{
  "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": "1741a5c0-0e6e-486c-8fe0-d4c092fcda99"
      },
      "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": null,
      "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": "c9271507-44c9-4759-8e43-ef9820a3d359"
      },
      "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": null,
      "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": null,
      "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",
        "equations16_1 = r\"\"\" \n",
        "$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \\\n",
        "$EU(a) = \\sum_{s'} P(\\textnormal{RESULT}(a)=s')U(s')$\\\n",
        "$action = argmax_aEU(a)$ \n",
        "\"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" \\\n",
        "Maximum Expected Utility Principle \\\n",
        "\"\"\"  + equations16_1\n",
        "\n",
        "equations16_2 = r\"\"\" \n",
        "$L = [p_i, S_i]$ \\\n",
        "$EU(L) = \\sum_i p_iU(S_i)$\n",
        "\"\"\"\n",
        "\n",
        "chapter16_2 =  \"\"\"\\\n",
        "Axioms of Utility Theory\n",
        "Orderability - (A > B), (B > A) or (A = B) \\\n",
        "Transitivity - (A > B) ^ (B > C) -> (A > C) \\\n",
        "Continuity - (A > B > C) -> Exists p [p, A; 1 - p; C] = B \\\n",
        "Substitutability - A = B -> [p, A; 1 - p, C] = [p, B; 1 - p, C] \\\n",
        "Monotonicity - A > B -> (p > q <-> [p, A; 1 - p, B] > [q, A; 1 - q, B]) \\\n",
        "Decomposibility - [p, A; 1 - p, [q, B; 1 - q, C]] = [p, A; (1-p)q, B; (1-p)(1-q), C] \n",
        "\"\"\" + equations16_2\n",
        "\n",
        "chapter16_3 =  \"\"\" \\\n",
        "preference elicitation - giving choices to humans and based on preferences, finding utility function \\\n",
        "US gov. agencies use value of a statistical life to determine cost/benefit of policies.\\\n",
        "micromort - 1/million chance of death\\\n",
        "QALY - quality-adjusted life year \\\n",
        "Concave - risk-averse, Convex - risk-seeking, Linear - risk-neutral \\\n",
        "certainty effect - people attracted to certain gains \\\n",
        "framing effect - wording of decision problem impacts choice \\\n",
        "ambiguity aversion - people choice known probabilities \\\n",
        "anchoring effect - relative judgements of utility \n",
        "\"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "\n",
        "equations17_1 = r\"\"\" \n",
        "$U^\\pi(s) = E[\\sum_{t=0}^\\infty \\gamma^tR(S_t, \\pi(S_t), S_{t+1})]$ \\\n",
        "$\\pi_s* = argmax_\\pi U^\\pi (s)$ \\\n",
        "$\\pi*(s) = argmax_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\n",
        "Bellman: $U(s) = max_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\n",
        "Action-utility function: $Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$\\\n",
        "$U(s) = max_a Q(s, a)$ \\\n",
        "$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$\\\n",
        "\"\"\"\n",
        "\n",
        "chapter17_1 = \"\"\" \\\n",
        "fully observable = perfect information \\\n",
        "transition model - describes probability of outcome of each action for every state \\ \n",
        "environment history - sequence of states and actions \\\n",
        "Markov Decision Process(MDP) contains set of states, actions of each state, transition model, reward function. \\\n",
        "DP used to solve MDP \\\n",
        "Solution for MDP is a policy pi that specifies what action pi(s) agent should take at any state \\\n",
        "Quality of policy measured by expected utility of possible environment histories generated by policy \\\n",
        "Optimal policy pi* has highest expected utility \\\n",
        "finite horizon - there exists a fixed time t after which nothing matters \\\n",
        "policy that depends on time is nonstationary \\\n",
        "optimal policy is stationary  \\\n",
        "policy that is guaranteed to reach terminal is a proper policy \\\n",
        "Can compare infinite sequences using average reward per action \n",
        "\"\"\"  + equations17_1\n",
        "\n",
        "\n",
        "chapter17_2 =  \"\"\"\n",
        "Value Iteration - Initliaze utilities for states to 0, use Bellman equation to update utility for every state, repeat until convergence \\\n",
        "Can obtain an optimal policy even when utility function is inaccurate \\\n",
        "O(S^2A), guaranteed to converge to unique optimal values \\\n",
        "Policy Iteration - Initialize policy arbitrarily, keep iterating utilities using policy with simplified Bellman until convergence and then update policy \\\n",
        "faster than value iteration (policy converges before values) \\\n",
        "Linear Program - minimize U(s) >= simplified Bellmans \\\n",
        "LP rarely more efficient than DP for solving MDP\n",
        "\"\"\"\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",
        "\"utility function\" : \"Your definition here\",\n",
        "\"expected utility\" : \"your definition here\",\n",
        "\"etc.\": \"your definition here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9b5nRLu0bTvF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lm0F09QeY4Di"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "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": "f0f0cc44-d14b-4547-dce1-bd009654e750"
      },
      "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": null,
      "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 Maximum Expected Utility Principle  \n$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \\\n$EU(a) = \\sum_{s'} P(\\textnormal{RESULT}(a)=s')U(s')$\\\n$action = argmax_aEU(a)$ \n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nAxioms of Utility Theory\nOrderability - (A > B), (B > A) or (A = B) Transitivity - (A > B) ^ (B > C) -> (A > C) Continuity - (A > B > C) -> Exists p [p, A; 1 - p; C] = B Substitutability - A = B -> [p, A; 1 - p, C] = [p, B; 1 - p, C] Monotonicity - A > B -> (p > q <-> [p, A; 1 - p, B] > [q, A; 1 - q, B]) Decomposibility - [p, A; 1 - p, [q, B; 1 - q, C]] = [p, A; (1-p)q, B; (1-p)(1-q), C] \n \n$L = [p_i, S_i]$ \\\n$EU(L) = \\sum_i p_iU(S_i)$\n\n\n## AIMA Chapter 16.3 Utility Functions\n preference elicitation - giving choices to humans and based on preferences, finding utility function US gov. agencies use value of a statistical life to determine cost/benefit of policies.micromort - 1/million chance of deathQALY - quality-adjusted life year Concave - risk-averse, Convex - risk-seeking, Linear - risk-neutral certainty effect - people attracted to certain gains framing effect - wording of decision problem impacts choice ambiguity aversion - people choice known probabilities anchoring effect - relative judgements of utility \n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n fully observable = perfect information transition model - describes probability of outcome of each action for every state \\ \nenvironment history - sequence of states and actions Markov Decision Process(MDP) contains set of states, actions of each state, transition model, reward function. DP used to solve MDP Solution for MDP is a policy pi that specifies what action pi(s) agent should take at any state Quality of policy measured by expected utility of possible environment histories generated by policy Optimal policy pi* has highest expected utility finite horizon - there exists a fixed time t after which nothing matters policy that depends on time is nonstationary optimal policy is stationary  policy that is guaranteed to reach terminal is a proper policy Can compare infinite sequences using average reward per action \n \n$U^\\pi(s) = E[\\sum_{t=0}^\\infty \\gamma^tR(S_t, \\pi(S_t), S_{t+1})]$ \\\n$\\pi_s* = argmax_\\pi U^\\pi (s)$ \\\n$\\pi*(s) = argmax_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\nBellman: $U(s) = max_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\nAction-utility function: $Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$\\\n$U(s) = max_a Q(s, a)$ \\\n$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$\\\n\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n\nValue Iteration - Initliaze utilities for states to 0, use Bellman equation to update utility for every state, repeat until convergence Can obtain an optimal policy even when utility function is inaccurate O(S^2A), guaranteed to converge to unique optimal values Policy Iteration - Initialize policy arbitrarily, keep iterating utilities using policy with simplified Bellman until convergence and then update policy faster than value iteration (policy converges before values) Linear Program - minimize U(s) >= simplified Bellmans LP rarely more efficient than DP for solving MDP\n\n\n## Module 8 Lecture Notes\n Your summary here \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 9 - Reinforcement Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# Russell and Norvig, AIMA Section 17.3 “Bandit Problems”\n",
        "\n",
        "equations17_3 = r\"\"\" \n",
        "$\\lambda = max_{T > 0} \\frac{E(\\sum_{t=0}^{T-1} \\gamma^tR_t)}{E(\\sum_{t=0}^{T-1} \\gamma^t)}$\n",
        "\"\"\"\n",
        "chapter17_3 =  \"\"\" \n",
        "n-armed bandit - slot machine with n arms/levers where each lever is a unknown fixed probabiltiy distribution\n",
        "of winnings \\\n",
        "exploitation-exploration tradeoff - exploitation of current known best action for rewards vs.\n",
        "exploration of unknown states/actions to gain info and possibly find better rewards \\\n",
        "\"\"\" + equations17_3\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\"\n",
        "In Reinforcement Learning, agent is not given MDP, agent is in MDP\\\n",
        "model-based reinforcement learning - agent uses transition model to determine rewards and decisions. if model\n",
        "is unknown, agent can learn model. \\\n",
        "model-free reinforcement learning - agent doesnt know and doesnt learn model. Instead it either does\n",
        "action-utility learning or policy search. \\\n",
        "Action-utility learning - most common is Q-learning where agent learns Q-function \\\n",
        "Policy Search - reflex agent learns a policy \\\n",
        "passive reinforcement learning - polciy is fixed and agent learns utilities of states\\\n",
        "\"\"\"\n",
        "chapter22_2 =  \"\"\" \n",
        "passive reinforecment learning - fully observable, fixed policy, agent learning utility function,\n",
        "similar to policy evaluation but agent doesnt know transition model nor reward function, agent executes\n",
        "set of trials using policy, in each trial, agent transitions until terminal\\\n",
        "direct utility estimation - utility of state is expected total reward from that state onward (aka reward-to-go)\\\n",
        "as trials approaches infinity, the utility estimation will approach true expectation \\\n",
        "however converges slowly, wastes info about state connections\\\n",
        "adaptive dynamic programming learns the transition model that connects states and solving MDP using DP.\\\n",
        "Modified policy iteration uses simplified value iteration to update estimated utilities, converges quickly.\\\n",
        "temporal-difference learning - fixex policy, update utilities of state every transition based on learning rate a \\\n",
        "V(s) = (1 - a)V(s) + a(reward + gamma * utility(s')) \\\n",
        "problem - dont learn values, not q-values \\\n",
        " \"\"\"\n",
        "\n",
        "equations22_3 = r\"\"\" \n",
        "$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$ \\\n",
        "$Q(s, a) = (1 - a)Q(s, a) + a(R(s, a, s') + \\gamma max_{a'} Q(s', a'))$\n",
        "\"\"\"\n",
        "\n",
        "chapter22_3 =  \"\"\" \n",
        "active temporal difference learning - learning action-utility function instead of just utility function\\\n",
        "q-learning converges to optimal policy even if acting suboptimally - off policy learning \\\n",
        "epsilon-greedy - if random number less than episolon choose random action, otherwise use policy for best action\\\n",
        "N(s, a) low -> curiosity \\\n",
        "\"\"\" + equations22_3\n",
        "\n",
        "equations22_4 = r\"\"\" \n",
        "$Q(s, a) = w_1f_1(s,a) + w_2f_2(s,a) + ... + w_nf_n(s,a)$ \\\n",
        "$\\delta = reward + \\gamma max_{a'}Q(s', a') - Q(s, a)$ \\\n",
        "$w_i = w_i + \\alpha * \\delta  * f_i(s, a)$\n",
        "\"\"\"\n",
        "\n",
        "chapter22_4 =  \"\"\" \n",
        "approx q-learning - too many states to learn q-values \n",
        "\"\"\" + equations22_4\n",
        "chapter22_5 =  \"\"\"\n",
        "policy search - keep tweaking policy while performance improves\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "8403f876-ec86-49fa-c54e-d19e0b007fc7"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n \nn-armed bandit - slot machine with n arms/levers where each lever is a unknown fixed probabiltiy distribution\nof winnings exploitation-exploration tradeoff - exploitation of current known best action for rewards vs.\nexploration of unknown states/actions to gain info and possibly find better rewards  \n$\\lambda = max_{T > 0} \\frac{E(\\sum_{t=0}^{T-1} \\gamma^tR_t)}{E(\\sum_{t=0}^{T-1} \\gamma^t)}$\n\n\n## AIMA Chapter 22.1 Learning from Rewards\n\nIn Reinforcement Learning, agent is not given MDP, agent is in MDPmodel-based reinforcement learning - agent uses transition model to determine rewards and decisions. if model\nis unknown, agent can learn model. model-free reinforcement learning - agent doesnt know and doesnt learn model. Instead it either does\naction-utility learning or policy search. Action-utility learning - most common is Q-learning where agent learns Q-function Policy Search - reflex agent learns a policy passive reinforcement learning - polciy is fixed and agent learns utilities of states\n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n \npassive reinforecment learning - fully observable, fixed policy, agent learning utility function,\nsimilar to policy evaluation but agent doesnt know transition model nor reward function, agent executes\nset of trials using policy, in each trial, agent transitions until terminaldirect utility estimation - utility of state is expected total reward from that state onward (aka reward-to-go)as trials approaches infinity, the utility estimation will approach true expectation however converges slowly, wastes info about state connectionsadaptive dynamic programming learns the transition model that connects states and solving MDP using DP.Modified policy iteration uses simplified value iteration to update estimated utilities, converges quickly.temporal-difference learning - fixex policy, update utilities of state every transition based on learning rate a V(s) = (1 - a)V(s) + a(reward + gamma * utility(s')) problem - dont learn values, not q-values  \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n \nactive temporal difference learning - learning action-utility function instead of just utility functionq-learning converges to optimal policy even if acting suboptimally - off policy learning epsilon-greedy - if random number less than episolon choose random action, otherwise use policy for best actionN(s, a) low -> curiosity  \n$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$ \\\n$Q(s, a) = (1 - a)Q(s, a) + a(R(s, a, s') + \\gamma max_{a'} Q(s', a'))$\n\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n \napprox q-learning - too many states to learn q-values \n \n$Q(s, a) = w_1f_1(s,a) + w_2f_2(s,a) + ... + w_nf_n(s,a)$ \\\n$\\delta = reward + \\gamma max_{a'}Q(s', a') - Q(s, a)$ \\\n$w_i = w_i + \\alpha * \\delta  * f_i(s, a)$\n\n\n## Module 9 Lecture Notes\n Your summary here \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",
        "\n",
        "equations12_1 = r\"\"\"\n",
        "$P(x | y) = \\frac{P(x)P(y | x)}{P(y)}$ \\\n",
        "Independence: $P(x, y) = P(x) * P(y) $ \\\n",
        "Conditional Independence: $P(x,y |z) = P(x |z) * P(y |z)$ \\\n",
        "Conditional Independence: $P(x|z,y) = P(x |z) $\\\n",
        "Conditional Independence: $P(x|z,y) = P(x |y) $\\\n",
        "PIE: $P(x \\lor y) = P(x) + P(y) - P(x \\land y)$\n",
        "\"\"\"\n",
        "chapter12_1 = \"\"\" Your summary here \"\"\" + equations12_1\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",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "1d38ba7e-9cea-4499-a022-f188765363a1"
      },
      "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": null,
      "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$P(x | y) = \\frac{P(x)P(y | x)}{P(y)}$ \\\nIndependence: $P(x, y) = P(x) * P(y) $ \\\nConditional Independence: $P(x,y |z) = P(x |z) * P(y |z)$ \\\nConditional Independence: $P(x|z,y) = P(x |z) $\\\nConditional Independence: $P(x|z,y) = P(x |y) $\\\nPIE: $P(x \\lor y) = P(x) + P(y) - P(x \\land y)$\n\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* **Term** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, Russell and Norvig, AIMA Chapter 13 “Probabilistic Reasoning” (13.1, 13.2, 13.3)\n",
        "chapter13_1 = \"\"\"\n",
        "D-Separation \\\n",
        " \"\"\"\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "cellView": "form",
        "outputId": "1504c89e-afc0-4f2d-e2e2-80f418669b6b"
      },
      "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": null,
      "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\nD-Separation  \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 = \"\"\" 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 = \"\"\"\n",
        "Properties of perceptrons \\\n",
        "separability - true if possble to separate data\\\n",
        "convergence \\\n",
        "mistake bound - max # mistakes related to margin of separability \\\n",
        "mistakes < k/(gamma^2) \\ \n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "fad12a4b-0003-4636-9577-20629dc29675"
      },
      "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": null,
      "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\nProperties of perceptrons separability - true if possble to separate dataconvergence mistake bound - max # mistakes related to margin of separability mistakes < k/(gamma^2) \\ \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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "f9a0eeac-c715-4159-9557-d290ee4af890"
      },
      "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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "dd905651-2b22-44ac-bccd-abe278c877d2"
      },
      "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": null,
      "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": "40a0b8d8-73de-4519-e2a0-a00d5c1ae744"
      },
      "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": null,
      "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 Maximum Expected Utility Principle  \n$P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \\\n$EU(a) = \\sum_{s'} P(\\textnormal{RESULT}(a)=s')U(s')$\\\n$action = argmax_aEU(a)$ \n\n\n## AIMA Chapter 16.2 The Basis of Utility Theory\nAxioms of Utility Theory\nOrderability - (A > B), (B > A) or (A = B) Transitivity - (A > B) ^ (B > C) -> (A > C) Continuity - (A > B > C) -> Exists p [p, A; 1 - p; C] = B Substitutability - A = B -> [p, A; 1 - p, C] = [p, B; 1 - p, C] Monotonicity - A > B -> (p > q <-> [p, A; 1 - p, B] > [q, A; 1 - q, B]) Decomposibility - [p, A; 1 - p, [q, B; 1 - q, C]] = [p, A; (1-p)q, B; (1-p)(1-q), C] \n \n$L = [p_i, S_i]$ \\\n$EU(L) = \\sum_i p_iU(S_i)$\n\n\n## AIMA Chapter 16.3 Utility Functions\n preference elicitation - giving choices to humans and based on preferences, finding utility function US gov. agencies use value of a statistical life to determine cost/benefit of policies.micromort - 1/million chance of deathQALY - quality-adjusted life year Concave - risk-averse, Convex - risk-seeking, Linear - risk-neutral certainty effect - people attracted to certain gains framing effect - wording of decision problem impacts choice ambiguity aversion - people choice known probabilities anchoring effect - relative judgements of utility \n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n fully observable = perfect information transition model - describes probability of outcome of each action for every state \\ \nenvironment history - sequence of states and actions Markov Decision Process(MDP) contains set of states, actions of each state, transition model, reward function. DP used to solve MDP Solution for MDP is a policy pi that specifies what action pi(s) agent should take at any state Quality of policy measured by expected utility of possible environment histories generated by policy Optimal policy pi* has highest expected utility finite horizon - there exists a fixed time t after which nothing matters policy that depends on time is nonstationary optimal policy is stationary  policy that is guaranteed to reach terminal is a proper policy Can compare infinite sequences using average reward per action \n \n$U^\\pi(s) = E[\\sum_{t=0}^\\infty \\gamma^tR(S_t, \\pi(S_t), S_{t+1})]$ \\\n$\\pi_s* = argmax_\\pi U^\\pi (s)$ \\\n$\\pi*(s) = argmax_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\nBellman: $U(s) = max_{a \\in A(s)} \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$ \\\nAction-utility function: $Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma U(s')]$\\\n$U(s) = max_a Q(s, a)$ \\\n$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$\\\n\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n\nValue Iteration - Initliaze utilities for states to 0, use Bellman equation to update utility for every state, repeat until convergence Can obtain an optimal policy even when utility function is inaccurate O(S^2A), guaranteed to converge to unique optimal values Policy Iteration - Initialize policy arbitrarily, keep iterating utilities using policy with simplified Bellman until convergence and then update policy faster than value iteration (policy converges before values) Linear Program - minimize U(s) >= simplified Bellmans LP rarely more efficient than DP for solving MDP\n\n\n## Module 8 Lecture Notes\n Your summary here \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n \nn-armed bandit - slot machine with n arms/levers where each lever is a unknown fixed probabiltiy distribution\nof winnings exploitation-exploration tradeoff - exploitation of current known best action for rewards vs.\nexploration of unknown states/actions to gain info and possibly find better rewards  \n$\\lambda = max_{T > 0} \\frac{E(\\sum_{t=0}^{T-1} \\gamma^tR_t)}{E(\\sum_{t=0}^{T-1} \\gamma^t)}$\n\n\n## AIMA Chapter 22.1 Learning from Rewards\n\nIn Reinforcement Learning, agent is not given MDP, agent is in MDPmodel-based reinforcement learning - agent uses transition model to determine rewards and decisions. if model\nis unknown, agent can learn model. model-free reinforcement learning - agent doesnt know and doesnt learn model. Instead it either does\naction-utility learning or policy search. Action-utility learning - most common is Q-learning where agent learns Q-function Policy Search - reflex agent learns a policy passive reinforcement learning - polciy is fixed and agent learns utilities of states\n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n \npassive reinforecment learning - fully observable, fixed policy, agent learning utility function,\nsimilar to policy evaluation but agent doesnt know transition model nor reward function, agent executes\nset of trials using policy, in each trial, agent transitions until terminaldirect utility estimation - utility of state is expected total reward from that state onward (aka reward-to-go)as trials approaches infinity, the utility estimation will approach true expectation however converges slowly, wastes info about state connectionsadaptive dynamic programming learns the transition model that connects states and solving MDP using DP.Modified policy iteration uses simplified value iteration to update estimated utilities, converges quickly.temporal-difference learning - fixex policy, update utilities of state every transition based on learning rate a V(s) = (1 - a)V(s) + a(reward + gamma * utility(s')) problem - dont learn values, not q-values  \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n \nactive temporal difference learning - learning action-utility function instead of just utility functionq-learning converges to optimal policy even if acting suboptimally - off policy learning epsilon-greedy - if random number less than episolon choose random action, otherwise use policy for best actionN(s, a) low -> curiosity  \n$Q(s, a) = \\sum_{s'}P(s'|s, a)[R(s, a, s') + \\gamma max_{a'} Q(s', a')]$ \\\n$Q(s, a) = (1 - a)Q(s, a) + a(R(s, a, s') + \\gamma max_{a'} Q(s', a'))$\n\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n \napprox q-learning - too many states to learn q-values \n \n$Q(s, a) = w_1f_1(s,a) + w_2f_2(s,a) + ... + w_nf_n(s,a)$ \\\n$\\delta = reward + \\gamma max_{a'}Q(s', a') - Q(s, a)$ \\\n$w_i = w_i + \\alpha * \\delta  * f_i(s, a)$\n\n\n## Module 9 Lecture Notes\n Your summary here \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$P(x | y) = \\frac{P(x)P(y | x)}{P(y)}$ \\\nIndependence: $P(x, y) = P(x) * P(y) $ \\\nConditional Independence: $P(x,y |z) = P(x |z) * P(y |z)$ \\\nConditional Independence: $P(x|z,y) = P(x |z) $\\\nConditional Independence: $P(x|z,y) = P(x |y) $\\\nPIE: $P(x \\lor y) = P(x) + P(y) - P(x \\land y)$\n\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* **Term** - your definition here\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n\nD-Separation  \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# 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\nProperties of perceptrons separability - true if possble to separate dataconvergence mistake bound - max # mistakes related to margin of separability mistakes < k/(gamma^2) \\ \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 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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