possible outcomes, A and B. If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). A ' B ⇒ (p>q ⇔ [p, A; 1 − p, B] ' [q, A; 1 − q,B])\n* **Preference elicitation** - involves presenting choices to the agent and using the observes preferences to determine the utility function\n* **Micromort** - a one in a million chance of death\n* **Monotonic preference** - agent prefers more money than less\n* **Unbiased** - the expected value of the error, E(EU!(a|e) − EU (a|e))), is zero, which means it makes sense to choose the action with the higest expected utility\n* **Normative theory** - describes how rational agents should act\n* **Stochaistic dominance** - probability dominance of outcomes\n* **Transition model** - P(s' | s, a)\n* **Policy** - A solution, pi(s) is the action recommended by the policy at state s\n* **Optimal policy** - highesr expected utility\n* **Discount factor** - between 0 and 1 describes prefernece of current over future rewards\n* **Proper policy** - guaranteed to reach a final state\n* **Contraction** - everytime you apply a change to 2 values, they come closer together\n* **Stationary preferences** - Sequence preferences do not vary if a constant is added to both sequences\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 =  \"\"\" We do not know the probability distributions.\n",
        "\n",
        "The world is till govened by an MSP but we must find the policy\n",
        "\n",
        "Model based learning: Through experiences build an empirical MDP model and then solve the model. We basically have training episodes\n",
        "\n",
        "Model-free learning: Dont know the transitions or rewards functions. We have a given policy and want to determine the state values.\n",
        "\n",
        "Direct evaluation (model free): Follow the policy multiple times, in each find the total value of being in a state (add rewards until the end) and avg out those to get the state values \n",
        "\n",
        "Temporal diff learning: more common states will be updated more often. update value of state each time you see it (v(s) -> (1-a)V(s) + a(sample)). More recent samples are gonna be woth more\n",
        "\n",
        "Q-learning: update q values everytime you see them ilimar to temp diff learning, there is convergance. Choose random actions\n",
        "\n",
        "Exploration vs exploitation: in q-learning you can use the epsilon greeding algorithm where if a random number is smaller than epsilon, then act randomly, else act on current policy. Lower epsilon over time\n",
        "\n",
        "Exploration function: f(utillity, num visited) = u + k/n, where good states are going to be visited more often \n",
        "\n",
        "Regret: measure how many mistakes we made over time. Regret is the difference between the optimal policy and our original policy. Minimizing regret meanis you are learning optimally\n",
        "\n",
        "Approximate q-learning: Sometimes there are TOO many states, so we need to generalize similar states. Represent states with multiple features. In each iteration update each individual weight of a feature.\n",
        "If something unexpected goes on, blame the features that were active. Disprefer all states with that states features \"\"\"\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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"exploration\" : \"you have to try unknown actions to get info\",\n",
        "\"exploitation\" : \"eventually, you have to use what you know\",\n",
        "\"regret\" : \"even if you learn intelligently, you make mistakes\",\n",
        "\"sampling\" : \"because of chance, you have to try things repeatedly\",\n",
        "\"difficulty\" : \"learning can be much harder than solving a known MDP\",\n",
        "\"passive learning\" : \"we let someone else choose the policy and then we follow it\",\n",
        "\"active learning\" : \"learning makes the action choices\"\n",
        "}"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "cellView": "form",
        "outputId": "0054d91b-60c8-4aa3-ac19-bc6188914a86"
      },
      "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 We do not know the probability distributions.\n\nThe world is till govened by an MSP but we must find the policy\n\nModel based learning: Through experiences build an empirical MDP model and then solve the model. We basically have training episodes\n\nModel-free learning: Dont know the transitions or rewards functions. We have a given policy and want to determine the state values.\n\nDirect evaluation (model free): Follow the policy multiple times, in each find the total value of being in a state (add rewards until the end) and avg out those to get the state values \n\nTemporal diff learning: more common states will be updated more often. update value of state each time you see it (v(s) -> (1-a)V(s) + a(sample)). More recent samples are gonna be woth more\n\nQ-learning: update q values everytime you see them ilimar to temp diff learning, there is convergance. Choose random actions\n\nExploration vs exploitation: in q-learning you can use the epsilon greeding algorithm where if a random number is smaller than epsilon, then act randomly, else act on current policy. Lower epsilon over time\n\nExploration function: f(utillity, num visited) = u + k/n, where good states are going to be visited more often \n\nRegret: measure how many mistakes we made over time. Regret is the difference between the optimal policy and our original policy. Minimizing regret meanis you are learning optimally\n\nApproximate q-learning: Sometimes there are TOO many states, so we need to generalize similar states. Represent states with multiple features. In each iteration update each individual weight of a feature.\nIf something unexpected goes on, blame the features that were active. Disprefer all states with that states features \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 Your summary here \n\n## Module 9 Definitions\n* **Exploration** - you have to try unknown actions to get info\n* **Exploitation** - eventually, you have to use what you know\n* **Regret** - even if you learn intelligently, you make mistakes\n* **Sampling** - because of chance, you have to try things repeatedly\n* **Difficulty** - learning can be much harder than solving a known MDP\n* **Passive learning** - we let someone else choose the policy and then we follow it\n* **Active learning** - learning makes the action choices\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 = \"\"\" Size of distribution if n variables have domaon size d is d^n \n",
        "\n",
        "Probabilistic inference: split variables into evidence, query, and hidden variables. Evidence variables are the relevant variables used to find the wanted information.\n",
        "Query variables are the actual variables we are looking for. Whe care about P(Query|Evidence1...EvidenceN\n",
        "\n",
        "Language models probability: Make markov assumption and only consider previous k words when prediction new word instead of considering all previous words in the sentence\n",
        "\n",
        "After building a bayes net you can group triplets into any of the three chains\"\"\"\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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"marginal distribution\" : \"a subtable of joint distributions\",\n",
        "\"conditional distribution\" : \"P(a|b) = P(a and b) / P(b)\",\n",
        "\"causal chain\" : \"low pressure(x) -> rain(y) -> traffic(z) =  P(x)P(y|x)P(z|y) = given y, the others are independent\",\n",
        "\"common cause\" : \"project due(y) -> piazza full(x), project due(y) -> OH full(z) = P(y)P(x|y)P(z|y) = given y, the others are independent\",\n",
        "\"common effect\" : \"raining(x) -> traffic(z), concert(y) -> traffic(z) = x and y are independent = given z, x and y are not independent\"\n",
        "\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "9b05f3b6-2969-47a6-dbf0-f785b7da848e"
      },
      "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 Size of distribution if n variables have domaon size d is d^n \n\nProbabilistic inference: split variables into evidence, query, and hidden variables. Evidence variables are the relevant variables used to find the wanted information.\nQuery variables are the actual variables we are looking for. Whe care about P(Query|Evidence1...EvidenceN\n\nLanguage models probability: Make markov assumption and only consider previous k words when prediction new word instead of considering all previous words in the sentence\n\nAfter building a bayes net you can group triplets into any of the three chains\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 Your summary here \n\n## Module 10 Definitions\n* **Marginal distribution** - a subtable of joint distributions\n* **Conditional distribution** - P(a|b) = P(a and b) / P(b)\n* **Causal chain** - low pressure(x) -> rain(y) -> traffic(z) =  P(x)P(y|x)P(z|y) = given y, the others are independent\n* **Common cause** - project due(y) -> piazza full(x), project due(y) -> OH full(z) = P(y)P(x|y)P(z|y) = given y, the others are independent\n* **Common effect** - raining(x) -> traffic(z), concert(y) -> traffic(z) = x and y are independent = given z, x and y are not independent\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 = \"\"\" Your summary here \"\"\"\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": 411
        },
        "cellView": "form",
        "outputId": "abe228d6-1ebc-46fb-d3fe-43ac23b891f3"
      },
      "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 Your summary here \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 = \"\"\" Machine learning. You label the data and then train your model. Your model will then know how to dwfine unlabeled data. Data\n",
        "is categorized based on a vector of features.\n",
        "\n",
        "Model based classification / Naive bayes classifier - Digit classification example: Build a model where both the label and features are random variables. Instantiate observed features. Query\n",
        "for the distribution of the label conditioned on the features. Assume all features are independent. Class lablel is the parent and it points towards\n",
        "all of its features. We basically use naive bays net to then determine the kabel of our input. \n",
        "\n",
        "ML concepts: (divide data into three data sets), (pick the feature-probability assignment that will maximize the likelyhood, aka product of all probabilities), (tuning), (try to add as\n",
        "many features as possible)\n",
        "   \"\"\"\n",
        "chapter19_2 = \"\"\" Perceptrons: Loosely inspired by a neuron. Multiple inputs with an associated wait and then based on the weighted sum the activation function will\n",
        "release it. Think of it as a decision maker that takes in a bunch of inputs as evidence and then makes a decision. (Cheese fesitval example). \n",
        "Activation function has a threshold, and you can add weights based in importance of inputs. We want ti learn the ideal weights for input featurs in the \n",
        "perceptron; input is going to be fixed but the weight vector must be changed. The weight vector will be multiplied by the input vector, the resulting vector is\n",
        "a decision boundary. Number of dimensions represent number of features. There is a bias term that allows us to move the decision vector around. We will learn \n",
        "weight through error driven learning: start with weight equal zero, for each instance if we guess correct, then there is no change in weights, else we adjust the\n",
        "weigh vector by adding or subracting the feature vector from the decision vector. In multiple dimensions, when incorrect you subtract from predicted and add to correct.\n",
        "\n",
        "\n",
        "Percepton properties: the decision boundary could be perfectly seperable or it might have mistakes which lead to a mistake bound. When the data is non separable\n",
        "you might want to modify the input data (like x^2) so that you could separate it there. There are also better seperating hyperplanes, best hyperplanes maximize \n",
        "distance between two class points created theough a support vector machine where two additional vectors are created and we want to \n",
        "maximize the margin between the two other lines. There can also be overtraining.   \"\"\"\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",
        "\"concept\" : \"definition\", \n",
        "\"overfitting\" : \"sticking too strictly with the label sets, happens when you assign zero probability to a feature-label pair\",\n",
        "\"smooth or regularize\" : \"laplace smoothing or interpolation\",\n",
        "\"laplace smoothing\" : \"pretend you saw every feature one more time (or k more times) than what you actually did\",\n",
        "\"inteprolation\" : \"P(x|y) = aP(x|y)+(1-a)P(x), to prevent when the conditional has never been seen \",\n",
        "\"tuning\" : \"after training the model with the training set, we have to tune the hyperparameters such as a or k in the held-out data, to then test\",\n",
        "\"baseline\" : \"have a baseline to be able to compare how your model works compared to the previous system\",\n",
        "\"MIRA\" : \"Choose the smallest update size that fixes the current mistake\",\n",
        "\n",
        "\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": "64afe8b2-599d-48a4-a701-49de47e0f651"
      },
      "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": "## WARNING: Your definition of 'tuning' is too long!\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Machine learning. You label the data and then train your model. Your model will then know how to dwfine unlabeled data. Data\nis categorized based on a vector of features.\n\nModel based classification / Naive bayes classifier - Digit classification example: Build a model where both the label and features are random variables. Instantiate observed features. Query\nfor the distribution of the label conditioned on the features. Assume all features are independent. Class lablel is the parent and it points towards\nall of its features. We basically use naive bays net to then determine the kabel of our input. \n\nML concepts: (divide data into three data sets), (pick the feature-probability assignment that will maximize the likelyhood, aka product of all probabilities), (tuning), (try to add as\nmany features as possible)\n   \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Perceptrons: Loosely inspired by a neuron. Multiple inputs with an associated wait and then based on the weighted sum the activation function will\nrelease it. Think of it as a decision maker that takes in a bunch of inputs as evidence and then makes a decision. (Cheese fesitval example). \nActivation function has a threshold, and you can add weights based in importance of inputs. We want ti learn the ideal weights for input featurs in the \nperceptron; input is going to be fixed but the weight vector must be changed. The weight vector will be multiplied by the input vector, the resulting vector is\na decision boundary. Number of dimensions represent number of features. There is a bias term that allows us to move the decision vector around. We will learn \nweight through error driven learning: start with weight equal zero, for each instance if we guess correct, then there is no change in weights, else we adjust the\nweigh vector by adding or subracting the feature vector from the decision vector. In multiple dimensions, when incorrect you subtract from predicted and add to correct.\n\n\nPercepton properties: the decision boundary could be perfectly seperable or it might have mistakes which lead to a mistake bound. When the data is non separable\nyou might want to modify the input data (like x^2) so that you could separate it there. There are also better seperating hyperplanes, best hyperplanes maximize \ndistance between two class points. There can also be overtraining.   \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* **Concept** - definition\n* **Overfitting** - sticking too strictly with the label sets, happens when you assign zero probability to a feature-label pair\n* **Smooth or regularize** - laplace smoothing or interpolation\n* **Laplace smoothing** - pretend you saw every feature one more time (or k more times) than what you actually did\n* **Inteprolation** - P(x|y) = aP(x|y)+(1-a)P(x), to prevent when the conditional has never been seen \n* **Tuning** - after training the model with the training set, we have to tune the hyperparameters such as a or k in the held-out data, to then test\n* **Baseline** - have a baseline to be able to compare how your model works compared to the previous system\n* **Mira** - Choose the smallest update size that fixes the current mistake\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 = \"\"\" Neural networks\n",
        "\n",
        "Logistic regression: \n",
        "\n",
        "Classifier components: feature representation, classification function, objective function, optimizing. Use sigmoid function of the weight-feature dot product. 50% is\n",
        "the decisioin boundary. Similar to perceptrons, multiple features and we must to shoot a result based on their values and weights. Movie example. Learn weights through \n",
        "a loss function thaht tells us the difference between the system output and goal output. We will choose gradient descent to minimize the loss function.\n",
        "\n",
        "Loss function: Goal is to determine how close the predicted output is from the expected output. One example is mean squared error. Cross entropy loss:  \"\"\"\n",
        "\n",
        "\n",
        "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "3c28d8a7-3f13-426e-c8d6-838f3e53b60d"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module13_sections = [\n",
        "  (\"SLP Chapter 5.1 Classification: the sigmoid\", slp_chapter5_1),\n",
        "  (\"SLP Chapter 5.2 Learning in Logistic Regression\", slp_chapter5_2),\n",
        "  (\"SLP Chapter 5.3 The cross-entropy loss function\", slp_chapter5_3),\n",
        "  (\"SLP Chapter 5.4 Gradient Descent\", slp_chapter5_4),\n",
        "  (\"SLP Chapter 5.5 Regularization\", slp_chapter5_5),\n",
        "  (\"SLP Chapter 5.6 Multinomial logistic regression\", slp_chapter5_6),\n",
        "\n",
        "  (\"SLP Chapter 7.1 Units\", slp_chapter7_1),\n",
        "  (\"SLP Chapter 7.2 The XOR problem\", slp_chapter7_2),\n",
        "  (\"SLP Chapter 7.3 Feedforward Neural Networks\", slp_chapter7_3),\n",
        "  (\"SLP Chapter 7.4 Feedforward networks for NLP: Classification\", slp_chapter7_4),\n",
        "  (\"SLP Chapter 7.5 Feedforward Neural Language Modeling\", slp_chapter7_5),\n",
        "  (\"SLP Chapter 7.6 Training Neural Nets\", slp_chapter7_6),\n",
        "  (\"SLP Chapter 7.7 Training the neural language model\", slp_chapter7_7),\n",
        "  (\"Module 13 Lecture Notes\", module13_lecture_notes),\n",
        "]\n",
        "\n",
        "module13_markdown = format_module(13, \"Neural Networks\", \n",
        "                         module13_sections, \n",
        "                         module13_definitions)\n",
        "display(Markdown(module13_markdown))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n Your summary here \n\n## Module 13 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 6 “Vector Semantics and Embeddings” (6.1-6.4, 6.8, 6.10-6.12)\n",
        "\n",
        "slp_chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "f2b7e276-5220-4fca-9502-126abadd7754"
      },
      "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": "194a04c1-122c-419d-8405-77f51703402b"
      },
      "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": "## WARNING: Your definition of 'continuity' is too long!\nWARNING: Your definition of 'monotonicity' is too long!\nWARNING: Your definition of 'unbiased' is too long!\n# 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 Axioms of utility theory will be followed by a rational agent. Markovw models dont care about past. Score of an agent is the sum of all the sequential scores. Discounting is a punishmnt for many steps, values\nof further rewards decay exponentially, this will bound our rewards. Deal with infinity by setting max number of steps or discounting with gamma or guarantee an absorbing - terminal state\n\nACTIONS ARE EASIER TO CHOOSE FROM VALUES THAN VICE VERSA\n\n\nPolicy iteration: Faster than value iteration (where actions converge much faster than the values) and it is optimal. We are given a fixed policy and then we try to improve it \n\n## AIMA Chapter 16.3 Utility Functions\n We set utility function by fixing the utilities of any perticular outcome (notmalize utilities use 1 for success, 0 for fail) \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n There is a reward in each state. \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Value itaration calculated a an optimal policy. Utility of a state is the states reward + expected utility of its neigbors.\nBellman update will reach equilibrium.\n\nValue iteration algo:\nComplexity O(s^2A), it ia guaranteed to converge \n\n## Module 8 Lecture Notes\n Your summary here \n\n## Module 8 Definitions\n* **Decision theory** - deals with choosing among actions based on the desirability of their immediate outcomes\n* **Utility function** - assigns a single number to express the desirability of a state.\n* **Lottery** - an action is a tiquet and lotery L has different outcomes s1...sn. L = [p1,S1;...;pn,Sn]\n* **Orderability** - given two lotteries an agent must prefer one or the other, or state both as equal (A>B, B>A, A==B)\n* **Transitivity** - given three loteries. If A > C and C > B, then A > B\n* **Continuity** - If some lottery B is between A and C in preference, then there is some probability p for which the rational agent will be indifferent between getting B for sure and the lottery that yields A with probability p and C with probability 1 − p.\n* **Sustainability** - A ∼ B ⇒ [p, A; 1 − p, C] ∼ [p, B; 1 − p, C]\n* **Monotonicity** - Suppose two lotteries have the same two possible outcomes, A and B. If an agent prefers A to B, then the agent must prefer the lottery that has a higher probability for A (and vice versa). A ' B ⇒ (p>q ⇔ [p, A; 1 − p, B] ' [q, A; 1 − q,B])\n* **Preference elicitation** - involves presenting choices to the agent and using the observes preferences to determine the utility function\n* **Micromort** - a one in a million chance of death\n* **Monotonic preference** - agent prefers more money than less\n* **Unbiased** - the expected value of the error, E(EU!(a|e) − EU (a|e))), is zero, which means it makes sense to choose the action with the higest expected utility\n* **Normative theory** - describes how rational agents should act\n* **Stochaistic dominance** - probability dominance of outcomes\n* **Transition model** - P(s' | s, a)\n* **Policy** - A solution, pi(s) is the action recommended by the policy at state s\n* **Optimal policy** - highesr expected utility\n* **Discount factor** - between 0 and 1 describes prefernece of current over future rewards\n* **Proper policy** - guaranteed to reach a final state\n* **Contraction** - everytime you apply a change to 2 values, they come closer together\n* **Stationary preferences** - Sequence preferences do not vary if a constant is added to both sequences\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n We do not know the probability distributions.\n\nThe world is till govened by an MSP but we must find the policy\n\nModel based learning: Through experiences build an empirical MDP model and then solve the model. We basically have training episodes\n\nModel-free learning: Dont know the transitions or rewards functions. We have a given policy and want to determine the state values.\n\nDirect evaluation (model free): Follow the policy multiple times, in each find the total value of being in a state (add rewards until the end) and avg out those to get the state values \n\nTemporal diff learning: more common states will be updated more often. update value of state each time you see it (v(s) -> (1-a)V(s) + a(sample)). More recent samples are gonna be woth more\n\nQ-learning: update q values everytime you see them ilimar to temp diff learning, there is convergance. Choose random actions\n\nExploration vs exploitation: in q-learning you can use the epsilon greeding algorithm where if a random number is smaller than epsilon, then act randomly, else act on current policy. Lower epsilon over time\n\nExploration function: f(utillity, num visited) = u + k/n, where good states are going to be visited more often \n\nRegret: measure how many mistakes we made over time. Regret is the difference between the optimal policy and our original policy. Minimizing regret meanis you are learning optimally\n\nApproximate q-learning: Sometimes there are TOO many states, so we need to generalize similar states. Represent states with multiple features. In each iteration update each individual weight of a feature.\nIf something unexpected goes on, blame the features that were active. Disprefer all states with that states features \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 Your summary here \n\n## Module 9 Definitions\n* **Exploration** - you have to try unknown actions to get info\n* **Exploitation** - eventually, you have to use what you know\n* **Regret** - even if you learn intelligently, you make mistakes\n* **Sampling** - because of chance, you have to try things repeatedly\n* **Difficulty** - learning can be much harder than solving a known MDP\n* **Passive learning** - we let someone else choose the policy and then we follow it\n* **Active learning** - learning makes the action choices\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Size of distribution if n variables have domaon size d is d^n \n\nProbabilistic inference: split variables into evidence, query, and hidden variables. Evidence variables are the relevant variables used to find the wanted information.\nQuery variables are the actual variables we are looking for. Whe care about P(Query|Evidence1...EvidenceN\n\nLanguage models probability: Make markov assumption and only consider previous k words when prediction new word instead of considering all previous words in the sentence\n\nAfter building a bayes net you can group triplets into any of the three chains\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 Your summary here \n\n## Module 10 Definitions\n* **Marginal distribution** - a subtable of joint distributions\n* **Conditional distribution** - P(a|b) = P(a and b) / P(b)\n* **Causal chain** - low pressure(x) -> rain(y) -> traffic(z) =  P(x)P(y|x)P(z|y) = given y, the others are independent\n* **Common cause** - project due(y) -> piazza full(x), project due(y) -> OH full(z) = P(y)P(x|y)P(z|y) = given y, the others are independent\n* **Common effect** - raining(x) -> traffic(z), concert(y) -> traffic(z) = x and y are independent = given z, x and y are not independent\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 Your summary here \n\n## Module 11 Definitions\n* **Concept** - definition\n\n\n## WARNING: Your definition of 'tuning' is too long!\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Machine learning. You label the data and then train your model. Your model will then know how to dwfine unlabeled data. Data\nis categorized based on a vector of features.\n\nModel based classification / Naive bayes classifier - Digit classification example: Build a model where both the label and features are random variables. Instantiate observed features. Query\nfor the distribution of the label conditioned on the features. Assume all features are independent. Class lablel is the parent and it points towards\nall of its features. We basically use naive bays net to then determine the kabel of our input. \n\nML concepts: (divide data into three data sets), (pick the feature-probability assignment that will maximize the likelyhood, aka product of all probabilities), (tuning), (try to add as\nmany features as possible)\n   \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Perceptrons: Loosely inspired by a neuron. Multiple inputs with an associated wait and then based on the weighted sum the activation function will\nrelease it. Think of it as a decision maker that takes in a bunch of inputs as evidence and then makes a decision. (Cheese fesitval example). \nActivation function has a threshold, and you can add weights based in importance of inputs. We want ti learn the ideal weights for input featurs in the \nperceptron; input is going to be fixed but the weight vector must be changed. The weight vector will be multiplied by the input vector, the resulting vector is\na decision boundary. Number of dimensions represent number of features. There is a bias term that allows us to move the decision vector around. We will learn \nweight through error driven learning: start with weight equal zero, for each instance if we guess correct, then there is no change in weights, else we adjust the\nweigh vector by adding or subracting the feature vector from the decision vector. In multiple dimensions, when incorrect you subtract from predicted and add to correct.\n\n\nPercepton properties: the decision boundary could be perfectly seperable or it might have mistakes which lead to a mistake bound. When the data is non separable\nyou might want to modify the input data (like x^2) so that you could separate it there. There are also better seperating hyperplanes, best hyperplanes maximize \ndistance between two class points. There can also be overtraining.   \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* **Concept** - definition\n* **Overfitting** - sticking too strictly with the label sets, happens when you assign zero probability to a feature-label pair\n* **Smooth or regularize** - laplace smoothing or interpolation\n* **Laplace smoothing** - pretend you saw every feature one more time (or k more times) than what you actually did\n* **Inteprolation** - P(x|y) = aP(x|y)+(1-a)P(x), to prevent when the conditional has never been seen \n* **Tuning** - after training the model with the training set, we have to tune the hyperparameters such as a or k in the held-out data, to then test\n* **Baseline** - have a baseline to be able to compare how your model works compared to the previous system\n* **Mira** - Choose the smallest update size that fixes the current mistake\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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