{
  "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/",
          "height": 0
        },
        "outputId": "fa94c602-6fdc-4b5a-c42d-2a133b693f37"
      },
      "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": 19,
      "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/",
          "height": 0
        },
        "outputId": "217a200d-98c5-43ba-a598-956a771cdf7a"
      },
      "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": 20,
      "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": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZZXyrV-xuAp"
      },
      "source": [
        "# Module 8 - Markov Decision Process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2NfE1CKxzS-"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 16, “Making Simple Decisions” (16.1-16.3)\n",
        "\n",
        "sample_equation = r\"\"\" $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \"\"\"\n",
        "\n",
        "chapter16_1 = \"\"\" Chapter 16.1 summary. \"\"\"  + sample_equation\n",
        "chapter16_2 =  \"\"\" Your summary here \"\"\"\n",
        "chapter16_3 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter17_2 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\" \n",
        "MDPs need a set of states, a set of actions, trainsition function T(s, a, s')\n",
        "that returns probability that taking action a from s will lead to s',\n",
        "reward function of R(s, a, s'), initial state, and one or more terminal states.\n",
        "discount factor is chosen to limit the rewards in the future at an exponential rate.\n",
        "V(s)= max_a Q(s, a).\n",
        "Q*(s, a) = sum_s' T(s, a, s')[R(s, a, s') + gamma* V(s')].\n",
        "Value Iteration - start with V(s) = 0, then update V_{k+1}(s) = max_a Q*(s, a),\n",
        "then set V(s) = V_{k + 1}(s) and increment k. Takes O(S^2A).\n",
        "Policy evaluation - V^pi(s) = sum T(s, pi(s), s')[R(s, pi(s), s') + gamma * V(s')].\n",
        "updates to V values take O(S^2) time since no longer need to search max of actions.\n",
        "Can also be solved as a system of linear equations.\n",
        "Updates of policy can be done by looking at max Q value. \n",
        "Policy iteration involves calculating utilities for some policy, and then\n",
        "updating the v values, repeating process until convergence.\n",
        "Utilities should reflect the preferences of the agent.\n",
        "Preference - A > B, indifference A ~ B.\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\" : \"sum of the rewards of the state action pairs in the history\",\n",
        "\"expected utility\" : \"expected value of being in a certain state s\",\n",
        "\"policy\": \"given you are in state s, best action to take, pi(s) - pi*(s) is optimal\",\n",
        "\"stationary preferences\": \"if [a, a2, a3] > [b, b2, b3] then [r, a, a2, a3] > [r, b, b2,b3]\",\n",
        "\"Axiom of Transitivity\": \"A > B and B > C then A > C\",\n",
        "\"Orderability\": \"You either prefer a to b, b to a, or are indifferent\",\n",
        "\"Continuity\": \"A > B > C then there is some p such that L[p:A, 1-p:C] ~ B\",\n",
        "\"Substitutability\": \"A ~ B then L[p:A 1-p:C] ~ L[p:B 1-p:C]\",\n",
        "\"Monotonicity\": \"A > B then p >= q <=> L[p:A 1-p:B] >= L[q:A 1-q:B]\",\n",
        "\"MEU\": \"an agent tries to maximize its expected utility\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 22,
      "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": 809
        },
        "outputId": "fccf66e3-5fe7-46d1-8e54-8795d5a1103c"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Your summary here \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n \nMDPs need a set of states, a set of actions, trainsition function T(s, a, s')\nthat returns probability that taking action a from s will lead to s',\nreward function of R(s, a, s'), initial state, and one or more terminal states.\ndiscount factor is chosen to limit the rewards in the future at an exponential rate.\nV(s)= max_a Q(s, a).\nQ*(s, a) = sum_s' T(s, a, s')[R(s, a, s') + gamma* V(s')].\nValue Iteration - start with V(s) = 0, then update V_{k+1}(s) = max_a Q*(s, a),\nthen set V(s) = V_{k + 1}(s) and increment k. Takes O(S^2A).\nPolicy evaluation - V^pi(s) = sum T(s, pi(s), s')[R(s, pi(s), s') + gamma * V(s')].\nupdates to V values take O(S^2) time since no longer need to search max of actions.\nCan also be solved as a system of linear equations.\nUpdates of policy can be done by looking at max Q value. \nPolicy iteration involves calculating utilities for some policy, and then\nupdating the v values, repeating process until convergence.\nUtilities should reflect the preferences of the agent.\nPreference - A > B, indifference A ~ B.\n \n\n## Module 8 Definitions\n* **Utility function** - sum of the rewards of the state action pairs in the history\n* **Expected utility** - expected value of being in a certain state s\n* **Policy** - given you are in state s, best action to take, pi(s) - pi*(s) is optimal\n* **Stationary preferences** - if [a, a2, a3] > [b, b2, b3] then [r, a, a2, a3] > [r, b, b2,b3]\n* **Axiom of transitivity** - A > B and B > C then A > C\n* **Orderability** - You either prefer a to b, b to a, or are indifferent\n* **Continuity** - A > B > C then there is some p such that L[p:A, 1-p:C] ~ B\n* **Substitutability** - A ~ B then L[p:A 1-p:C] ~ L[p:B 1-p:C]\n* **Monotonicity** - A > B then p >= q <=> L[p:A 1-p:B] >= L[q:A 1-q:B]\n* **Meu** - an agent tries to maximize its expected utility\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 9 - Reinforcement Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# Russell and Norvig, AIMA Section 17.3 “Bandit Problems”\n",
        "chapter17_3 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_2 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_3 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_4 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_5 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\" \n",
        " Similar to MDPs but the probability associated with each action is unknown to start\n",
        " consists of states, actions, model T(s, a, s'), reward R(s, a, s').\n",
        " model based learning attempts to approximate the probability distribution based\n",
        " on what it has learned. Then, you can use the MDP processes once T has been estimated.\n",
        "Model based learning trains by viewing epsiodes and then apporximating T and R.\n",
        "Model free learning attempts to generate the value of being in each state without\n",
        "calculating reward or transition functions.\n",
        "Direct evaluation - act according to pi, every time you visit a state write down\n",
        "the sum of discounted values, and average the samples.\n",
        "However, this does not leverage information about states being connected.\n",
        "Temporal Difference - policy is fixed and sample = R(s, pi(s), s') + gamma * V^pi (s')\n",
        "then update V(s) = (1 - a)(V(s)) + a*sample (similar to stochastic gradient descent).\n",
        "This process exponentially forgets the past results.\n",
        "Q value iteration - Q(s, a) = (1 - a)Q(s, a) + a[R(s, a, s') + gamma * max_a' Q(s', a'].\n",
        "Learns optimal policy if you explore enough, set the learning rate small enough, but not too quickly.\n",
        "epsilon-greedy - randomly explore a new path epsilon percent of the time.\n",
        "exploration function f(u, n) = u + k / n, which incentivises exploring not very explored states.\n",
        "The q update then becomes a[R(s, a, s') + max_a' f(Q(s', a') + N(s', a'))], where N is number of times visited.\n",
        "Regret measures the total mistake cost, or optimal policy rewards - current policy rewards\n",
        "Use feature respresentation to represent a V value as the sum of weights * features.\n",
        "transition = (s, a, r, s'), difference = [r + gamma * max_a' Q(s', a')] - Q(s, a)\n",
        "w(i) = w(i) + a(difference) (also like stochastic gradient descent).\n",
        "\n",
        "\n",
        " \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"exploration\" : \"trying new paths to get more information\",\n",
        "\"exploitation\": \"stick with the currently known path\",\n",
        "\"Passive Reinforcement Learning\": \"olicy is already decided for us\",\n",
        "\"Exploration function\": \"Function to determine which actions are better when exploring\",\n",
        "}"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "cellView": "form",
        "outputId": "4d0c2177-5fea-42bd-8f7a-fe8a9eecb3d6"
      },
      "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": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Your summary here \n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Your summary here \n\n## Module 9 Lecture Notes\n \n Similar to MDPs but the probability associated with each action is unknown to start\n consists of states, actions, model T(s, a, s'), reward R(s, a, s').\n model based learning attempts to approximate the probability distribution based\n on what it has learned. Then, you can use the MDP processes once T has been estimated.\nModel based learning trains by viewing epsiodes and then apporximating T and R.\nModel free learning attempts to generate the value of being in each state without\ncalculating reward or transition functions.\nDirect evaluation - act according to pi, every time you visit a state write down\nthe sum of discounted values, and average the samples.\nHowever, this does not leverage information about states being connected.\nTemporal Difference - policy is fixed and sample = R(s, pi(s), s') + gamma * V^pi (s')\nthen update V(s) = (1 - a)(V(s)) + a*sample (similar to stochastic gradient descent).\nThis process exponentially forgets the past results.\nQ value iteration - Q(s, a) = (1 - a)Q(s, a) + a[R(s, a, s') + gamma * max_a' Q(s', a'].\nLearns optimal policy if you explore enough, set the learning rate small enough, but not too quickly.\nepsilon-greedy - randomly explore a new path epsilon percent of the time.\nexploration function f(u, n) = u + k / n, which incentivises exploring not very explored states.\nThe q update then becomes a[R(s, a, s') + max_a' f(Q(s', a') + N(s', a'))], where N is number of times visited.\nRegret measures the total mistake cost, or optimal policy rewards - current policy rewards\nUse feature respresentation to represent a V value as the sum of weights * features.\ntransition = (s, a, r, s'), difference = [r + gamma * max_a' Q(s', a')] - Q(s, a)\nw(i) = w(i) + a(difference) (also like stochastic gradient descent).\n\n\n \n\n## Module 9 Definitions\n* **Exploration** - trying new paths to get more information\n* **Exploitation** - stick with the currently known path\n* **Passive reinforcement learning** - olicy is already decided for us\n* **Exploration function** - Function to determine which actions are better when exploring\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 10 - Probabilities and Language Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 12 “Quantifying Uncertainty” (Sections 12.1-12.7)\n",
        "chapter12_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_6 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Jurafsky and Martin, Speech and Language Processing Chapter 3 “N-Gram Language Models”  (Sections 3.1-3.5,3.7)\n",
        "SLP_chapter3_1 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" \n",
        "Inference by enumeration - have 3 sets of rvs - evidence (things we know), query (things we want to find),\n",
        "and hidden (things in our table that we dont necessarily care about) so find P(Q|e1, e2 ...).\n",
        "select entires consistent with evidence, sum out hidden variables, normalize.\n",
        "Chain rule - P(a, b, c, d) = p(a)p(b|a)...\n",
        "markov assumption - probability of moving to the next state only depends on the previous k states\n",
        " \"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"probabalistic model\" : \"joint diistribution over a set of random variables\",\n",
        "\"marginal distribution\": \"sub tables that eliminate variables - P(t) = sum_s P(t, s)\",\n",
        "}"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1130
        },
        "cellView": "form",
        "outputId": "2f9efb20-30a2-48df-d854-86eb00ef3aab"
      },
      "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": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n \nInference by enumeration - have 3 sets of rvs - evidence (things we know), query (things we want to find),\nand hidden (things in our table that we dont necessarily care about) so find P(Q|e1, e2 ...).\nselect entires consistent with evidence, sum out hidden variables, normalize.\nChain rule - P(a, b, c, d) = p(a)p(b|a)...\nmarkov assumption - probability of moving to the next state only depends on the previous k states\n \n\n## Module 10 Definitions\n* **Probabalistic model** - joint diistribution over a set of random variables\n* **Marginal distribution** - sub tables that eliminate variables - P(t) = sum_s P(t, s)\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, Russell and Norvig, AIMA Chapter 13 “Probabilistic Reasoning” (13.1, 13.2, 13.3)\n",
        "chapter13_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\"\n",
        "bayes net assumption is that a state is only dependent on its parents.\n",
        "causal chain - x -> y -> z. z is conditionally independent of x given y.\n",
        "common cause - x <- y -> z. given y, z and x are conditionally indpendent.\n",
        "common effect - x -> y <- z. x and z are independent, but given z they are not \n",
        "conditionally independent as it increases the likelihood one occured.\n",
        "In order to check if x and y are conditionally independent given z, check all \n",
        "undirected path and if there are no active trails they are independent.\n",
        "active if a->b->c and b is unobserved, a <- b -> c and b is unobserved,\n",
        "a -> b <- c and one of bs descendants (or itself) is observed.\n",
        "\n",
        "\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"independence\" : \"P(x, y) = P(x)P(y)\", \n",
        "\"conditional independence\": \"P(x,y |z) = P(x|z)P(y|z)\"   \n",
        "}\n"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "cellView": "form",
        "outputId": "7f451e34-c76a-4602-a6f8-1d380ebe67ca"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n\nbayes net assumption is that a state is only dependent on its parents.\ncausal chain - x -> y -> z. z is conditionally independent of x given y.\ncommon cause - x <- y -> z. given y, z and x are conditionally indpendent.\ncommon effect - x -> y <- z. x and z are independent, but given z they are not \nconditionally independent as it increases the likelihood one occured.\nIn order to check if x and y are conditionally independent given z, check all \nundirected path and if there are no active trails they are independent.\nactive if a->b->c and b is unobserved, a <- b -> c and b is unobserved,\na -> b <- c and one of bs descendants (or itself) is observed.\n\n\n \n\n## Module 11 Definitions\n* **Independence** - P(x, y) = P(x)P(y)\n* **Conditional independence** - P(x,y |z) = P(x|z)P(y|z)\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 12 - Naive Bayes and Perceptrons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 19 “Learning from Examples” (Sections 19.1-19.6)\n",
        "\n",
        "chapter19_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter19_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Speech and Language Processing Chapter 4 “Naive Bayes and Sentiment Classification”\n",
        "slp_chapter4_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_7 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_9 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter4_10 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\"\n",
        "Naive bayes- assume all features are independent effects of the label.\n",
        "p(Y | f1 f2 f3 ...) is proportional to P(Y) * product(P(fij | Y))\n",
        "total number of parameters in linear in the number of features.\n",
        "However, it makes strong assumptions that may not be true.\n",
        "First get joint prob of label and features for each label. Then sum to get the \n",
        "probability of the evidence. Lastly, divide by the probability of the features \n",
        "to get the probability of (Y|f1, f2, f3 ...).\n",
        "Avoid 0 probabilities using laplace smoothing (pretend you saw an event 1 more time than actual)\n",
        "Interpolation - a P(x|y) + (1 - a)P(x).\n",
        "Perceptron takes in inputs and applies weights to them, sums them up, and then applies an activation function\n",
        "to them. Bias is the y intercept for the perceptron\n",
        "multi class add to true label w = w + f, subtract from predicted label w = w - f\n",
        "MIRA - wy = wy' -tf(x) in order to prevent thrashing.\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"classification\" : \"given features x predict label y\",\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1366
        },
        "cellView": "form",
        "outputId": "1431803f-ea58-4439-804e-da0701a30adf"
      },
      "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": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n\nNaive bayes- assume all features are independent effects of the label.\np(Y | f1 f2 f3 ...) is proportional to P(Y) * product(P(fij | Y))\ntotal number of parameters in linear in the number of features.\nHowever, it makes strong assumptions that may not be true.\nFirst get joint prob of label and features for each label. Then sum to get the \nprobability of the evidence. Lastly, divide by the probability of the features \nto get the probability of (Y|f1, f2, f3 ...).\nAvoid 0 probabilities using laplace smoothing (pretend you saw an event 1 more time than actual)\nInterpolation - a P(x|y) + (1 - a)P(x).\nPerceptron takes in inputs and applies weights to them, sums them up, and then applies an activation function\nto them. Bias is the y intercept for the perceptron\nmulti class add to true label w = w + f, subtract from predicted label w = w - f\nMIRA - wy = wy' -tf(x) in order to prevent thrashing.\n \n\n## Module 12 Definitions\n* **Classification** - given features x predict label y\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" \n",
        "Feature representation - for each input observation, represent it as a vector of \n",
        "features. \n",
        "Logistic regression - restrict output values by applying sigmoid function\n",
        "1 / (1 + e^-z), where z = the output of the perceptron. \n",
        "mean squared loss = .5 * (y' - y)^2. \n",
        "cross entroppy - P(y|x) = y'^y (1 - y'), then minimize the negative log probability.  \n",
        "stochastic gradient descent - w_t+1 = w_t - n (d / dw)f(x; w)\n",
        "theta_t+1 = theta_t - n * partial derivative (L(f(x;w))).\n",
        "With repect to one weight, the partial derivate of CE is y' / (sigma(wx + b) - y) * x_i.\n",
        "softmax = e^(z_j) / sum(e^z_j)\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1130
        },
        "cellView": "form",
        "outputId": "404d0308-1936-48b0-a3c4-cee5e2db3ac0"
      },
      "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 Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n \nFeature representation - for each input observation, represent it as a vector of \nfeatures. \nLogistic regression - restrict output values by applying sigmoid function\n1 / (1 + e^-z), where z = the output of the perceptron. \nmean squared loss = .5 * (y' - y)^2. \ncross entroppy - P(y|x) = y'^y (1 - y'), then minimize the negative log probability.  \nstochastic gradient descent - w_t+1 = w_t - n (d / dw)f(x; w)\ntheta_t+1 = theta_t - n * partial derivative (L(f(x;w))).\nWith repect to one weight, the partial derivate of CE is y' / (sigma(wx + b) - y) * x_i.\nsoftmax = e^(z_j) / sum(e^z_j)\n \n\n## Module 13 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 6 “Vector Semantics and Embeddings” (6.1-6.4, 6.8, 6.10-6.12)\n",
        "\n",
        "slp_chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\" \n",
        "mle probability - p (w_i+1 = m | w_i - k : i) = # w_(i - k : i  + 1) / # w_(i - k : i)\n",
        "nerual network - train a model to predict for each word, the probability the word follows the\n",
        "input (k words).  \n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 997
        },
        "outputId": "b035ff9b-5aed-4845-d8e7-330500c6d5a6"
      },
      "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": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n \nFeature representation - for each input observation, represent it as a vector of \nfeatures. \nLogistic regression - restrict output values by applying sigmoid function\n1 / (1 + e^-z), where z = the output of the perceptron. \nmean squared loss = .5 * (y' - y)^2. \ncross entroppy - P(y|x) = y'^y (1 - y'), then minimize the negative log probability.  \nstochastic gradient descent - w_t+1 = w_t - n (d / dw)f(x; w)\ntheta_t+1 = theta_t - n * partial derivative (L(f(x;w))).\nWith repect to one weight, the partial derivate of CE is y' / (sigma(wx + b) - y) * x_i.\nsoftmax = e^(z_j) / sum(e^z_j)\n \n\n## Module 14 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX7lbnJd4sT5"
      },
      "source": [
        "# Your Exam Notes\n",
        "\n",
        "Here are your full set of exam notes that you can use on your midterm.  Choose **Runtime > Run all**, then your notes will be displayed.\n",
        "\n",
        "If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print:\n",
        "* Run the cell below. \n",
        "* Click its \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6545
        },
        "id": "T3cm1WV542_a",
        "outputId": "13edc46f-6037-4bab-fa8f-ea2959f8e89c"
      },
      "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": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Chapter 16.1 summary.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## AIMA Chapter 16.3 Utility Functions\n Your summary here \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n Your summary here \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n \nMDPs need a set of states, a set of actions, trainsition function T(s, a, s')\nthat returns probability that taking action a from s will lead to s',\nreward function of R(s, a, s'), initial state, and one or more terminal states.\ndiscount factor is chosen to limit the rewards in the future at an exponential rate.\nV(s)= max_a Q(s, a).\nQ*(s, a) = sum_s' T(s, a, s')[R(s, a, s') + gamma* V(s')].\nValue Iteration - start with V(s) = 0, then update V_{k+1}(s) = max_a Q*(s, a),\nthen set V(s) = V_{k + 1}(s) and increment k. Takes O(S^2A).\nPolicy evaluation - V^pi(s) = sum T(s, pi(s), s')[R(s, pi(s), s') + gamma * V(s')].\nupdates to V values take O(S^2) time since no longer need to search max of actions.\nCan also be solved as a system of linear equations.\nUpdates of policy can be done by looking at max Q value. \nPolicy iteration involves calculating utilities for some policy, and then\nupdating the v values, repeating process until convergence.\nUtilities should reflect the preferences of the agent.\nPreference - A > B, indifference A ~ B.\n \n\n## Module 8 Definitions\n* **Utility function** - sum of the rewards of the state action pairs in the history\n* **Expected utility** - expected value of being in a certain state s\n* **Policy** - given you are in state s, best action to take, pi(s) - pi*(s) is optimal\n* **Stationary preferences** - if [a, a2, a3] > [b, b2, b3] then [r, a, a2, a3] > [r, b, b2,b3]\n* **Axiom of transitivity** - A > B and B > C then A > C\n* **Orderability** - You either prefer a to b, b to a, or are indifferent\n* **Continuity** - A > B > C then there is some p such that L[p:A, 1-p:C] ~ B\n* **Substitutability** - A ~ B then L[p:A 1-p:C] ~ L[p:B 1-p:C]\n* **Monotonicity** - A > B then p >= q <=> L[p:A 1-p:B] >= L[q:A 1-q:B]\n* **Meu** - an agent tries to maximize its expected utility\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Your summary here \n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n Your summary here \n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n Your summary here \n\n## Module 9 Lecture Notes\n \n Similar to MDPs but the probability associated with each action is unknown to start\n consists of states, actions, model T(s, a, s'), reward R(s, a, s').\n model based learning attempts to approximate the probability distribution based\n on what it has learned. Then, you can use the MDP processes once T has been estimated.\nModel based learning trains by viewing epsiodes and then apporximating T and R.\nModel free learning attempts to generate the value of being in each state without\ncalculating reward or transition functions.\nDirect evaluation - act according to pi, every time you visit a state write down\nthe sum of discounted values, and average the samples.\nHowever, this does not leverage information about states being connected.\nTemporal Difference - policy is fixed and sample = R(s, pi(s), s') + gamma * V^pi (s')\nthen update V(s) = (1 - a)(V(s)) + a*sample (similar to stochastic gradient descent).\nThis process exponentially forgets the past results.\nQ value iteration - Q(s, a) = (1 - a)Q(s, a) + a[R(s, a, s') + gamma * max_a' Q(s', a'].\nLearns optimal policy if you explore enough, set the learning rate small enough, but not too quickly.\nepsilon-greedy - randomly explore a new path epsilon percent of the time.\nexploration function f(u, n) = u + k / n, which incentivises exploring not very explored states.\nThe q update then becomes a[R(s, a, s') + max_a' f(Q(s', a') + N(s', a'))], where N is number of times visited.\nRegret measures the total mistake cost, or optimal policy rewards - current policy rewards\nUse feature respresentation to represent a V value as the sum of weights * features.\ntransition = (s, a, r, s'), difference = [r + gamma * max_a' Q(s', a')] - Q(s, a)\nw(i) = w(i) + a(difference) (also like stochastic gradient descent).\n\n\n \n\n## Module 9 Definitions\n* **Exploration** - trying new paths to get more information\n* **Exploitation** - stick with the currently known path\n* **Passive reinforcement learning** - olicy is already decided for us\n* **Exploration function** - Function to determine which actions are better when exploring\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n \nInference by enumeration - have 3 sets of rvs - evidence (things we know), query (things we want to find),\nand hidden (things in our table that we dont necessarily care about) so find P(Q|e1, e2 ...).\nselect entires consistent with evidence, sum out hidden variables, normalize.\nChain rule - P(a, b, c, d) = p(a)p(b|a)...\nmarkov assumption - probability of moving to the next state only depends on the previous k states\n \n\n## Module 10 Definitions\n* **Probabalistic model** - joint diistribution over a set of random variables\n* **Marginal distribution** - sub tables that eliminate variables - P(t) = sum_s P(t, s)\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n\nbayes net assumption is that a state is only dependent on its parents.\ncausal chain - x -> y -> z. z is conditionally independent of x given y.\ncommon cause - x <- y -> z. given y, z and x are conditionally indpendent.\ncommon effect - x -> y <- z. x and z are independent, but given z they are not \nconditionally independent as it increases the likelihood one occured.\nIn order to check if x and y are conditionally independent given z, check all \nundirected path and if there are no active trails they are independent.\nactive if a->b->c and b is unobserved, a <- b -> c and b is unobserved,\na -> b <- c and one of bs descendants (or itself) is observed.\n\n\n \n\n## Module 11 Definitions\n* **Independence** - P(x, y) = P(x)P(y)\n* **Conditional independence** - P(x,y |z) = P(x|z)P(y|z)\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n Your summary here \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n Your summary here \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Your summary here \n\n## AIMA Chapter 5.5 Stochastic Games\n Your summary here \n\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n Your summary here \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n Your summary here \n\n## SLP Chapter 4.1 Naive Bayes Classifiers\n Your summary here \n\n## SLP Chapter 4.2 Training the Naive Bayes Classifier\n Your summary here \n\n## SLP Chapter 4.3 Worked Example\n Your summary here \n\n## SLP Chapter 4.4 Optimizing for Sentiment Analysis\n Your summary here \n\n## SLP Chapter 4.5 Naive Bayes for other text classification tasks\n Your summary here \n\n## SLP Chapter 4.6 Naive Bayes as a Language Model\n Your summary here \n\n## SLP Chapter 4.7 Evaluation: Precision, Recall, F-measure\n Your summary here \n\n## SLP Chapter 4.8 Test sets and Cross-validation\n Your summary here \n\n## SLP Chapter 4.9 Statistical Significance Testing\n Your summary here \n\n## SLP Chapter 4.10 Avoiding Harms in Classification\n Your summary here \n\n## Module 12 Lecture Notes\n\nNaive bayes- assume all features are independent effects of the label.\np(Y | f1 f2 f3 ...) is proportional to P(Y) * product(P(fij | Y))\ntotal number of parameters in linear in the number of features.\nHowever, it makes strong assumptions that may not be true.\nFirst get joint prob of label and features for each label. Then sum to get the \nprobability of the evidence. Lastly, divide by the probability of the features \nto get the probability of (Y|f1, f2, f3 ...).\nAvoid 0 probabilities using laplace smoothing (pretend you saw an event 1 more time than actual)\nInterpolation - a P(x|y) + (1 - a)P(x).\nPerceptron takes in inputs and applies weights to them, sums them up, and then applies an activation function\nto them. Bias is the y intercept for the perceptron\nmulti class add to true label w = w + f, subtract from predicted label w = w - f\nMIRA - wy = wy' -tf(x) in order to prevent thrashing.\n \n\n## Module 12 Definitions\n* **Classification** - given features x predict label y\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n Your summary here \n\n## SLP Chapter 7.3 Feedforward Neural Networks\n Your summary here \n\n## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n Your summary here \n\n## SLP Chapter 7.5 Feedforward Neural Language Modeling\n Your summary here \n\n## SLP Chapter 7.6 Training Neural Nets\n Your summary here \n\n## SLP Chapter 7.7 Training the neural language model\n Your summary here \n\n## Module 13 Lecture Notes\n \nFeature representation - for each input observation, represent it as a vector of \nfeatures. \nLogistic regression - restrict output values by applying sigmoid function\n1 / (1 + e^-z), where z = the output of the perceptron. \nmean squared loss = .5 * (y' - y)^2. \ncross entroppy - P(y|x) = y'^y (1 - y'), then minimize the negative log probability.  \nstochastic gradient descent - w_t+1 = w_t - n (d / dw)f(x; w)\ntheta_t+1 = theta_t - n * partial derivative (L(f(x;w))).\nWith repect to one weight, the partial derivate of CE is y' / (sigma(wx + b) - y) * x_i.\nsoftmax = e^(z_j) / sum(e^z_j)\n \n\n## Module 13 Definitions\n* **Concept** - definition\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 13 Lecture Notes\n \nFeature representation - for each input observation, represent it as a vector of \nfeatures. \nLogistic regression - restrict output values by applying sigmoid function\n1 / (1 + e^-z), where z = the output of the perceptron. \nmean squared loss = .5 * (y' - y)^2. \ncross entroppy - P(y|x) = y'^y (1 - y'), then minimize the negative log probability.  \nstochastic gradient descent - w_t+1 = w_t - n (d / dw)f(x; w)\ntheta_t+1 = theta_t - n * partial derivative (L(f(x;w))).\nWith repect to one weight, the partial derivate of CE is y' / (sigma(wx + b) - y) * x_i.\nsoftmax = e^(z_j) / sum(e^z_j)\n \n\n## Module 14 Definitions\n* **Concept** - definition\n",
            "text/plain": [
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