{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "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": "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": "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": "e637da81-51a1-469e-ea08-f72a9e1b3899"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cf1979a-e30d-4904-b9f1-7eb4ea8cb9a8"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = True"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 16, “Making Simple Decisions” (16.1-16.3)\n",
        "\n",
        "chapter16_1 = \"\"\" \"\"\"\n",
        "chapter16_2 =  \"\"\" \"\"\"\n",
        "chapter16_3 =  \"\"\" \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" \"\"\"\n",
        "chapter17_2 =  \"\"\" \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\" \n",
        "Markov decision process: a sequential decision problem + fully observable environment + stochastic transition model + additive rewards + compute expected utility\n",
        "\n",
        "What if the game lasts forever?\n",
        " - finite horizon: terminate episodes after a fixed T steps\n",
        " - nonstationary policies (π depends on time left)\n",
        "\n",
        "Value Iteration\n",
        " - run time slow O(s^2 A)\n",
        " - the policy often converges long before the values\n",
        "\n",
        "Policy Iteration\n",
        " - O(s^2) per policy evaluation, can converge (much) faster\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\" : \"assigns a single number to express the desirability of a state\",\n",
        "\"markovian\" : \"the probability of arriving in the next state only depends on current state and not history\",\n",
        "\"expected utility\" : \"average utility value of the outcomes, weighted by the probability\",\n",
        "\"maximum expected utility (MEU)\" : \"a rational agent should choose the action that maximizes the agent’s expected utility\",\n",
        "\"value function\": \"in a deterministic environment an agent needs only a preference ranking on states—the numbers don’t matter\",\n",
        "\"ordinal utility function\" : \"Same as value function\",\n",
        "\"nonstationary policy\": \"A policy that depends on the time (optimal policy is stationary)\",\n",
        "\"proper policy\": \"A policy that is guaranteed to reach a terminal state\",\n",
        "\"asynchronous policy iteration\": \"on each iteration we can pick any subset of states and apply either kind of updating\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 8,
      "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",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "outputId": "5939bd2b-28fa-4cf6-e9c7-3affce7fd300"
      },
      "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": 9,
      "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 \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n \n\n## AIMA Chapter 16.3 Utility Functions\n \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n \n\n## Module 8 Lecture Notes\n \nMarkov decision process: a sequential decision problem + fully observable environment + stochastic transition model + additive rewards + compute expected utility\n\nWhat if the game lasts forever?\n - finite horizon: terminate episodes after a fixed T steps\n - nonstationary policies (π depends on time left)\n\nValue Iteration\n - run time slow O(s^2 A)\n - the policy often converges long before the values\n\nPolicy Iteration\n - O(s^2) per policy evaluation, can converge (much) faster\n\n\n## Module 8 Definitions\n* **Utility function** - assigns a single number to express the desirability of a state\n* **Markovian** - the probability of arriving in the next state only depends on current state and not history\n* **Expected utility** - average utility value of the outcomes, weighted by the probability\n* **Maximum expected utility (meu)** - a rational agent should choose the action that maximizes the agent’s expected utility\n* **Value function** - in a deterministic environment an agent needs only a preference ranking on states—the numbers don’t matter\n* **Ordinal utility function** - Same as value function\n* **Nonstationary policy** - A policy that depends on the time (optimal policy is stationary)\n* **Proper policy** - A policy that is guaranteed to reach a terminal state\n* **Asynchronous policy iteration** - on each iteration we can pick any subset of states and apply either kind of updating\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 =  \"\"\" \"\"\"\n",
        "chapter22_2 =  \"\"\" \"\"\"\n",
        "chapter22_3 =  \"\"\" \"\"\"\n",
        "chapter22_4 =  \"\"\" \"\"\"\n",
        "chapter22_5 =  \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\"\n",
        "Important Ideas in Reinforcement Learning\n",
        " - exploration: you have to try unknown actions to get information\n",
        " - exploitation: eventually, you have to use what you know\n",
        " - regret: the difference between your (expected) rewards and optimal (expected) rewards\n",
        " - sampling: because of chance, you have to try things repeatedly\n",
        "\n",
        "Direct Evaluation\n",
        " - compute values for each state under π\n",
        " - average together observed sample values\n",
        " - (good) easy to understand + doesn’t require T, R + eventually converge\n",
        " - (bad) wastes information about state connections + long time to learn\n",
        "\n",
        "Sample-Based Policy Evaluation\n",
        " - take samples of outcomes s’ by doing the action and average\n",
        " - problem: cannot rewind time and keep visiting state s\n",
        "\n",
        "Temporal Difference Learning (fixed policy)\n",
        " - update V(s) each time we experience a transition (s,a,s’,r)\n",
        " - likely outcomes s’ will contribute updates more often\n",
        " - decreasing learning rate (α) can give converging averages\n",
        "\n",
        "Problems with TD Value Learning\n",
        " - cannot turn values into a new policy, no knowledge of T\n",
        " - solution: learn Q values directly, not values\n",
        "\n",
        "Q Learning (off-policy learning)\n",
        " - Q(s,a)=(1-α)Q(s,a)+(α)sample\n",
        "\n",
        "Q Learning Properties\n",
        " - converges to optimal policy\n",
        " - have to explore enough + eventually make the learning rate small enough\n",
        "\n",
        "ε-Greedy Exploration\n",
        " - with (small) probability ε, act randomly\n",
        " - with (large) probability 1-ε, act on current policy\n",
        " - eventually explore the space, but inefficient\n",
        " - lower ε over time\n",
        "\n",
        "Exploration Functions\n",
        " - takes a value estimate u and a visit count n, and returns an optimistic utility\n",
        " - e.g. f(u,n) = u + k/n\n",
        "\n",
        "Approximate Q-Learning\n",
        " - advantage: our experience is summed up in a few powerful numbers\n",
        " - disadvantage: states may share features but actually be very different in value\n",
        "\n",
        "Approximate Q-Learning Update\n",
        " - transition = (s,a,r,s’)\n",
        " - only adjust weights of active features\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"Absorbing State\" : \"state where no actions have any effect and no rewards are received\",\n",
        "\"Active RL\" : \"the agent must also figure out what to do (exploration)\",\n",
        "\"Apprenticeship Learning\" : \"training a learning agent using demonstrations rather than rewards\",\n",
        "\"Bernoulli Bandit\" : \"each arm produces a reward of 0 or 1 with a fixed but unknown probability μi\",\n",
        "\"Function Approximation\" : \"construct a compact approx of the true utility function or Q-function\",\n",
        "\"Model-Based RL\" : \"agent uses a transition model to interpret reward signals and make decisions\",\n",
        "\"Model-Free RL\" : \"the agent neither knows nor learns a transition model for the environment\",\n",
        "\"Off-Policy\" : \"evaluate & improve a policy that is different from the one used for action selection\",\n",
        "\"On-Policy\" : \"evaluate and improve the same policy which is being used to select actions\",\n",
        "\"Passive RL\" : \"the agent’s policy is fixed and the task is to learn the utilities of states\",\n",
        "}"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c33849dc-ecec-4727-f799-c83ce3ecd053"
      },
      "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": 14,
      "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 \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n \n\n## Module 9 Lecture Notes\n\nImportant Ideas in Reinforcement Learning\n - exploration: you have to try unknown actions to get information\n - exploitation: eventually, you have to use what you know\n - regret: the difference between your (expected) rewards and optimal (expected) rewards\n - sampling: because of chance, you have to try things repeatedly\n\nDirect Evaluation\n - compute values for each state under π\n - average together observed sample values\n - (good) easy to understand + doesn’t require T, R + eventually converge\n - (bad) wastes information about state connections + long time to learn\n\nSample-Based Policy Evaluation\n - take samples of outcomes s’ by doing the action and average\n - problem: cannot rewind time and keep visiting state s\n\nTemporal Difference Learning (fixed policy)\n - update V(s) each time we experience a transition (s,a,s’,r)\n - likely outcomes s’ will contribute updates more often\n - decreasing learning rate (α) can give converging averages\n\nProblems with TD Value Learning\n - cannot turn values into a new policy, no knowledge of T\n - solution: learn Q values directly, not values\n\nQ Learning (off-policy learning)\n - Q(s,a)=(1-α)Q(s,a)+(α)sample\n\nQ Learning Properties\n - converges to optimal policy\n - have to explore enough + eventually make the learning rate small enough\n\nε-Greedy Exploration\n - with (small) probability ε, act randomly\n - with (large) probability 1-ε, act on current policy\n - eventually explore the space, but inefficient\n - lower ε over time\n\nExploration Functions\n - takes a value estimate u and a visit count n, and returns an optimistic utility\n - e.g. f(u,n) = u + k/n\n\nApproximate Q-Learning\n - advantage: our experience is summed up in a few powerful numbers\n - disadvantage: states may share features but actually be very different in value\n\nApproximate Q-Learning Update\n - transition = (s,a,r,s’)\n - only adjust weights of active features\n\n\n\n## Module 9 Definitions\n* **Absorbing state** - state where no actions have any effect and no rewards are received\n* **Active rl** - the agent must also figure out what to do (exploration)\n* **Apprenticeship learning** - training a learning agent using demonstrations rather than rewards\n* **Bernoulli bandit** - each arm produces a reward of 0 or 1 with a fixed but unknown probability μi\n* **Function approximation** - construct a compact approx of the true utility function or Q-function\n* **Model-based rl** - agent uses a transition model to interpret reward signals and make decisions\n* **Model-free rl** - the agent neither knows nor learns a transition model for the environment\n* **Off-policy** - evaluate & improve a policy that is different from the one used for action selection\n* **On-policy** - evaluate and improve the same policy which is being used to select actions\n* **Passive rl** - the agent’s policy is fixed and the task is to learn the utilities of states\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 = \"\"\" \"\"\"\n",
        "chapter12_2 = \"\"\" \"\"\"\n",
        "chapter12_3 = \"\"\" \"\"\"\n",
        "chapter12_4 = \"\"\" \"\"\"\n",
        "chapter12_5 = \"\"\" \"\"\"\n",
        "chapter12_6 = \"\"\" \"\"\"\n",
        "chapter12_7 = \"\"\" \"\"\"\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 = \"\"\" \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" \n",
        "Bayes Rule\n",
        " - P(a│b)=P(a,b)/(P(b)), \n",
        " - P(a│b)∙P(b)=P(a,b)\n",
        " - P(a│b)=(P(b|a))/(p(b))∙P(a)\n",
        "\n",
        "Inclusion–Exclusion Principle: P(a∨b) = P(a) + P(b) – P(a∧b)\n",
        "\n",
        "Perplexity: inverse probability of a sentence, normalized by the number of words\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"Extrinsic Evaluation\" : \"measure model in an application and measure how much it improves\",\n",
        "\"Factored Representation\" : \"possible world is represented by a set of variable/value pairs\",\n",
        "\"Intrinsic Evaluation\" : \"one that measures the quality of a model independent of any application\",\n",
        "\"Laziness\" : \"it is too much work to list the complete set of antecedents or consequents\",\n",
        "\"Theoretical Ignorance\" : \"no complete theory for the domain.\",\n",
        "}"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "cellView": "form"
      },
      "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": []
    },
    {
      "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",
        "chapter13_2 = \"\"\" \"\"\"\n",
        "chapter13_3 = \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\"\n",
        "Independence\n",
        " - P(x│y)=P(x),P(y│x)=P(y)\n",
        " - P(x ∧ y)=P(x)P(y)\n",
        "\n",
        "Conditional Independence\n",
        " - x is conditionally independent of y given z\n",
        " - P(x,y│z)=P(x│z)P(y│z) or P(x│z,y)=P(x|z)\n",
        "\n",
        "Size of Bayes’ Net\n",
        " - how big is a joint distribution over N Boolean variables? 2^N\n",
        " - how big is an N-node net if nodes have up to k parents? O(N∙2^(k+1))\n",
        "\n",
        "D-Separation\n",
        " - a path is active if each triple is active\n",
        " - no active paths = independence\n",
        " - gray node = information is given \n",
        "\n",
        "Inference by Enumeration vs. Variable Elimination\n",
        " - inference slow: join up the whole joint distribution before you sum out hidden variables\n",
        " - advanced technique: variable elimination\n",
        " - interleave joining and marginalizing\n",
        " - still NP-hard, but usually much faster than inference by enumeration\n",
        " - clustering algorithms / join tree algorithms - run time O(n)\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = { }\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "cellView": "form",
        "outputId": "f9f25521-4930-467c-934f-fd2b49ae6342"
      },
      "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": 18,
      "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 \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n \n\n## Module 11 Lecture Notes\n\nIndependence\n - P(x│y)=P(x),P(y│x)=P(y)\n - P(x ∧ y)=P(x)P(y)\n\nConditional Independence\n - x is conditionally independent of y given z\n - P(x,y│z)=P(x│z)P(y│z) or P(x│z,y)=P(x|z)\n\nSize of Bayes’ Net\n - how big is a joint distribution over N Boolean variables? 2^N\n - how big is an N-node net if nodes have up to k parents? O(N∙2^(k+1))\n\nD-Separation\n - a path is active if each triple is active\n - no active paths = independence\n - gray node = information is given \n\nInference by Enumeration vs. Variable Elimination\n - inference slow: join up the whole joint distribution before you sum out hidden variables\n - advanced technique: variable elimination\n - interleave joining and marginalizing\n - still NP-hard, but usually much faster than inference by enumeration\n - clustering algorithms / join tree algorithms - run time O(n)\n\n\n## Module 11 Definitions\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 = \"\"\" \"\"\"\n",
        "chapter19_2 = \"\"\" \"\"\"\n",
        "chapter19_3 = \"\"\" \"\"\"\n",
        "chapter19_4 = \"\"\" \"\"\"\n",
        "chapter19_5 = \"\"\" \"\"\"\n",
        "chapter19_6 = \"\"\" \"\"\"\n",
        "\n",
        "# Speech and Language Processing Chapter 4 “Naive Bayes and Sentiment Classification”\n",
        "slp_chapter4_1 = \"\"\" \"\"\"\n",
        "slp_chapter4_2 = \"\"\" \"\"\"\n",
        "slp_chapter4_3 = \"\"\" \"\"\"\n",
        "slp_chapter4_4 = \"\"\" \"\"\"\n",
        "slp_chapter4_5 = \"\"\" \"\"\"\n",
        "slp_chapter4_6 = \"\"\" \"\"\"\n",
        "slp_chapter4_7 = \"\"\" \"\"\"\n",
        "slp_chapter4_8 = \"\"\" \"\"\"\n",
        "slp_chapter4_9 = \"\"\" \"\"\"\n",
        "slp_chapter4_10 = \"\"\" \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\"\n",
        "Naïve Bayes (Generative Model)\n",
        " - Assume all features are independent effects of the label\n",
        "\n",
        "Laplace Smoothing\n",
        " - P_LAP (x)=(c(x)+k)/(N+k|X|), k is the strength of the prior\n",
        " - P_(LAP,k) (x|y)=(c(x,y)+k)/(c(y)+k|X|) Laplace for conditionals, smooth each condition independently\n",
        "\n",
        "Linear Interpolation (smoothing)\n",
        " - P_LIN (x│y)=αP(x│y)+(1.0-α)P(x)\n",
        "\n",
        "Learning: Multiclass Perceptron\n",
        " - predict with current weights, if correct no change.\n",
        " - if wrong: lower score of wrong answer, raise score of right answer\n",
        "\n",
        "Mistake Bound\n",
        " - the maximum number of mistakes related to the margin or degree of separability\n",
        " - mistakes < k/δ^2  large margin = easily separable = fewer mistakes (vice versa)\n",
        "\n",
        "MIRA\n",
        " - choose an update size that fixes the current mistake but minimizes the change to w\n",
        "\n",
        "Support Vector Machines (SVM)\n",
        " - maximizing the margin, only support vectors matter\n",
        " - support vector machines (SVMs) find the separator with max margin\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"Decision Tree Pruning\" : \"eliminate nodes that are not clearly relevant (combats overfitting)\",\n",
        "\"Generative Classifiers\" : \"build a model of how a class could generate some input data\",\n",
        "\"Reinforcement Learning\" : \"agent learns from a series of reinforcements: rewards & punishments\",\n",
        "\"Supervised Learning\" : \"the agent observes input-output pairs and learns a function that maps from input to output\",\n",
        "\"Unsupervised Learning\" : \"the agent learns patterns in the input without any explicit feedback\",\n",
        "\"Variance\" : \"the amount of change in the hypothesis due to fluctuation in the training data\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "e8e73256-3156-4232-fc8b-76936101ae61"
      },
      "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": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n \n\n## AIMA Chapter 5.5 Stochastic Games\n \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 \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n \n\n## SLP Chapter 4.3 Worked Example\n \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n \n\n## SLP Chapter 4.9 Statistical Significance Testing\n \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n \n\n## Module 12 Lecture Notes\n\nNaïve Bayes (Generative Model)\n - Assume all features are independent effects of the label\n\nLaplace Smoothing\n - P_LAP (x)=(c(x)+k)/(N+k|X|), k is the strength of the prior\n - P_(LAP,k) (x|y)=(c(x,y)+k)/(c(y)+k|X|) Laplace for conditionals, smooth each condition independently\n\nLinear Interpolation (smoothing)\n - P_LIN (x│y)=αP(x│y)+(1.0-α)P(x)\n\nLearning: Multiclass Perceptron\n - predict with current weights, if correct no change.\n - if wrong: lower score of wrong answer, raise score of right answer\n\nMistake Bound\n - the maximum number of mistakes related to the margin or degree of separability\n - mistakes < k/δ^2  large margin = easily separable = fewer mistakes (vice versa)\n\nMIRA\n - choose an update size that fixes the current mistake but minimizes the change to w\n\nSupport Vector Machines (SVM)\n - maximizing the margin, only support vectors matter\n - support vector machines (SVMs) find the separator with max margin\n\n\n\n## Module 12 Definitions\n* **Decision tree pruning** - eliminate nodes that are not clearly relevant (combats overfitting)\n* **Generative classifiers** - build a model of how a class could generate some input data\n* **Reinforcement learning** - agent learns from a series of reinforcements: rewards & punishments\n* **Supervised learning** - the agent observes input-output pairs and learns a function that maps from input to output\n* **Unsupervised learning** - the agent learns patterns in the input without any explicit feedback\n* **Variance** - the amount of change in the hypothesis due to fluctuation in the training data\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 = \"\"\" \"\"\"\n",
        "slp_chapter5_2 = \"\"\" \"\"\"\n",
        "slp_chapter5_3 = \"\"\" \"\"\"\n",
        "slp_chapter5_4 = \"\"\" \"\"\"\n",
        "slp_chapter5_5 = \"\"\" \"\"\"\n",
        "slp_chapter5_6 = \"\"\" \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" \"\"\"\n",
        "slp_chapter7_2 = \"\"\" \"\"\"\n",
        "slp_chapter7_3 = \"\"\" \"\"\"\n",
        "slp_chapter7_4 = \"\"\" \"\"\"\n",
        "slp_chapter7_5 = \"\"\" \"\"\"\n",
        "slp_chapter7_6 = \"\"\" \"\"\"\n",
        "slp_chapter7_7 = \"\"\" \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" \n",
        "ML Classifier Components \n",
        " - a feature representation of the input, [x1,x2,...,xn]. \n",
        " - a classification function that computes the estimated class y ̂ via p(y|x). \n",
        " - an objective function for learning, usually involving minimizing error on training.\n",
        " - an algorithm for optimizing the objective function.\n",
        "\n",
        "Cross Entropy Loss\n",
        " - For one observation x, maximize the probability of the correct label p(y|x).\n",
        " - P(y|x) = y ̂^y (1-y ̂ )^(1-y)\n",
        " - If y=1, then p(y|x) = y ̂\n",
        " - If y=0, then p(y|x) = 1-y ̂\n",
        "\n",
        "Regularization\n",
        " - penalize large weights by adding regularization term to the loss function\n",
        " - L2 regularization (Ridge Regression) is a quadratic function of L2 regularization the weight values, \n",
        " - named because it uses the (square of the) L2 norm of the weight values.\n",
        " - L1 regularization (Lasso Regression) is a linear function of the weight values\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"Dropout\" : \"randomly dropping some units and their connections from the network during training\",\n",
        "\"Discriminative Model\" : \"attempts to directly compute P(y|x)\",\n",
        "\"Generative Model\" : \"how to generate the features if we knew it was of class c\",\n",
        "\"Hard Classification Task\" : \"only one class is the correct one\",\n",
        "\"Representation Learning\" : \"learn features automatically in an unsupervised way from the input\"\n",
        "}\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "05c2cf79-23d8-43b5-9400-18be70391401"
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n \n\n## SLP Chapter 5.3 The cross-entropy loss function\n \n\n## SLP Chapter 5.4 Gradient Descent\n \n\n## SLP Chapter 5.5 Regularization\n \n\n## SLP Chapter 5.6 Multinomial logistic regression\n \n\n## SLP Chapter 7.1 Units\n \n\n## SLP Chapter 7.2 The XOR problem\n \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n \n\n## SLP Chapter 7.6 Training Neural Nets\n \n\n## SLP Chapter 7.7 Training the neural language model\n \n\n## Module 13 Lecture Notes\n \nML Classifier Components \n - a feature representation of the input, [x1,x2,...,xn]. \n - a classification function that computes the estimated class y ̂ via p(y|x). \n - an objective function for learning, usually involving minimizing error on training.\n - an algorithm for optimizing the objective function.\n\nCross Entropy Loss\n - For one observation x, maximize the probability of the correct label p(y|x).\n - P(y|x) = y ̂^y (1-y ̂ )^(1-y)\n - If y=1, then p(y|x) = y ̂\n - If y=0, then p(y|x) = 1-y ̂\n\nRegularization\n - penalize large weights by adding regularization term to the loss function\n - L2 regularization (Ridge Regression) is a quadratic function of L2 regularization the weight values, \n - named because it uses the (square of the) L2 norm of the weight values.\n - L1 regularization (Lasso Regression) is a linear function of the weight values\n\n\n## Module 13 Definitions\n* **Dropout** - randomly dropping some units and their connections from the network during training\n* **Discriminative model** - attempts to directly compute P(y|x)\n* **Generative model** - how to generate the features if we knew it was of class c\n* **Hard classification task** - only one class is the correct one\n* **Representation learning** - learn features automatically in an unsupervised way from the input\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 = \"\"\" \"\"\"\n",
        "slp_chapter9_2 = \"\"\" \"\"\"\n",
        "slp_chapter9_3 = \"\"\" \"\"\"\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 = \"\"\" \"\"\"\n",
        "slp_chapter6_2 = \"\"\" \"\"\"\n",
        "slp_chapter6_3 = \"\"\" \"\"\"\n",
        "slp_chapter6_4 = \"\"\" \"\"\"\n",
        "slp_chapter6_8 = \"\"\" \"\"\"\n",
        "slp_chapter6_10 = \"\"\" \"\"\"\n",
        "slp_chapter6_11 = \"\"\" \"\"\"\n",
        "slp_chapter6_12 = \"\"\" \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\"\n",
        "Neural Language Models\n",
        " - Advantage over n-gram LMs: \n",
        " - don’t need smoothing\n",
        " - can handle much longer histories\n",
        " - can generalize over contexts of similar words\n",
        " - have much higher predictive accuracy\n",
        " - Disadvantage: slower to train than traditional n-gram LMs\n",
        "\n",
        "Neural LMs\n",
        " - The input to the neural network is a k-gram of words w_(1:k)\n",
        " - The output is a probability distribution over the next word\n",
        " - The k context words are treated as a word window. Each word is associated with an embedded vector, and the input vector x just concatenates v(w) for each of the k words x = [v(w_1),v(w_2),…,v(w_k)]\n",
        "\n",
        "TF-IDF \n",
        " - Term frequency ∙ inverse document frequency\n",
        "\n",
        "Co-occurrence matrix\n",
        " - sparse: long (length |V|=50,000+), most elements are zeros\n",
        " - dense: short (length 200-1000), most elements are non-zero\n",
        "\n",
        "Train a classifier to get dense vectors\n",
        " - treat the target word and a neighboring context word as positive examples.\n",
        " - randomly sample other words in the lexicon to get negative samples.\n",
        " - use logistic regression to train a classifier to distinguish those two cases.\n",
        " - use the weights as the embeddings.\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"Distributional Hypothesis\" : \"the link between similarity in how words are distributed and similarity in what they mean\",\n",
        "\"First-Order Co-Occurrence\" : \"two words that are typically nearby each other\",\n",
        "\"Principle of Contrast\" : \"difference in linguistic form associated with some difference in meaning\",\n",
        "\"Second-Order Co-Occurrence\" : \"two words have similar second-order co-occurrence neighbors\"\n",
        "}\n",
        "\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "310a7f90-ed8b-47f7-f413-1a6c4e7dfc81"
      },
      "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": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n \n\n## SLP Chapter 9.3 RNNs as Language Models\n \n\n## SLP Chapter 6.1 Lexical Semantics\n \n\n## SLP Chapter 6.2 Vector Semantics\n \n\n## SLP Chapter 6.3 Words and Vectors\n \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n \n\n## SLP Chapter 6.8 Word2vec\n \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n \n\n## SLP Chapter 6.11 Bias and Embeddings\n \n\n## SLP Chapter 6.12 Evaluating Vector Models\n \n\n## Module 13 Lecture Notes\n \nML Classifier Components \n - a feature representation of the input, [x1,x2,...,xn]. \n - a classification function that computes the estimated class y ̂ via p(y|x). \n - an objective function for learning, usually involving minimizing error on training.\n - an algorithm for optimizing the objective function.\n\nCross Entropy Loss\n - For one observation x, maximize the probability of the correct label p(y|x).\n - P(y|x) = y ̂^y (1-y ̂ )^(1-y)\n - If y=1, then p(y|x) = y ̂\n - If y=0, then p(y|x) = 1-y ̂\n\nRegularization\n - penalize large weights by adding regularization term to the loss function\n - L2 regularization (Ridge Regression) is a quadratic function of L2 regularization the weight values, \n - named because it uses the (square of the) L2 norm of the weight values.\n - L1 regularization (Lasso Regression) is a linear function of the weight values\n\n\n## Module 14 Definitions\n* **Distributional hypothesis** - the link between similarity in how words are distributed and similarity in what they mean\n* **First-order co-occurrence** - two words that are typically nearby each other\n* **Principle of contrast** - difference in linguistic form associated with some difference in meaning\n* **Second-order co-occurrence** - two words have similar second-order co-occurrence neighbors\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": "7c3f39f3-1396-4dd1-e2e1-38888455678c"
      },
      "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": 38,
      "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 \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n \n\n## AIMA Chapter 16.3 Utility Functions\n \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n \n\n## Module 8 Lecture Notes\n \nMarkov decision process: a sequential decision problem + fully observable environment + stochastic transition model + additive rewards + compute expected utility\n\nWhat if the game lasts forever?\n - finite horizon: terminate episodes after a fixed T steps\n - nonstationary policies (π depends on time left)\n\nValue Iteration\n - run time slow O(s^2 A)\n - the policy often converges long before the values\n\nPolicy Iteration\n - O(s^2) per policy evaluation, can converge (much) faster\n\n\n## Module 8 Definitions\n* **Utility function** - assigns a single number to express the desirability of a state\n* **Markovian** - the probability of arriving in the next state only depends on current state and not history\n* **Expected utility** - average utility value of the outcomes, weighted by the probability\n* **Maximum expected utility (meu)** - a rational agent should choose the action that maximizes the agent’s expected utility\n* **Value function** - in a deterministic environment an agent needs only a preference ranking on states—the numbers don’t matter\n* **Ordinal utility function** - Same as value function\n* **Nonstationary policy** - A policy that depends on the time (optimal policy is stationary)\n* **Proper policy** - A policy that is guaranteed to reach a terminal state\n* **Asynchronous policy iteration** - on each iteration we can pick any subset of states and apply either kind of updating\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 \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n \n\n## Module 9 Lecture Notes\n\nImportant Ideas in Reinforcement Learning\n - exploration: you have to try unknown actions to get information\n - exploitation: eventually, you have to use what you know\n - regret: the difference between your (expected) rewards and optimal (expected) rewards\n - sampling: because of chance, you have to try things repeatedly\n\nDirect Evaluation\n - compute values for each state under π\n - average together observed sample values\n - (good) easy to understand + doesn’t require T, R + eventually converge\n - (bad) wastes information about state connections + long time to learn\n\nSample-Based Policy Evaluation\n - take samples of outcomes s’ by doing the action and average\n - problem: cannot rewind time and keep visiting state s\n\nTemporal Difference Learning (fixed policy)\n - update V(s) each time we experience a transition (s,a,s’,r)\n - likely outcomes s’ will contribute updates more often\n - decreasing learning rate (α) can give converging averages\n\nProblems with TD Value Learning\n - cannot turn values into a new policy, no knowledge of T\n - solution: learn Q values directly, not values\n\nQ Learning (off-policy learning)\n - Q(s,a)=(1-α)Q(s,a)+(α)sample\n\nQ Learning Properties\n - converges to optimal policy\n - have to explore enough + eventually make the learning rate small enough\n\nε-Greedy Exploration\n - with (small) probability ε, act randomly\n - with (large) probability 1-ε, act on current policy\n - eventually explore the space, but inefficient\n - lower ε over time\n\nExploration Functions\n - takes a value estimate u and a visit count n, and returns an optimistic utility\n - e.g. f(u,n) = u + k/n\n\nApproximate Q-Learning\n - advantage: our experience is summed up in a few powerful numbers\n - disadvantage: states may share features but actually be very different in value\n\nApproximate Q-Learning Update\n - transition = (s,a,r,s’)\n - only adjust weights of active features\n\n\n\n## Module 9 Definitions\n* **Absorbing state** - state where no actions have any effect and no rewards are received\n* **Active rl** - the agent must also figure out what to do (exploration)\n* **Apprenticeship learning** - training a learning agent using demonstrations rather than rewards\n* **Bernoulli bandit** - each arm produces a reward of 0 or 1 with a fixed but unknown probability μi\n* **Function approximation** - construct a compact approx of the true utility function or Q-function\n* **Model-based rl** - agent uses a transition model to interpret reward signals and make decisions\n* **Model-free rl** - the agent neither knows nor learns a transition model for the environment\n* **Off-policy** - evaluate & improve a policy that is different from the one used for action selection\n* **On-policy** - evaluate and improve the same policy which is being used to select actions\n* **Passive rl** - the agent’s policy is fixed and the task is to learn the utilities of states\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n \n\n## AIMA Chapter 12.2 Basic Probability Notation\n \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n \n\n## AIMA Chapter 12.4 Independence\n \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n \n\n## AIMA Chapter 12.6 Naive Bayes Models\n \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n \n\n## SLP Chapter 3.1 N-Grams\n \n\n## SLP Chapter 3.2 Evaluating Language Models\n \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n \n\n## SLP Chapter 3.4 Generalization and Zeros\n \n\n## SLP Chapter 3.5 Smoothing\n \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n \n\n## Module 10 Lecture Notes\n \nBayes Rule\n - P(a│b)=P(a,b)/(P(b)), \n - P(a│b)∙P(b)=P(a,b)\n - P(a│b)=(P(b|a))/(p(b))∙P(a)\n\nInclusion–Exclusion Principle: P(a∨b) = P(a) + P(b) – P(a∧b)\n\nPerplexity: inverse probability of a sentence, normalized by the number of words\n\n\n## Module 10 Definitions\n* **Extrinsic evaluation** - measure model in an application and measure how much it improves\n* **Factored representation** - possible world is represented by a set of variable/value pairs\n* **Intrinsic evaluation** - one that measures the quality of a model independent of any application\n* **Laziness** - it is too much work to list the complete set of antecedents or consequents\n* **Theoretical ignorance** - no complete theory for the domain.\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n \n\n## Module 11 Lecture Notes\n\nIndependence\n - P(x│y)=P(x),P(y│x)=P(y)\n - P(x ∧ y)=P(x)P(y)\n\nConditional Independence\n - x is conditionally independent of y given z\n - P(x,y│z)=P(x│z)P(y│z) or P(x│z,y)=P(x|z)\n\nSize of Bayes’ Net\n - how big is a joint distribution over N Boolean variables? 2^N\n - how big is an N-node net if nodes have up to k parents? O(N∙2^(k+1))\n\nD-Separation\n - a path is active if each triple is active\n - no active paths = independence\n - gray node = information is given \n\nInference by Enumeration vs. Variable Elimination\n - inference slow: join up the whole joint distribution before you sum out hidden variables\n - advanced technique: variable elimination\n - interleave joining and marginalizing\n - still NP-hard, but usually much faster than inference by enumeration\n - clustering algorithms / join tree algorithms - run time O(n)\n\n\n## Module 11 Definitions\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n \n\n## AIMA Chapter 5.5 Stochastic Games\n \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 \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n \n\n## SLP Chapter 4.3 Worked Example\n \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n \n\n## SLP Chapter 4.9 Statistical Significance Testing\n \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n \n\n## Module 12 Lecture Notes\n\nNaïve Bayes (Generative Model)\n - Assume all features are independent effects of the label\n\nLaplace Smoothing\n - P_LAP (x)=(c(x)+k)/(N+k|X|), k is the strength of the prior\n - P_(LAP,k) (x|y)=(c(x,y)+k)/(c(y)+k|X|) Laplace for conditionals, smooth each condition independently\n\nLinear Interpolation (smoothing)\n - P_LIN (x│y)=αP(x│y)+(1.0-α)P(x)\n\nLearning: Multiclass Perceptron\n - predict with current weights, if correct no change.\n - if wrong: lower score of wrong answer, raise score of right answer\n\nMistake Bound\n - the maximum number of mistakes related to the margin or degree of separability\n - mistakes < k/δ^2  large margin = easily separable = fewer mistakes (vice versa)\n\nMIRA\n - choose an update size that fixes the current mistake but minimizes the change to w\n\nSupport Vector Machines (SVM)\n - maximizing the margin, only support vectors matter\n - support vector machines (SVMs) find the separator with max margin\n\n\n\n## Module 12 Definitions\n* **Decision tree pruning** - eliminate nodes that are not clearly relevant (combats overfitting)\n* **Generative classifiers** - build a model of how a class could generate some input data\n* **Reinforcement learning** - agent learns from a series of reinforcements: rewards & punishments\n* **Supervised learning** - the agent observes input-output pairs and learns a function that maps from input to output\n* **Unsupervised learning** - the agent learns patterns in the input without any explicit feedback\n* **Variance** - the amount of change in the hypothesis due to fluctuation in the training data\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n \n\n## SLP Chapter 5.3 The cross-entropy loss function\n \n\n## SLP Chapter 5.4 Gradient Descent\n \n\n## SLP Chapter 5.5 Regularization\n \n\n## SLP Chapter 5.6 Multinomial logistic regression\n \n\n## SLP Chapter 7.1 Units\n \n\n## SLP Chapter 7.2 The XOR problem\n \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n \n\n## SLP Chapter 7.6 Training Neural Nets\n \n\n## SLP Chapter 7.7 Training the neural language model\n \n\n## Module 13 Lecture Notes\n \nML Classifier Components \n - a feature representation of the input, [x1,x2,...,xn]. \n - a classification function that computes the estimated class y ̂ via p(y|x). \n - an objective function for learning, usually involving minimizing error on training.\n - an algorithm for optimizing the objective function.\n\nCross Entropy Loss\n - For one observation x, maximize the probability of the correct label p(y|x).\n - P(y|x) = y ̂^y (1-y ̂ )^(1-y)\n - If y=1, then p(y|x) = y ̂\n - If y=0, then p(y|x) = 1-y ̂\n\nRegularization\n - penalize large weights by adding regularization term to the loss function\n - L2 regularization (Ridge Regression) is a quadratic function of L2 regularization the weight values, \n - named because it uses the (square of the) L2 norm of the weight values.\n - L1 regularization (Lasso Regression) is a linear function of the weight values\n\n\n## Module 13 Definitions\n* **Dropout** - randomly dropping some units and their connections from the network during training\n* **Discriminative model** - attempts to directly compute P(y|x)\n* **Generative model** - how to generate the features if we knew it was of class c\n* **Hard classification task** - only one class is the correct one\n* **Representation learning** - learn features automatically in an unsupervised way from the input\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n \n\n## SLP Chapter 9.3 RNNs as Language Models\n \n\n## SLP Chapter 6.1 Lexical Semantics\n \n\n## SLP Chapter 6.2 Vector Semantics\n \n\n## SLP Chapter 6.3 Words and Vectors\n \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n \n\n## SLP Chapter 6.8 Word2vec\n \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n \n\n## SLP Chapter 6.11 Bias and Embeddings\n \n\n## SLP Chapter 6.12 Evaluating Vector Models\n \n\n## Module 13 Lecture Notes\n \nML Classifier Components \n - a feature representation of the input, [x1,x2,...,xn]. \n - a classification function that computes the estimated class y ̂ via p(y|x). \n - an objective function for learning, usually involving minimizing error on training.\n - an algorithm for optimizing the objective function.\n\nCross Entropy Loss\n - For one observation x, maximize the probability of the correct label p(y|x).\n - P(y|x) = y ̂^y (1-y ̂ )^(1-y)\n - If y=1, then p(y|x) = y ̂\n - If y=0, then p(y|x) = 1-y ̂\n\nRegularization\n - penalize large weights by adding regularization term to the loss function\n - L2 regularization (Ridge Regression) is a quadratic function of L2 regularization the weight values, \n - named because it uses the (square of the) L2 norm of the weight values.\n - L1 regularization (Lasso Regression) is a linear function of the weight values\n\n\n## Module 14 Definitions\n* **Distributional hypothesis** - the link between similarity in how words are distributed and similarity in what they mean\n* **First-order co-occurrence** - two words that are typically nearby each other\n* **Principle of contrast** - difference in linguistic form associated with some difference in meaning\n* **Second-order co-occurrence** - two words have similar second-order co-occurrence neighbors\n",
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