{
  "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": "30670dbd-bd37-4660-c9cb-30f014088dc4"
      },
      "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": "bcea6331-9dfc-4c3e-9943-0402fe87d451"
      },
      "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": 2,
      "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",
        "\n",
        "chapter16_1 = \"\"\" MEU principle: rational agent choose actions that maximizes its expected utility. Performance score measures history, utility function apply to the next state and guide actions step by step \"\"\"\n",
        "chapter16_2 =  \"\"\" A utility function exists for any rational agent but its not unique. \"\"\"\n",
        "chapter16_3 =  \"\"\"\"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" Fully observable, stochastic environment, Markovian transition model and additive reward make a sequential decision probelm MDP, consisting of \\\n",
        "a set of states, a set of actions in each state, a transition model, and a reward funciton. Solving MDP involves dynamic programming. Optimal policy yields highest EU. \\\n",
        "Infinite horizon doesn't imply infinite sequence.\"\"\"\n",
        "chapter17_2 =  \"\"\" Value Iteration, policy iteration, linear programming and Monte Carlo planning are 4 different ways to solve mdp. The first 3 are offline. \\\n",
        "policy iteration consists of policy evaluation and policy improvement.Linear programming seeks to minimize U(s) subject to the constraint of bellman equation.\n",
        " \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\" \n",
        "problems of value iteration:\n",
        "Problem 1:  O(S^2A) per iteration\n",
        "Problem 2: max rarely changes\n",
        "Problem 3: converges long before the values\\n\n",
        "Discount because sooner rewards matters more and it helps convergence\n",
        " \"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 8.\n",
        "# You can define any term in the textbook or lecture, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 25 words long.\n",
        "\n",
        "module8_definitions = {\n",
        "\"utility function\" : \"measures preference\",\n",
        "\"expected utility\" : \"probability weighted average of utility from outcomes\",\n",
        "\"value function/ordinal utility function\": \"rank of preference, not number\",\n",
        "\"orderability\":r\"\"\" $ A \\succ B, A \\prec B \\textnormal{or} A \\sim B$ \"\"\",\n",
        "\"transitivity\":r\"\"\" $ (A \\succ B) \\land (B \\succ C) \\implies A \\succ C$ \"\"\",\n",
        "\"continuity\":r\"\"\" $ A \\succ B \\succ C \\implies \\exist p, [p,A;1-p,C]\\sim B$ \"\"\",\n",
        "\"substitutability\": r\"\"\" $ A \\sim B \\implies [p,A;1-p, C] \\sim [p, B; 1-p, C] $ \"\"\",\n",
        "\"monotonicity\": r\"\"\" $ A \\succ B \\implies (p>q \\iff [p,A; 1-p, B] \\succ [q, A; 1-q, B] $ \"\"\",\n",
        "\"decomposability\": r\"\"\" $ [p,A; 1-p, [q,B; 1-q, C]] \\sim [p,A; (1-p)q,B; (1-p)(1-q), C] $ \"\"\", \n",
        "\"policy\":\"instructions for actions in any state the agent might reach\",\n",
        "\"finite horizon\": \"fixed number of action\",\n",
        "\"value iteration\":'simultaneously updates state values by bellman',\n",
        "\"policy iteration\":\"repeat same policy until convergence then improve\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 3,
      "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": 845
        },
        "cellView": "form",
        "outputId": "24a8a2ec-2b0d-40d4-a78a-27e00007f3f0"
      },
      "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": 4,
      "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 MEU principle: rational agent choose actions that maximizes its expected utility. Performance score measures history, utility function apply to the next state and guide actions step by step \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n A utility function exists for any rational agent but its not unique. \n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Fully observable, stochastic environment, Markovian transition model and additive reward make a sequential decision probelm MDP, consisting of a set of states, a set of actions in each state, a transition model, and a reward funciton. Solving MDP involves dynamic programming. Optimal policy yields highest EU. Infinite horizon doesn't imply infinite sequence.\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Value Iteration, policy iteration, linear programming and Monte Carlo planning are 4 different ways to solve mdp. The first 3 are offline. policy iteration consists of policy evaluation and policy improvement.Linear programming seeks to minimize U(s) subject to the constraint of bellman equation.\n \n\n## Module 8 Lecture Notes\n \nproblems of value iteration:\nProblem 1:  O(S^2A) per iteration\nProblem 2: max rarely changes\nProblem 3: converges long before the values\n\nDiscount because sooner rewards matters more and it helps convergence\n \n\n## Module 8 Definitions\n* **Utility function** - measures preference\n* **Expected utility** - probability weighted average of utility from outcomes\n* **Value function/ordinal utility function** - rank of preference, not number\n* **Orderability** -  $ A \\succ B, A \\prec B \\textnormal{or} A \\sim B$ \n* **Transitivity** -  $ (A \\succ B) \\land (B \\succ C) \\implies A \\succ C$ \n* **Continuity** -  $ A \\succ B \\succ C \\implies \\exist p, [p,A;1-p,C]\\sim B$ \n* **Substitutability** -  $ A \\sim B \\implies [p,A;1-p, C] \\sim [p, B; 1-p, C] $ \n* **Monotonicity** -  $ A \\succ B \\implies (p>q \\iff [p,A; 1-p, B] \\succ [q, A; 1-q, B] $ \n* **Decomposability** -  $ [p,A; 1-p, [q,B; 1-q, C]] \\sim [p,A; (1-p)q,B; (1-p)(1-q), C] $ \n* **Policy** - instructions for actions in any state the agent might reach\n* **Finite horizon** - fixed number of action\n* **Value iteration** - simultaneously updates state values by bellman\n* **Policy iteration** - repeat same policy until convergence then improve\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 =  \"\"\"  \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\" Agent must act to learn from environment in reinforcement learning. It has no prior knowledge of transition model and reward.\\\n",
        "Q-learning is a model free RL. It learns a quality function Q which sums the rewards from a state if taking action a. The agent tries to maximize q value.\\\n",
        "Another way is policy search. The agent is reflex agent that directly learns a map (policy) from states to actions.\n",
        " \"\"\"\n",
        "chapter22_2 =  \"\"\" In passive RL, agent has a given policy pi(s) and try to learn the utility function when pi is applied at the initial states. \\\n",
        "Direct utility estimation is problematic in that it cuts down connection between states which utility relies on. Temporal difference updates utility based on\\\n",
        "difference between successive states and attempts to adjust the utility estimates toward an ideal local equilibrium\"\"\"\n",
        "chapter22_3 =  \"\"\" Active RL agents decide what actions to take. It faces a tradeoff between exploration and exploitation\"\"\"\n",
        "chapter22_4 =  \"\"\" Evaluation function approximates utility function. RL agents can learn the parameters of approximation functions to optimize utility instead of learning all states \"\"\"\n",
        "chapter22_5 =  \"\"\" Policy search keep adjusting the policy until its performance improves. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\" \n",
        "Direct utility estimation consumes time. TD value learning cannot reflect actions. Should learn TD Q instead.\n",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"sparse reward\" : \"majority states give no informative rewards\",\n",
        "\"model based RL\": \"agents uses a transition model to interpret rewards\",\n",
        "\"direct utility estimation\":\"expected total reward from the state onward\",\n",
        "\"greed\":\"preference for high values of utility\",\n",
        "\"curiosity\":'preference for low frequency actions',\n",
        "\"q learning\":r\" $ Q(s,a) = (1-\\alpha) Q(s,a)+ \\alpha(\\gamma Q(s',a')+ R(s,a,s')) $ \",\n",
        "\"regret\":'total cost for mistakes'\n",
        "}"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "cellView": "form",
        "outputId": "feac977e-ac12-4e47-e00c-05435868028b"
      },
      "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": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n  \n\n## AIMA Chapter 22.1 Learning from Rewards\n Agent must act to learn from environment in reinforcement learning. It has no prior knowledge of transition model and reward.Q-learning is a model free RL. It learns a quality function Q which sums the rewards from a state if taking action a. The agent tries to maximize q value.Another way is policy search. The agent is reflex agent that directly learns a map (policy) from states to actions.\n \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n In passive RL, agent has a given policy pi(s) and try to learn the utility function when pi is applied at the initial states. Direct utility estimation is problematic in that it cuts down connection between states which utility relies on. Temporal difference updates utility based ondifference between successive states and attempts to adjust the utility estimates toward an ideal local equilibrium\n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Active RL agents decide what actions to take. It faces a tradeoff between exploration and exploitation\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Evaluation function approximates utility function. RL agents can learn the parameters of approximation functions to optimize utility instead of learning all states \n\n## Module 9 Lecture Notes\n \nDirect utility estimation consumes time. TD value learning cannot reflect actions. Should learn TD Q instead.\n \n\n## Module 9 Definitions\n* **Sparse reward** - majority states give no informative rewards\n* **Model based rl** - agents uses a transition model to interpret rewards\n* **Direct utility estimation** - expected total reward from the state onward\n* **Greed** - preference for high values of utility\n* **Curiosity** - preference for low frequency actions\n* **Q learning** -  $ Q(s,a) = (1-\\alpha) Q(s,a)+ \\alpha(\\gamma Q(s',a')+ R(s,a,s')) $ \n* **Regret** - total cost for mistakes\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 = \"\"\" Probability quantifies uncertainty \"\"\"\n",
        "chapter12_2 = \"\"\" Probability equations\"\"\"\n",
        "chapter12_3 = \"\"\" Full joint distribution truth tables doesn't scale well. \"\"\"\n",
        "chapter12_4 = \"\"\" P(A|B)=P(A) if A is independent on B \"\"\"\n",
        "chapter12_5 = \"\"\" Bayes Rule \"\"\"\n",
        "chapter12_6 = \"\"\" Naive Bayes \"\"\"\n",
        "chapter12_7 = \"\"\" Logical agents can improve by probability calculations \"\"\"\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 = \"\"\" Chain rule can be applied to n-gram models to construat probabilistic model for language. The assumption is Markovian.\"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Perplexity measures language models. \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Generating random text from language models evaluates it \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" To generalize language models, we need to deal with out of vocabulary words by choosing fixed vocab, convert unknown tokens or estimate probabilities.\"\"\"\n",
        "SLP_chapter3_5 = \"\"\" To deal with zero counts, we can use Laplacian smoothing to add 1 to those instances, add K smoothing or interpolation from lower n-grams. Discount is given to higher orders\"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Stupid backoff doesn't give higher discount. \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" conditional probability is calculated by word counts. \"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"marginal\" : \"sinlge variable probability\",\n",
        "\"Bayes rule\":r\" $ P(b|a)=\\frac{P(a|b)P(b)}{P(a)} $ \",\n",
        "\"Naive Bayes\": r\" $ P(Cause,Effect)=P(Cause) \\prod P(Cause|Effect)  $ \",\n",
        "\"n-gram\":\"sequence of n words\",\n",
        "\"perplexity\":r\" $ PP(W)=P(w_1w_2 \\cdots w_N)^{-\\frac{1}{N}} $ \"\n",
        "}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1182
        },
        "cellView": "form",
        "outputId": "e4b0541f-e227-493c-da3d-39d5b823ca4a"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Probability quantifies uncertainty \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Probability equations\n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Full joint distribution truth tables doesn't scale well. \n\n## AIMA Chapter 12.4 Independence\n P(A|B)=P(A) if A is independent on B \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Bayes Rule \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Naive Bayes \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Logical agents can improve by probability calculations \n\n## SLP Chapter 3.1 N-Grams\n Chain rule can be applied to n-gram models to construat probabilistic model for language. The assumption is Markovian.\n\n## SLP Chapter 3.2 Evaluating Language Models\n Perplexity measures language models. \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Generating random text from language models evaluates it \n\n## SLP Chapter 3.4 Generalization and Zeros\n To generalize language models, we need to deal with out of vocabulary words by choosing fixed vocab, convert unknown tokens or estimate probabilities.\n\n## SLP Chapter 3.5 Smoothing\n To deal with zero counts, we can use Laplacian smoothing to add 1 to those instances, add K smoothing or interpolation from lower n-grams. Discount is given to higher orders\n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Stupid backoff doesn't give higher discount. \n\n## Module 10 Lecture Notes\n conditional probability is calculated by word counts. \n\n## Module 10 Definitions\n* **Marginal** - sinlge variable probability\n* **Bayes rule** -  $ P(b|a)=\\frac{P(a|b)P(b)}{P(a)} $ \n* **Naive bayes** -  $ P(Cause,Effect)=P(Cause) \\prod P(Cause|Effect)  $ \n* **N-gram** - sequence of n words\n* **Perplexity** -  $ PP(W)=P(w_1w_2 \\cdots w_N)^{-\\frac{1}{N}} $ \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 = \"\"\" Bayesian network represents dependencies between variables by a DAG, where nodes are variables and arrows are dependencies. \"\"\"\n",
        "chapter13_2 = r\"\"\" $ P(x_1,...,x_n) = \\prod_{i=1}^n \\theta(x_i| parents(X_i)) $ \"\"\"\n",
        "chapter13_3 = \"\"\" Inference can be done by enumeration or variable elimination.  \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\" If one or more path is active, independence is not guaranteed. If all inactive, independece guaranteed.\\\n",
        "Path is active if each tripel is active \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "r\" $ \\perp \\!\\!\\! \\perp $\" : \"independence\",    \n",
        "}\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "cellView": "form",
        "outputId": "3621c5c9-acd7-47d4-b707-eade1c492d44"
      },
      "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": 10,
      "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 Bayesian network represents dependencies between variables by a DAG, where nodes are variables and arrows are dependencies. \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n $ P(x_1,...,x_n) = \\prod_{i=1}^n \\theta(x_i| parents(X_i)) $ \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Inference can be done by enumeration or variable elimination.  \n\n## Module 11 Lecture Notes\n If one or more path is active, independence is not guaranteed. If all inactive, independece guaranteed.Path is active if each tripel is active \n\n## Module 11 Definitions\n* ** $ \\perp \\!\\!\\! \\perp $** - independence\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 = \"\"\" Different types of learning \"\"\"\n",
        "chapter19_2 = \"\"\" Supervised learning \"\"\"\n",
        "chapter19_3 = \"\"\" Decision Tree \"\"\"\n",
        "chapter19_4 = \"\"\" Techniques to optimize model by loss function, hyper parameter tuning, regularization or feature selection  \"\"\"\n",
        "chapter19_5 = \"\"\"  \"\"\"\n",
        "chapter19_6 = \"\"\" Linear regression and gradient descent \"\"\"\n",
        "\n",
        "# Speech and Language Processing Chapter 4 “Naive Bayes and Sentiment Classification”\n",
        "slp_chapter4_1 = \"\"\" Use BOW as features, use argmax on bayes assumptions to classify documents. \"\"\"\n",
        "slp_chapter4_2 = \"\"\" Train naive bayes by adding log prior and log likelihood class by class \"\"\"\n",
        "slp_chapter4_3 = \"\"\"  \"\"\"\n",
        "slp_chapter4_4 = \"\"\" Binary NB optimizes NB for sentimental analysis \"\"\"\n",
        "slp_chapter4_5 = \"\"\" NB applied to spam detection \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"bag of words\" : \"only frequency matters, positions doesn't\",    \n",
        "\"precision\":\"TP/(TP+FP)\",\n",
        "\"recall\":\"TP/(TP+FN)\",\n",
        "\"F\":\"2PR/(P+R)\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        },
        "outputId": "665f90ae-0cc9-4e47-d92a-3a54ea5cc668"
      },
      "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",
        "  (\"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": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Different types of learning \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Supervised learning \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Decision Tree \n\n## AIMA Chapter 5.5 Stochastic Games\n Techniques to optimize model by loss function, hyper parameter tuning, regularization or feature selection  \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n  \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Linear regression and gradient descent \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Use BOW as features, use argmax on bayes assumptions to classify documents. \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Train naive bayes by adding log prior and log likelihood class by class \n\n## SLP Chapter 4.3 Worked Example\n  \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Binary NB optimizes NB for sentimental analysis \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n NB applied to spam detection \n\n## Module 12 Lecture Notes\n Your summary here \n\n## Module 12 Definitions\n* **Bag of words** - only frequency matters, positions doesn't\n* **Precision** - TP/(TP+FP)\n* **Recall** - TP/(TP+FN)\n* **F** - 2PR/(P+R)\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 = \"\"\" Logistic regression uses sigmoid(wx+b) \"\"\"\n",
        "slp_chapter5_2 = \"\"\" LR uses CrossEntropyLoss and gradient descent to learn \"\"\"\n",
        "slp_chapter5_3 = r\"\"\" $ ylog\\hat{y} + (1-y)log(1-\\hat{y}) $ \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Gradient descent finds minimum by moving small steps towards the direction of gradient. SGD calculates gradients of random samples from training set each time \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Regularization can be added to gradient descent limit the scale of parameters.  \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Multinomial LR uses softmax \"\"\" + r\" $ L_{CE} = \\sum_{k}y_klogy_k $ \"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Neural network consists of units \"\"\"\n",
        "slp_chapter7_2 = \"\"\" NN solves XOR  \"\"\"\n",
        "slp_chapter7_5 = \"\"\" Language model feeds in vector representations for senteces and predicts probabilities on output \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Loss is propagated backward in FFNN \"\"\"\n",
        "slp_chapter7_7 = \"\"\"  \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" NB is generative, Logistic is discriminitive \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"L2 Penalty\" : \"Euclidean distance\",\n",
        "\"L1 Penalty\" : \"Manhattan distance\"   \n",
        "}\n"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1103
        },
        "cellView": "form",
        "outputId": "bc51b68d-4be4-4ae1-a4cc-a6b33ce95cd9"
      },
      "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 7.5 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": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Logistic regression uses sigmoid(wx+b) \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n LR uses CrossEntropyLoss and gradient descent to learn \n\n## SLP Chapter 5.3 The cross-entropy loss function\n $ ylog\\hat{y} + (1-y)log(1-\\hat{y}) $ \n\n## SLP Chapter 5.4 Gradient Descent\n Gradient descent finds minimum by moving small steps towards the direction of gradient. SGD calculates gradients of random samples from training set each time \n\n## SLP Chapter 5.5 Regularization\n Regularization can be added to gradient descent limit the scale of parameters.  \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Multinomial LR uses softmax  $ \\sum_{k}y_klogy_k $ \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 NB is generative, Logistic is discriminitive \n\n## Module 13 Definitions\n* **L2 penalty** - Euclidean distance\n* **L1 penalty** - Manhattan distance\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 = \"\"\" Recap of module 12 \"\"\"\n",
        "slp_chapter9_2 = \"\"\" RNN units relies upon its previous outputs. The update is similar to FFNN \"\"\"\n",
        "slp_chapter9_3 = \"\"\" RNN attempts to predict next word based on current word and previous words represented in hidden states \"\"\"\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 = \"\"\" Linguistic concepts on word semantics \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Embeddings help to measure vector semantics  \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Cooccurance matrix represents words as vectors \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Cosine similarity measures similarity between vectors \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Skip gram samples negative pairs and use positive pairs together to train a logistic regression classifier to embed words \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Embeddings should care the size of context window and the ability to capture relational meanings \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Embeddings might amplify bias \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Similarity is a popular metric to evaluate embeddings. \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\"\n",
        "Dense vectors are easier to use in ml. Tfidf sparse, word2vec dense. Dense generalizes better and captures synonymy\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"embeddings\" : \"vectors representing words\",  \n",
        "'term-document matrix':'row represents word, column represents document and entry is count',\n",
        "\"word-word matrix\":\"cooccurance of words\",\n",
        "\"cosine similarity\": r\" $\\frac{v \\cdot w}{|v||w|} $ \",\n",
        "\"tf\":\"frequency of word in document\",\n",
        "\"idf\":'invers of number of documents it occured in'\n",
        "}\n",
        "\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1038
        },
        "cellView": "form",
        "outputId": "057146ec-cd79-4693-ace2-b02feb9db796"
      },
      "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": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Recap of module 12 \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n RNN units relies upon its previous outputs. The update is similar to FFNN \n\n## SLP Chapter 9.3 RNNs as Language Models\n RNN attempts to predict next word based on current word and previous words represented in hidden states \n\n## SLP Chapter 6.1 Lexical Semantics\n Linguistic concepts on word semantics \n\n## SLP Chapter 6.2 Vector Semantics\n Embeddings help to measure vector semantics  \n\n## SLP Chapter 6.3 Words and Vectors\n Cooccurance matrix represents words as vectors \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Cosine similarity measures similarity between vectors \n\n## SLP Chapter 6.8 Word2vec\n Skip gram samples negative pairs and use positive pairs together to train a logistic regression classifier to embed words \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Embeddings should care the size of context window and the ability to capture relational meanings \n\n## SLP Chapter 6.11 Bias and Embeddings\n Embeddings might amplify bias \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Similarity is a popular metric to evaluate embeddings. \n\n## Module 13 Lecture Notes\n NB is generative, Logistic is discriminitive \n\n## Module 14 Definitions\n* **Embeddings** - vectors representing words\n* **Term-document matrix** - row represents word, column represents document and entry is count\n* **Word-word matrix** - cooccurance of words\n* **Cosine similarity** -  $\\frac{v \\cdot w}{|v||w|} $ \n* **Tf** - frequency of word in document\n* **Idf** - invers of number of documents it occured in\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": 6098
        },
        "id": "T3cm1WV542_a",
        "outputId": "01e9112f-c377-4f12-fe14-aafd091b0155"
      },
      "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": 33,
      "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 MEU principle: rational agent choose actions that maximizes its expected utility. Performance score measures history, utility function apply to the next state and guide actions step by step \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n A utility function exists for any rational agent but its not unique. \n\n## AIMA Chapter 16.3 Utility Functions\n\n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Fully observable, stochastic environment, Markovian transition model and additive reward make a sequential decision probelm MDP, consisting of a set of states, a set of actions in each state, a transition model, and a reward funciton. Solving MDP involves dynamic programming. Optimal policy yields highest EU. Infinite horizon doesn't imply infinite sequence.\n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Value Iteration, policy iteration, linear programming and Monte Carlo planning are 4 different ways to solve mdp. The first 3 are offline. policy iteration consists of policy evaluation and policy improvement.Linear programming seeks to minimize U(s) subject to the constraint of bellman equation.\n \n\n## Module 8 Lecture Notes\n \nproblems of value iteration:\nProblem 1:  O(S^2A) per iteration\nProblem 2: max rarely changes\nProblem 3: converges long before the values\n\nDiscount because sooner rewards matters more and it helps convergence\n \n\n## Module 8 Definitions\n* **Utility function** - measures preference\n* **Expected utility** - probability weighted average of utility from outcomes\n* **Value function/ordinal utility function** - rank of preference, not number\n* **Orderability** -  $ A \\succ B, A \\prec B \\textnormal{or} A \\sim B$ \n* **Transitivity** -  $ (A \\succ B) \\land (B \\succ C) \\implies A \\succ C$ \n* **Continuity** -  $ A \\succ B \\succ C \\implies \\exist p, [p,A;1-p,C]\\sim B$ \n* **Substitutability** -  $ A \\sim B \\implies [p,A;1-p, C] \\sim [p, B; 1-p, C] $ \n* **Monotonicity** -  $ A \\succ B \\implies (p>q \\iff [p,A; 1-p, B] \\succ [q, A; 1-q, B] $ \n* **Decomposability** -  $ [p,A; 1-p, [q,B; 1-q, C]] \\sim [p,A; (1-p)q,B; (1-p)(1-q), C] $ \n* **Policy** - instructions for actions in any state the agent might reach\n* **Finite horizon** - fixed number of action\n* **Value iteration** - simultaneously updates state values by bellman\n* **Policy iteration** - repeat same policy until convergence then improve\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n  \n\n## AIMA Chapter 22.1 Learning from Rewards\n Agent must act to learn from environment in reinforcement learning. It has no prior knowledge of transition model and reward.Q-learning is a model free RL. It learns a quality function Q which sums the rewards from a state if taking action a. The agent tries to maximize q value.Another way is policy search. The agent is reflex agent that directly learns a map (policy) from states to actions.\n \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n In passive RL, agent has a given policy pi(s) and try to learn the utility function when pi is applied at the initial states. Direct utility estimation is problematic in that it cuts down connection between states which utility relies on. Temporal difference updates utility based ondifference between successive states and attempts to adjust the utility estimates toward an ideal local equilibrium\n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Active RL agents decide what actions to take. It faces a tradeoff between exploration and exploitation\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Evaluation function approximates utility function. RL agents can learn the parameters of approximation functions to optimize utility instead of learning all states \n\n## Module 9 Lecture Notes\n \nDirect utility estimation consumes time. TD value learning cannot reflect actions. Should learn TD Q instead.\n \n\n## Module 9 Definitions\n* **Sparse reward** - majority states give no informative rewards\n* **Model based rl** - agents uses a transition model to interpret rewards\n* **Direct utility estimation** - expected total reward from the state onward\n* **Greed** - preference for high values of utility\n* **Curiosity** - preference for low frequency actions\n* **Q learning** -  $ Q(s,a) = (1-\\alpha) Q(s,a)+ \\alpha(\\gamma Q(s',a')+ R(s,a,s')) $ \n* **Regret** - total cost for mistakes\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Probability quantifies uncertainty \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Probability equations\n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Full joint distribution truth tables doesn't scale well. \n\n## AIMA Chapter 12.4 Independence\n P(A|B)=P(A) if A is independent on B \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Bayes Rule \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Naive Bayes \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Logical agents can improve by probability calculations \n\n## SLP Chapter 3.1 N-Grams\n Chain rule can be applied to n-gram models to construat probabilistic model for language. The assumption is Markovian.\n\n## SLP Chapter 3.2 Evaluating Language Models\n Perplexity measures language models. \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Generating random text from language models evaluates it \n\n## SLP Chapter 3.4 Generalization and Zeros\n To generalize language models, we need to deal with out of vocabulary words by choosing fixed vocab, convert unknown tokens or estimate probabilities.\n\n## SLP Chapter 3.5 Smoothing\n To deal with zero counts, we can use Laplacian smoothing to add 1 to those instances, add K smoothing or interpolation from lower n-grams. Discount is given to higher orders\n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Stupid backoff doesn't give higher discount. \n\n## Module 10 Lecture Notes\n conditional probability is calculated by word counts. \n\n## Module 10 Definitions\n* **Marginal** - sinlge variable probability\n* **Bayes rule** -  $ P(b|a)=\\frac{P(a|b)P(b)}{P(a)} $ \n* **Naive bayes** -  $ P(Cause,Effect)=P(Cause) \\prod P(Cause|Effect)  $ \n* **N-gram** - sequence of n words\n* **Perplexity** -  $ PP(W)=P(w_1w_2 \\cdots w_N)^{-\\frac{1}{N}} $ \n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Bayesian network represents dependencies between variables by a DAG, where nodes are variables and arrows are dependencies. \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n $ P(x_1,...,x_n) = \\prod_{i=1}^n \\theta(x_i| parents(X_i)) $ \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Inference can be done by enumeration or variable elimination.  \n\n## Module 11 Lecture Notes\n If one or more path is active, independence is not guaranteed. If all inactive, independece guaranteed.Path is active if each tripel is active \n\n## Module 11 Definitions\n* ** $ \\perp \\!\\!\\! \\perp $** - independence\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Different types of learning \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Supervised learning \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Decision Tree \n\n## AIMA Chapter 5.5 Stochastic Games\n Techniques to optimize model by loss function, hyper parameter tuning, regularization or feature selection  \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n  \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Linear regression and gradient descent \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Use BOW as features, use argmax on bayes assumptions to classify documents. \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Train naive bayes by adding log prior and log likelihood class by class \n\n## SLP Chapter 4.3 Worked Example\n  \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Binary NB optimizes NB for sentimental analysis \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n NB applied to spam detection \n\n## Module 12 Lecture Notes\n Your summary here \n\n## Module 12 Definitions\n* **Bag of words** - only frequency matters, positions doesn't\n* **Precision** - TP/(TP+FP)\n* **Recall** - TP/(TP+FN)\n* **F** - 2PR/(P+R)\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Logistic regression uses sigmoid(wx+b) \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n LR uses CrossEntropyLoss and gradient descent to learn \n\n## SLP Chapter 5.3 The cross-entropy loss function\n $ ylog\\hat{y} + (1-y)log(1-\\hat{y}) $ \n\n## SLP Chapter 5.4 Gradient Descent\n Gradient descent finds minimum by moving small steps towards the direction of gradient. SGD calculates gradients of random samples from training set each time \n\n## SLP Chapter 5.5 Regularization\n Regularization can be added to gradient descent limit the scale of parameters.  \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Multinomial LR uses softmax  $ \\sum_{k}y_klogy_k $ \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 NB is generative, Logistic is discriminitive \n\n## Module 13 Definitions\n* **L2 penalty** - Euclidean distance\n* **L1 penalty** - Manhattan distance\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Recap of module 12 \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n RNN units relies upon its previous outputs. The update is similar to FFNN \n\n## SLP Chapter 9.3 RNNs as Language Models\n RNN attempts to predict next word based on current word and previous words represented in hidden states \n\n## SLP Chapter 6.1 Lexical Semantics\n Linguistic concepts on word semantics \n\n## SLP Chapter 6.2 Vector Semantics\n Embeddings help to measure vector semantics  \n\n## SLP Chapter 6.3 Words and Vectors\n Cooccurance matrix represents words as vectors \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Cosine similarity measures similarity between vectors \n\n## SLP Chapter 6.8 Word2vec\n Skip gram samples negative pairs and use positive pairs together to train a logistic regression classifier to embed words \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Embeddings should care the size of context window and the ability to capture relational meanings \n\n## SLP Chapter 6.11 Bias and Embeddings\n Embeddings might amplify bias \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Similarity is a popular metric to evaluate embeddings. \n\n## Module 13 Lecture Notes\n NB is generative, Logistic is discriminitive \n\n## Module 14 Definitions\n* **Embeddings** - vectors representing words\n* **Term-document matrix** - row represents word, column represents document and entry is count\n* **Word-word matrix** - cooccurance of words\n* **Cosine similarity** -  $\\frac{v \\cdot w}{|v||w|} $ \n* **Tf** - frequency of word in document\n* **Idf** - invers of number of documents it occured in\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}