{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Template_for CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de2008a8-7978-46f0-e349-2e0082ef1f7f"
      },
      "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": 2,
      "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": "976d2aad-dd3d-4840-9568-df3e077d519f"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": 4,
      "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 = \"\"\"The principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\n",
        " \n",
        "To find a solution to an MDP, you need to define the following things:\n",
        "• Asetofstatess∈S\n",
        "• Asetofactionsa∈A\n",
        "• A transition function T(s, a, s’)\n",
        "• Probability that executing action a in s will lead to s’ P(s’ | s, a)\n",
        "• The probability is called the model\n",
        "• A reward function R(s, a, s’)\n",
        "• Sometimes just R(s) or R(s’)\n",
        "• An initial state s0\n",
        "• Optionally, one or more terminal states\n",
        "In search problems a solution was a plan，Instead, the solution to an MDP is a policy. \"\"\"  + sample_equation\n",
        "chapter16_2 =  \"\"\" we list six constraints that we require any reasonable preference relation to obey:\n",
        "1.ORDERABILITY: A>B,A<B or A~B\n",
        "2.TRANSITIVITY: A>B,B>C -> A>C\n",
        "3.CONTINUITY:A>B>C, there is p, [p*A+(1-p)*C] ~ B\n",
        "4.SUBSTITUTABILITY:A~B,[p*A+(1-p)*C]~[p*B+(1-p)*C]\n",
        "5.MONOTONICITY: A>B, (p>q) <-> [p*A+(1-p)*B]>[q*A+(1-q)*B]\n",
        "6.DECOMPOSABILITY:[p*A+(1-p)*(q*B+(1-q)*C)]~[p*A+(1-p)q*B+(1-p)(1-q)*C]\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "chapter16_3 =  \"\"\"preference elicitation, involves presenting choices to the human and \n",
        "using the observed preferences to pin down the underlying utility function. \"\"\"\n",
        "\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 17, “Making Complex Decision” (17.1-17.2)\n",
        "chapter17_1 = \"\"\"  Transitions are Markovian: the probability of arriving in s’ only depends on s and not the history of earlier states. \"\"\"\n",
        "chapter17_2 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module8_lecture_notes = \"\"\" \n",
        "We will specify a utility or reward function for the agent.\n",
        "A Markov decision process or MDP is\n",
        "• a sequential decision problem\n",
        "• for a fully observable environment\n",
        "• with a stochastic transition model\n",
        "• that has additive rewards\n",
        "MDPs are non-deterministic search problems. One way of solving them is via expectimax search.\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\" : \"Your definition here\",\n",
        "\"expected utility\" : \"your definition here\",\n",
        "\"normative theory\": \"describes how a rational agent should act\",\n",
        "'descriptive theory':'describes how actual agents—for example, humans— really do act.',\n",
        "'framing effect':' exact wording of a decision problem can have a big impact on the agent’s choices',\n",
        "'anchoring effect.':'People feel more comfortable making relative utility judgments rather than absolute ones',\n",
        "'finite horizon':'there is a fixed time after which nothing matters',\n",
        "'nonstationary':'policy depends on time',\n",
        "' proper policy':'guarantte to reach the goal'\n",
        "}\n",
        "\n"
      ],
      "execution_count": 5,
      "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": 895
        },
        "outputId": "214d17aa-9a69-4742-d738-26b83eed4a38"
      },
      "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": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nThe principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\n \nTo find a solution to an MDP, you need to define the following things:\n• Asetofstatess∈S\n• Asetofactionsa∈A\n• A transition function T(s, a, s’)\n• Probability that executing action a in s will lead to s’ P(s’ | s, a)\n• The probability is called the model\n• A reward function R(s, a, s’)\n• Sometimes just R(s) or R(s’)\n• An initial state s0\n• Optionally, one or more terminal states\nIn search problems a solution was a plan，Instead, the solution to an MDP is a policy.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n we list six constraints that we require any reasonable preference relation to obey:\n1.ORDERABILITY: A>B,A<B or A~B\n2.TRANSITIVITY: A>B,B>C -> A>C\n3.CONTINUITY:A>B>C, there is p, [p*A+(1-p)*C] ~ B\n4.SUBSTITUTABILITY:A~B,[p*A+(1-p)*C]~[p*B+(1-p)*C]\n5.MONOTONICITY: A>B, (p>q) <-> [p*A+(1-p)*B]>[q*A+(1-q)*B]\n6.DECOMPOSABILITY:[p*A+(1-p)*(q*B+(1-q)*C)]~[p*A+(1-p)q*B+(1-p)(1-q)*C]\n\n\n\n\n\n## AIMA Chapter 16.3 Utility Functions\npreference elicitation, involves presenting choices to the human and \nusing the observed preferences to pin down the underlying utility function. \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n  Transitions are Markovian: the probability of arriving in s’ only depends on s and not the history of earlier states. \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n \nWe will specify a utility or reward function for the agent.\nA Markov decision process or MDP is\n• a sequential decision problem\n• for a fully observable environment\n• with a stochastic transition model\n• that has additive rewards\nMDPs are non-deterministic search problems. One way of solving them is via expectimax search.\n\n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Normative theory** - describes how a rational agent should act\n* **Descriptive theory** - describes how actual agents—for example, humans— really do act.\n* **Framing effect** -  exact wording of a decision problem can have a big impact on the agent’s choices\n* **Anchoring effect.** - People feel more comfortable making relative utility judgments rather than absolute ones\n* **Finite horizon** - there is a fixed time after which nothing matters\n* **Nonstationary** - policy depends on time\n* ** proper policy** - guarantte to reach the goal\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 =  \"\"\" Still assume a Markov decision process (MDP):\n",
        "A set of states s ∈ S\n",
        "A set of actions (per state) A\n",
        "A model T(s,a,s’)\n",
        "A reward function R(s,a,s’)\n",
        "Still looking for a policy π(s)\n",
        "New twist: don’t know T or R\n",
        " Several schemes for forcing exploration\n",
        "Simplest: random actions (ε-greedy)\n",
        "nstead, we want to generalize:\n",
        " Learn about some small number of training states from experience\n",
        "Generalize that experience to new, similar situations\n",
        "This is a fundamental idea in machine learning, and we’ll see it over and over again\"\"\"\n",
        "\n",
        "# Russell and Norvig, AIMA Chapter 22 “Reinforcement Learning” (Sections 22.1-22.5)\n",
        "chapter22_1 =  \"\"\" Your summary here \"\"\"\n",
        "chapter22_2 =  \"\"\" Q-learning is an off-policy learning algorithm\n",
        "SARSA is an on-policy algorithm: it learns Q-values that answer the question “What would this action be worth in this state, assuming I stick with my policy? \"\"\"\n",
        "chapter22_3 =  \"\"\" an agent must make a tradeoff between exploitation of\n",
        "the current best action to maximize its short-term reward and exploration of previously unknown states to gain information that can lead to a change in policy \n",
        " Any such scheme should not be greedy in terms of the immediate next move, but should be what is called “greedy in the limit of infinite exploration,” or GLIE\"\"\"\n",
        "chapter22_4 =  \"\"\" hierarchical RL agent is solving a Markovian decision problem with the following elements:\n",
        "The states are the choice states\n",
        "The actions at are the choices available\n",
        "The reward function is the expected sum of rewards\n",
        "The transition model \"\"\"\n",
        "chapter22_5 =  \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module9_lecture_notes = \"\"\"   \"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "'exploitation':' current best action to obtain rewards ',\n",
        "'exploration':'previously unknown states and actions to gain information',\n",
        "'Regret':'make mistakes sometimes',\n",
        "'Sampling':'try things repeatly',\n",
        "'Difficulty':'learning is harder than solving MDP',\n",
        "'MODEL-FREE REINFORCEMENT LEARNING':'agent neither knows nor learns a transition model for the environment',\n",
        "'ACTION-UTILITY LEARNING:':'most common form is Q-learning',\n",
        "'absorbing state':'no actions have any effect and no rewards are received.',\n",
        "\n",
        "}"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "cellView": "form",
        "outputId": "ae820e7a-27d6-4b6b-cfee-7b61c045f83c"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Still assume a Markov decision process (MDP):\nA set of states s ∈ S\nA set of actions (per state) A\nA model T(s,a,s’)\nA reward function R(s,a,s’)\nStill looking for a policy π(s)\nNew twist: don’t know T or R\n Several schemes for forcing exploration\nSimplest: random actions (ε-greedy)\nnstead, we want to generalize:\n Learn about some small number of training states from experience\nGeneralize that experience to new, similar situations\nThis is a fundamental idea in machine learning, and we’ll see it over and over again\n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Q-learning is an off-policy learning algorithm\nSARSA is an on-policy algorithm: it learns Q-values that answer the question “What would this action be worth in this state, assuming I stick with my policy? \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n an agent must make a tradeoff between exploitation of\nthe current best action to maximize its short-term reward and exploration of previously unknown states to gain information that can lead to a change in policy \n Any such scheme should not be greedy in terms of the immediate next move, but should be what is called “greedy in the limit of infinite exploration,” or GLIE\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n hierarchical RL agent is solving a Markovian decision problem with the following elements:\nThe states are the choice states\nThe actions at are the choices available\nThe reward function is the expected sum of rewards\nThe transition model \n\n## Module 9 Lecture Notes\n   \n\n## Module 9 Definitions\n* **Term** - your definition here\n* **Exploitation** -  current best action to obtain rewards \n* **Exploration** - previously unknown states and actions to gain information\n* **Regret** - make mistakes sometimes\n* **Sampling** - try things repeatly\n* **Difficulty** - learning is harder than solving MDP\n* **Model-free reinforcement learning** - agent neither knows nor learns a transition model for the environment\n* **Action-utility learning:** - most common form is Q-learning\n* **Absorbing state** - no actions have any effect and no rewards are received.\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 = \"\"\" LAZINESS: It is too much work to list the complete set of antecedents or consequents needed to ensure an exceptionless rule and too hard to use such rules.\n",
        "THEORETICAL IGNORANCE: Medical science has no complete theory for the domain.\n",
        "PRACTICAL IGNORANCE: Even if we know all the rules, we might be uncertain about a particular patient because not all the necessary tests have been or can be run.\n",
        " \"\"\"\n",
        "chapter12_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_6 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Jurafsky and Martin, Speech and Language Processing Chapter 3 “N-Gram Language Models”  (Sections 3.1-3.5,3.7)\n",
        "SLP_chapter3_1 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "8f974eec-6a6a-4037-80ee-01974ef3c18e"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n LAZINESS: It is too much work to list the complete set of antecedents or consequents needed to ensure an exceptionless rule and too hard to use such rules.\nTHEORETICAL IGNORANCE: Medical science has no complete theory for the domain.\nPRACTICAL IGNORANCE: Even if we know all the rules, we might be uncertain about a particular patient because not all the necessary tests have been or can be run.\n \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n Your summary here \n\n## Module 10 Definitions\n* **Term** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHREsHSSyA-h"
      },
      "source": [
        "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuFC8w7CyCns"
      },
      "source": [
        "# Russell and Norvig, Russell and Norvig, AIMA Chapter 13 “Probabilistic Reasoning” (13.1, 13.2, 13.3)\n",
        "chapter13_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_2 = \"\"\" context-specific independence or CSI. A conditional distribution exhibits CSI if a variable is conditionally independent of some of its parents given certain values of others. expit or inverse logit model. It uses the logistic function to produce a soft threshold \"\"\"\n",
        "chapter13_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\" Bayes’ nets implicitly encode joint distributions\n",
        " \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "cellView": "form",
        "outputId": "92611ba7-7401-477b-8dd7-7c397da857e4"
      },
      "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": 12,
      "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 context-specific independence or CSI. A conditional distribution exhibits CSI if a variable is conditionally independent of some of its parents given certain values of others. expit or inverse logit model. It uses the logistic function to produce a soft threshold \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n Bayes’ nets implicitly encode joint distributions\n \n\n## Module 11 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3FPMzG9yUls"
      },
      "source": [
        "#Module 12 - Naive Bayes and Perceptrons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEIOpL7vyWcc"
      },
      "source": [
        "# Russell and Norvig, AIMA Chapter 19 “Learning from Examples” (Sections 19.1-19.6)\n",
        "\n",
        "chapter19_1 = \"\"\" The improvements, and the techniques used to make them, depend on these factors:\n",
        "Which component is to be improved.\n",
        "What prior knowledge the agent has, which influences the model it builds. What data and feedback on that data is available. \"\"\"\n",
        "chapter19_2 = \"\"\" The confidence of a probabilistic classifier: • Posterioroverthetoplabel\n",
        "• Representshowsuretheclassifierisof the classification\n",
        "• Anyprobabilisticmodelwillhave confidences\n",
        "• Noguaranteeconfidenceiscorrect Calibration\n",
        "• Weakcalibration:higherconfidences mean higher accuracy\n",
        "• Strongcalibration:confidencepredicts accuracy rate\n",
        "• What’sthevalueofcalibration \"\"\"\n",
        "chapter19_3 = \"\"\" Idea: adjust the weight update to mitigate these effects\n",
        "MIRA*: choose an update size that fixes the current mistake...\n",
        "... but, minimizes the change to w\n",
        "In practice, it’s also bad to make updates that are too large\n",
        "• Example may be labeled incorrectly\n",
        "• You may not have enough features\n",
        "• Solution: cap the maximum possible value of t with some constant C\n",
        "• Corresponds to an optimization that assumes non- separable data\n",
        "• Usually converges faster than perceptron\n",
        "• Usually better, especially on noisy data \"\"\"\n",
        "chapter19_4 = \"\"\" Basically, SVMs are MIRA where you optimize over all examples at\n",
        "once \"\"\"\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 = \"\"\" \n",
        "problem with perceptron\n",
        "Noise: if the data isn’t separable, weights might thrash\n",
        "• Averaging weight vectors over time can help (averaged perceptron)\n",
        "Mediocre generalization: finds a “barely” separating solution\n",
        "Overtraining: test / held-out accuracy usually rises, then falls\n",
        "• Overtraining is a kind of overfitting\n",
        "\n",
        "fix solution:\n",
        "Idea: adjust the weight update to mitigate these effects\n",
        "MIRA = Margin Infused Relaxed Algorithm\n",
        "Choose an update size that fixes the current mistake... ... but, minimizes the change to w \n",
        "In practice, it’s also bad to make updates that are too large\n",
        "• Example may be labeled incorrectly\n",
        "• You may not have enough features\n",
        "• Solution: cap the maximum possible value of t with some constant C\n",
        "• Corresponds to an optimization that assumes non- separable data\n",
        "• Usually converges faster than perceptron\n",
        "• Usually better, especially on noisy data\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module12_lecture_notes = \"\"\" \n",
        "Property of Perceptron\n",
        "Separability: true if some parameters get the training set perfectly correct\n",
        "Convergence: if the training is separable, perceptron will eventually converge (binary case)\n",
        "Mistake Bound: the maximum number of mistakes (binary case) related to the margin or degree of separability\n",
        "\n",
        "Naïve Bayes\n",
        "• Builds a model training data\n",
        "• Gives prediction probabilities\n",
        "• Strong assumptions about feature independence • One pass through data (counting)\n",
        "Perceptrons / MIRA:\n",
        "• Makes less assumptions about data\n",
        "• Mistake-driven learning\n",
        "• Multiple passes through data (prediction) • Often more accurate \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "bef2e647-bc26-41a5-87f9-ff1d111d2bc7"
      },
      "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": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n The improvements, and the techniques used to make them, depend on these factors:\nWhich component is to be improved.\nWhat prior knowledge the agent has, which influences the model it builds. What data and feedback on that data is available. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n The confidence of a probabilistic classifier: • Posterioroverthetoplabel\n• Representshowsuretheclassifierisof the classification\n• Anyprobabilisticmodelwillhave confidences\n• Noguaranteeconfidenceiscorrect Calibration\n• Weakcalibration:higherconfidences mean higher accuracy\n• Strongcalibration:confidencepredicts accuracy rate\n• What’sthevalueofcalibration \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Idea: adjust the weight update to mitigate these effects\nMIRA*: choose an update size that fixes the current mistake...\n... but, minimizes the change to w\nIn practice, it’s also bad to make updates that are too large\n• Example may be labeled incorrectly\n• You may not have enough features\n• Solution: cap the maximum possible value of t with some constant C\n• Corresponds to an optimization that assumes non- separable data\n• Usually converges faster than perceptron\n• Usually better, especially on noisy data \n\n## AIMA Chapter 5.5 Stochastic Games\n Basically, SVMs are MIRA where you optimize over all examples at\nonce \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 \nProperty of Perceptron\nSeparability: true if some parameters get the training set perfectly correct\nConvergence: if the training is separable, perceptron will eventually converge (binary case)\nMistake Bound: the maximum number of mistakes (binary case) related to the margin or degree of separability\n\nNaïve Bayes\n• Builds a model training data\n• Gives prediction probabilities\n• Strong assumptions about feature independence • One pass through data (counting)\nPerceptrons / MIRA:\n• Makes less assumptions about data\n• Mistake-driven learning\n• Multiple passes through data (prediction) • Often more accurate \n\n## Module 12 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" How do we get the weights of the model? We learn the parameters (weights + bias) via learning. This requires 2 components:\n",
        "1. Anobjectivefunctionorlossfunctionthattellsusdistancebetween the system output and the gold output. We will use cross-entropy loss.\n",
        "2. An algorithm for optimizing the objective function. We will use stochastic gradient descent to minimize the loss function. \"\"\"\n",
        "slp_chapter5_2 = \"\"\" Stochastic gradient descent chooses a single random example at a time and updates its weights on that example. As a result the updates can fluctuate.\n",
        "An alternate is batch training, which computes the gradient over the entire dataset. This gives a much better estimate of which direction to move the weights but takes a long time to compute.\n",
        "A commonly used compromise is mini-batch training, where we train on a small batch. The batch size can be 512 or 1024, often selected based on computational\n",
        "resources, so that all examples in the mini-batch can be processed in parallel. The loss is then accumulated. \"\"\"\n",
        "slp_chapter5_3 = \"\"\" For sentiment classification we might extend the class labels to be positive, negative, and neutral.\n",
        "We want to know the probability of y for each class c ∈ C, p(y = c|x).\n",
        "To get a proper probability, we will use a generalization of the sigmoid function\n",
        "called the softmax function. \"\"\"\n",
        "slp_chapter5_4 = \"\"\" The simplest kind of NN is the Feed-Forward Neural Network Multilayer network, all units are usually fully-connected, and no cycles.\n",
        "The outputs from each layer are passed to units in the next higher layer, and no outputs are passed back to lower layers. \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Like logistic regression, we want to learn the best parameters for the neural net to make its predictions 𝑦/ as close to possible as the gold standard labels in our training data y.\n",
        "What do we need?\n",
        "A loss function – cross-entropy loss\n",
        "An optimization algorithm – gradient descent\n",
        "A way of computing the gradient of the loss function – error propagation \"\"\"\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 = \"\"\" Machine learning classifiers require a training corpus of M observations input/output pairs (x(i),y(i)).\n",
        "1. Afeaturerepresentationoftheinput.Foreachinputobservation x(i), this will be a vector of features [x1,x2,...,xn].\n",
        "2. Aclassificationfunctionthatcomputestheestimatedclass𝑦!via p(y|x).\n",
        "3. Anobjectivefunctionforlearning,usuallyinvolvingminimizing error on training examples.\n",
        "4. Analgorithmforoptimizingtheobjectivefunction.\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "85740f96-0699-49a9-ca34-97dc060de740"
      },
      "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": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n How do we get the weights of the model? We learn the parameters (weights + bias) via learning. This requires 2 components:\n1. Anobjectivefunctionorlossfunctionthattellsusdistancebetween the system output and the gold output. We will use cross-entropy loss.\n2. An algorithm for optimizing the objective function. We will use stochastic gradient descent to minimize the loss function. \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Stochastic gradient descent chooses a single random example at a time and updates its weights on that example. As a result the updates can fluctuate.\nAn alternate is batch training, which computes the gradient over the entire dataset. This gives a much better estimate of which direction to move the weights but takes a long time to compute.\nA commonly used compromise is mini-batch training, where we train on a small batch. The batch size can be 512 or 1024, often selected based on computational\nresources, so that all examples in the mini-batch can be processed in parallel. The loss is then accumulated. \n\n## SLP Chapter 5.3 The cross-entropy loss function\n For sentiment classification we might extend the class labels to be positive, negative, and neutral.\nWe want to know the probability of y for each class c ∈ C, p(y = c|x).\nTo get a proper probability, we will use a generalization of the sigmoid function\ncalled the softmax function. \n\n## SLP Chapter 5.4 Gradient Descent\n The simplest kind of NN is the Feed-Forward Neural Network Multilayer network, all units are usually fully-connected, and no cycles.\nThe outputs from each layer are passed to units in the next higher layer, and no outputs are passed back to lower layers. \n\n## SLP Chapter 5.5 Regularization\n Like logistic regression, we want to learn the best parameters for the neural net to make its predictions 𝑦/ as close to possible as the gold standard labels in our training data y.\nWhat do we need?\nA loss function – cross-entropy loss\nAn optimization algorithm – gradient descent\nA way of computing the gradient of the loss function – error propagation \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 Machine learning classifiers require a training corpus of M observations input/output pairs (x(i),y(i)).\n1. Afeaturerepresentationoftheinput.Foreachinputobservation x(i), this will be a vector of features [x1,x2,...,xn].\n2. Aclassificationfunctionthatcomputestheestimatedclass𝑦!via p(y|x).\n3. Anobjectivefunctionforlearning,usuallyinvolvingminimizing error on training examples.\n4. Analgorithmforoptimizingtheobjectivefunction.\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 = \"\"\" Neural Language models have several advantages over n-gram LMs: 1. They don’t need smoothing\n",
        "2. They can handle much longer histories.\n",
        "3. They can generalize over contexts of similar words.\n",
        "4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs. Disadvantage: slower to train than traditional n-gram LMs \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Associate each word in the vocabulary with a vector-representation, thereby creating a notion of similarity between words.\n",
        "2. Express the joint probability function of a word sequence in terms of the word vectors for the words in that sequence.\n",
        "3. Simultaneously learn the word vectors and the parameters of the function.\n",
        "The word vectors are low-dimensional (d=30 to d=100) dense vectors, like we’ve seen\n",
        "before.\n",
        "The probability function is expressed the product of conditional probabilities of the next word given the previous word, using a multi-layer, feed forward neural network. \"\"\"\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 = \"\"\" Each column vector represent a document. Each row vectore represent a term. value of a cell is how often a term shows in a document.\n",
        "Why dense vectors?\n",
        "• Short vectors may be easier to use as features in machine learning (fewer weights to tune)• Dense vectors may generalize better than storing explicit counts\n",
        "• They may do better at capturing synonymy:\n",
        "• car and automobile are synonyms; but are distinct dimensions in sparse vectors\n",
        "• a word with car as a neighbor and a word with automobile as a neighbor should be similar, but aren't\n",
        "• In practice, they work better\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"Synonyms\" : \"word with same meaning\",\n",
        "'Antonyms':'words with opposite meaning',\n",
        "'Connotations':'words that are positive or negative',\n",
        "\n",
        "}\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f7e1f83f-8b17-4c55-edf2-0440e56c4519"
      },
      "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": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Neural Language models have several advantages over n-gram LMs: 1. They don’t need smoothing\n2. They can handle much longer histories.\n3. They can generalize over contexts of similar words.\n4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs. Disadvantage: slower to train than traditional n-gram LMs \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Associate each word in the vocabulary with a vector-representation, thereby creating a notion of similarity between words.\n2. Express the joint probability function of a word sequence in terms of the word vectors for the words in that sequence.\n3. Simultaneously learn the word vectors and the parameters of the function.\nThe word vectors are low-dimensional (d=30 to d=100) dense vectors, like we’ve seen\nbefore.\nThe probability function is expressed the product of conditional probabilities of the next word given the previous word, using a multi-layer, feed forward neural network. \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 Machine learning classifiers require a training corpus of M observations input/output pairs (x(i),y(i)).\n1. Afeaturerepresentationoftheinput.Foreachinputobservation x(i), this will be a vector of features [x1,x2,...,xn].\n2. Aclassificationfunctionthatcomputestheestimatedclass𝑦!via p(y|x).\n3. Anobjectivefunctionforlearning,usuallyinvolvingminimizing error on training examples.\n4. Analgorithmforoptimizingtheobjectivefunction.\n\n## Module 14 Definitions\n* **Synonyms** - word with same meaning\n* **Antonyms** - words with opposite meaning\n* **Connotations** - words that are positive or negative\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": "18ad8972-e34a-45ab-d8e5-6177870faa88"
      },
      "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": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 8: Markov Decision Processes\n## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\nThe principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility\n \nTo find a solution to an MDP, you need to define the following things:\n• Asetofstatess∈S\n• Asetofactionsa∈A\n• A transition function T(s, a, s’)\n• Probability that executing action a in s will lead to s’ P(s’ | s, a)\n• The probability is called the model\n• A reward function R(s, a, s’)\n• Sometimes just R(s) or R(s’)\n• An initial state s0\n• Optionally, one or more terminal states\nIn search problems a solution was a plan，Instead, the solution to an MDP is a policy.  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n\n## AIMA Chapter 16.2 The Basis of Utility Theory\n we list six constraints that we require any reasonable preference relation to obey:\n1.ORDERABILITY: A>B,A<B or A~B\n2.TRANSITIVITY: A>B,B>C -> A>C\n3.CONTINUITY:A>B>C, there is p, [p*A+(1-p)*C] ~ B\n4.SUBSTITUTABILITY:A~B,[p*A+(1-p)*C]~[p*B+(1-p)*C]\n5.MONOTONICITY: A>B, (p>q) <-> [p*A+(1-p)*B]>[q*A+(1-q)*B]\n6.DECOMPOSABILITY:[p*A+(1-p)*(q*B+(1-q)*C)]~[p*A+(1-p)q*B+(1-p)(1-q)*C]\n\n\n\n\n\n## AIMA Chapter 16.3 Utility Functions\npreference elicitation, involves presenting choices to the human and \nusing the observed preferences to pin down the underlying utility function. \n\n## AIMA Chapter 17.1 Sequential Decision Problems\n  Transitions are Markovian: the probability of arriving in s’ only depends on s and not the history of earlier states. \n\n## AIMA Chapter 17.2 Algorithms for MDPs\n Your summary here \n\n## Module 8 Lecture Notes\n \nWe will specify a utility or reward function for the agent.\nA Markov decision process or MDP is\n• a sequential decision problem\n• for a fully observable environment\n• with a stochastic transition model\n• that has additive rewards\nMDPs are non-deterministic search problems. One way of solving them is via expectimax search.\n\n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Normative theory** - describes how a rational agent should act\n* **Descriptive theory** - describes how actual agents—for example, humans— really do act.\n* **Framing effect** -  exact wording of a decision problem can have a big impact on the agent’s choices\n* **Anchoring effect.** - People feel more comfortable making relative utility judgments rather than absolute ones\n* **Finite horizon** - there is a fixed time after which nothing matters\n* **Nonstationary** - policy depends on time\n* ** proper policy** - guarantte to reach the goal\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n Still assume a Markov decision process (MDP):\nA set of states s ∈ S\nA set of actions (per state) A\nA model T(s,a,s’)\nA reward function R(s,a,s’)\nStill looking for a policy π(s)\nNew twist: don’t know T or R\n Several schemes for forcing exploration\nSimplest: random actions (ε-greedy)\nnstead, we want to generalize:\n Learn about some small number of training states from experience\nGeneralize that experience to new, similar situations\nThis is a fundamental idea in machine learning, and we’ll see it over and over again\n\n## AIMA Chapter 22.1 Learning from Rewards\n Your summary here \n\n## AIMA Chapter 22.2 Passive Reinforcement Learning\n Q-learning is an off-policy learning algorithm\nSARSA is an on-policy algorithm: it learns Q-values that answer the question “What would this action be worth in this state, assuming I stick with my policy? \n\n## AIMA Chapter 22.3 Active Reinforcement Learning\n an agent must make a tradeoff between exploitation of\nthe current best action to maximize its short-term reward and exploration of previously unknown states to gain information that can lead to a change in policy \n Any such scheme should not be greedy in terms of the immediate next move, but should be what is called “greedy in the limit of infinite exploration,” or GLIE\n\n## AIMA Chapter 22.4 Generalization in Reinforcement Learning\n hierarchical RL agent is solving a Markovian decision problem with the following elements:\nThe states are the choice states\nThe actions at are the choices available\nThe reward function is the expected sum of rewards\nThe transition model \n\n## Module 9 Lecture Notes\n   \n\n## Module 9 Definitions\n* **Term** - your definition here\n* **Exploitation** -  current best action to obtain rewards \n* **Exploration** - previously unknown states and actions to gain information\n* **Regret** - make mistakes sometimes\n* **Sampling** - try things repeatly\n* **Difficulty** - learning is harder than solving MDP\n* **Model-free reinforcement learning** - agent neither knows nor learns a transition model for the environment\n* **Action-utility learning:** - most common form is Q-learning\n* **Absorbing state** - no actions have any effect and no rewards are received.\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n LAZINESS: It is too much work to list the complete set of antecedents or consequents needed to ensure an exceptionless rule and too hard to use such rules.\nTHEORETICAL IGNORANCE: Medical science has no complete theory for the domain.\nPRACTICAL IGNORANCE: Even if we know all the rules, we might be uncertain about a particular patient because not all the necessary tests have been or can be run.\n \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n Your summary here \n\n## Module 10 Definitions\n* **Term** - your definition here\n\n\n# Module 11: Probabilistic Reasoning and Bayes’ Nets\n## AIMA Chapter 13.1 Representing Knowledge in an Uncertain Domain\n Your summary here \n\n## AIMA Chapter 13.2 The Semantics of Bayesian Networks\n context-specific independence or CSI. A conditional distribution exhibits CSI if a variable is conditionally independent of some of its parents given certain values of others. expit or inverse logit model. It uses the logistic function to produce a soft threshold \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n Bayes’ nets implicitly encode joint distributions\n \n\n## Module 11 Definitions\n* **Concept** - definition\n\n\n# Module 12: Naive Bayes and Perceptrons\n## AIMA Chapter 5.1 Game Theory\n The improvements, and the techniques used to make them, depend on these factors:\nWhich component is to be improved.\nWhat prior knowledge the agent has, which influences the model it builds. What data and feedback on that data is available. \n\n## AIMA Chapter 5.2 Optimal Decisions in Games\n The confidence of a probabilistic classifier: • Posterioroverthetoplabel\n• Representshowsuretheclassifierisof the classification\n• Anyprobabilisticmodelwillhave confidences\n• Noguaranteeconfidenceiscorrect Calibration\n• Weakcalibration:higherconfidences mean higher accuracy\n• Strongcalibration:confidencepredicts accuracy rate\n• What’sthevalueofcalibration \n\n## AIMA Chapter 5.3 Heuristic Alpha–Beta Tree Search\n Idea: adjust the weight update to mitigate these effects\nMIRA*: choose an update size that fixes the current mistake...\n... but, minimizes the change to w\nIn practice, it’s also bad to make updates that are too large\n• Example may be labeled incorrectly\n• You may not have enough features\n• Solution: cap the maximum possible value of t with some constant C\n• Corresponds to an optimization that assumes non- separable data\n• Usually converges faster than perceptron\n• Usually better, especially on noisy data \n\n## AIMA Chapter 5.5 Stochastic Games\n Basically, SVMs are MIRA where you optimize over all examples at\nonce \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 \nProperty of Perceptron\nSeparability: true if some parameters get the training set perfectly correct\nConvergence: if the training is separable, perceptron will eventually converge (binary case)\nMistake Bound: the maximum number of mistakes (binary case) related to the margin or degree of separability\n\nNaïve Bayes\n• Builds a model training data\n• Gives prediction probabilities\n• Strong assumptions about feature independence • One pass through data (counting)\nPerceptrons / MIRA:\n• Makes less assumptions about data\n• Mistake-driven learning\n• Multiple passes through data (prediction) • Often more accurate \n\n## Module 12 Definitions\n* **Concept** - definition\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n How do we get the weights of the model? We learn the parameters (weights + bias) via learning. This requires 2 components:\n1. Anobjectivefunctionorlossfunctionthattellsusdistancebetween the system output and the gold output. We will use cross-entropy loss.\n2. An algorithm for optimizing the objective function. We will use stochastic gradient descent to minimize the loss function. \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Stochastic gradient descent chooses a single random example at a time and updates its weights on that example. As a result the updates can fluctuate.\nAn alternate is batch training, which computes the gradient over the entire dataset. This gives a much better estimate of which direction to move the weights but takes a long time to compute.\nA commonly used compromise is mini-batch training, where we train on a small batch. The batch size can be 512 or 1024, often selected based on computational\nresources, so that all examples in the mini-batch can be processed in parallel. The loss is then accumulated. \n\n## SLP Chapter 5.3 The cross-entropy loss function\n For sentiment classification we might extend the class labels to be positive, negative, and neutral.\nWe want to know the probability of y for each class c ∈ C, p(y = c|x).\nTo get a proper probability, we will use a generalization of the sigmoid function\ncalled the softmax function. \n\n## SLP Chapter 5.4 Gradient Descent\n The simplest kind of NN is the Feed-Forward Neural Network Multilayer network, all units are usually fully-connected, and no cycles.\nThe outputs from each layer are passed to units in the next higher layer, and no outputs are passed back to lower layers. \n\n## SLP Chapter 5.5 Regularization\n Like logistic regression, we want to learn the best parameters for the neural net to make its predictions 𝑦/ as close to possible as the gold standard labels in our training data y.\nWhat do we need?\nA loss function – cross-entropy loss\nAn optimization algorithm – gradient descent\nA way of computing the gradient of the loss function – error propagation \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 Machine learning classifiers require a training corpus of M observations input/output pairs (x(i),y(i)).\n1. Afeaturerepresentationoftheinput.Foreachinputobservation x(i), this will be a vector of features [x1,x2,...,xn].\n2. Aclassificationfunctionthatcomputestheestimatedclass𝑦!via p(y|x).\n3. Anobjectivefunctionforlearning,usuallyinvolvingminimizing error on training examples.\n4. Analgorithmforoptimizingtheobjectivefunction.\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 Neural Language models have several advantages over n-gram LMs: 1. They don’t need smoothing\n2. They can handle much longer histories.\n3. They can generalize over contexts of similar words.\n4. Neural LMs tend to have much higher predictive accuracy than n-gram LMs. Disadvantage: slower to train than traditional n-gram LMs \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Associate each word in the vocabulary with a vector-representation, thereby creating a notion of similarity between words.\n2. Express the joint probability function of a word sequence in terms of the word vectors for the words in that sequence.\n3. Simultaneously learn the word vectors and the parameters of the function.\nThe word vectors are low-dimensional (d=30 to d=100) dense vectors, like we’ve seen\nbefore.\nThe probability function is expressed the product of conditional probabilities of the next word given the previous word, using a multi-layer, feed forward neural network. \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 Machine learning classifiers require a training corpus of M observations input/output pairs (x(i),y(i)).\n1. Afeaturerepresentationoftheinput.Foreachinputobservation x(i), this will be a vector of features [x1,x2,...,xn].\n2. Aclassificationfunctionthatcomputestheestimatedclass𝑦!via p(y|x).\n3. Anobjectivefunctionforlearning,usuallyinvolvingminimizing error on training examples.\n4. Analgorithmforoptimizingtheobjectivefunction.\n\n## Module 14 Definitions\n* **Synonyms** - word with same meaning\n* **Antonyms** - words with opposite meaning\n* **Connotations** - words that are positive or negative\n",
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