{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "michael Template_for CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "120597dd-ec16-4b19-c213-05e77b965794"
      },
      "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": 58,
      "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": "bdf156c6-838f-4f8c-ca5d-69d6ed5eb580"
      },
      "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": 59,
      "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": 60,
      "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 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Definitions of concepts from Module 8.\n",
        "# You can define any term in the textbook or lecture, \n",
        "# but you most use your own words for the definitions.\n",
        "# Each definition should be at most 25 words long.\n",
        "\n",
        "module8_definitions = {\n",
        "\"utility function\" : \"Your definition here\",\n",
        "\"expected utility\" : \"your definition here\",\n",
        "\"etc.\": \"your definition here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": 61,
      "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": 583
        },
        "outputId": "91429f44-36bd-4c16-f6cf-f0cdfac43d11"
      },
      "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": 62,
      "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 Your summary here \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xRbdqDox2zD"
      },
      "source": [
        "#Module 9 - Reinforcement Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpBvu7EMx7sy"
      },
      "source": [
        "# Russell and Norvig, AIMA Section 17.3 “Bandit Problems”\n",
        "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",
        " indexing facts in knowledge base. A simple scheme called predicate indexing puts all the\n",
        "forward chaining: The forward-chaining algorithm in Figure 9.3 is for ease, not eff ciency. There are 3 sources of inefficiency. \n",
        "First-order definite clauses:  \n",
        "-minimum-remaining-values (MRV) heuristic used for CSPs in Chapter 6 would suggest ordering the conjuncts to look for missiles if there fewer missiles than there are objects owned by Nono.\n",
        "Rete algorithm3: preprocesses the rules in the knowledge base to construct a dataflow network in which each node is literal from premise.\n",
        "magic set: relevant bindinggs\n",
        "backward chainingg: depth-first search algorithm. This means that its space requirements are linear in size of proof. It means that back- ward chaining (unlike forward chaining) suffers from problems with repeated states and in- completeness. \n",
        "dynamic programming, in which the to subproblems are constructed incrementally from those of subproblems and are cached to avoid recomputation. \n",
        "constraint logic programmingg\n",
        "godels incompleteness theroem: there true arithmetic sentences that cant prove.\n",
        "\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "cellView": "form",
        "outputId": "51c5bf51-d0de-49f7-f6e2-ac5aaef82d31"
      },
      "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": 64,
      "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 indexing facts in knowledge base. A simple scheme called predicate indexing puts all the\nforward chaining: The forward-chaining algorithm in Figure 9.3 is for ease, not eff ciency. There are 3 sources of inefficiency. \nFirst-order definite clauses:  \n-minimum-remaining-values (MRV) heuristic used for CSPs in Chapter 6 would suggest ordering the conjuncts to look for missiles if there fewer missiles than there are objects owned by Nono.\nRete algorithm3: preprocesses the rules in the knowledge base to construct a dataflow network in which each node is literal from premise.\nmagic set: relevant bindinggs\nbackward chainingg: depth-first search algorithm. This means that its space requirements are linear in size of proof. It means that back- ward chaining (unlike forward chaining) suffers from problems with repeated states and in- completeness. \ndynamic programming, in which the to subproblems are constructed incrementally from those of subproblems and are cached to avoid recomputation. \nconstraint logic programmingg\ngodels incompleteness theroem: there true arithmetic sentences that cant prove.\n\n\n## Module 9 Definitions\n* **Term** - your definition here\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 10 - Probabilities and Language Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 12 “Quantifying Uncertainty” (Sections 12.1-12.7)\n",
        "chapter12_1 = \"\"\" To make such choices, an agent must have preferences among the different possible outcomes of the various plans. An outcome is completely specified state, including such factors as whether the agent arrives on time and the length of the wait at the airport. We use utility theory to represent preferences and reason quantitatively with them. \n",
        "A fully specified probability model associates a numerical probability with each possible world.\n",
        "it is a factored representation, in which a possible world is represented by a set of variablevalue pairs. A more expressive representation is also possible, as shown in Chapter 15. probability distribution of Weather and Cavity. We can also mix variables and values; P(sun,Cavity) would be a two-element vector giving the probabilities of a cavity with a sunny day and no cavity with a sunny day.\n",
        "We can also derive the well-known formula for the probability of a disjunction, sometimes called the inclusion–exclusion principle:\n",
        "probabilistic inference—that is, the compu- tation of probabilities for query propositions given observed.\n",
        "The naive Bayes model assumes that words occur independently documents, with fre- quencies determined by document category. This assumption is clearly violated in practice.\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": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "1d1a4ee3-c26a-4d14-82ef-18d86c7d03e2"
      },
      "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": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n To make such choices, an agent must have preferences among the different possible outcomes of the various plans. An outcome is completely specified state, including such factors as whether the agent arrives on time and the length of the wait at the airport. We use utility theory to represent preferences and reason quantitatively with them. \nA fully specified probability model associates a numerical probability with each possible world.\nit is a factored representation, in which a possible world is represented by a set of variablevalue pairs. A more expressive representation is also possible, as shown in Chapter 15. probability distribution of Weather and Cavity. We can also mix variables and values; P(sun,Cavity) would be a two-element vector giving the probabilities of a cavity with a sunny day and no cavity with a sunny day.\nWe can also derive the well-known formula for the probability of a disjunction, sometimes called the inclusion–exclusion principle:\nprobabilistic inference—that is, the compu- tation of probabilities for query propositions given observed.\nThe naive Bayes model assumes that words occur independently documents, with fre- quencies determined by document category. This assumption is clearly violated in practice.\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 = \"\"\" Your summary here \"\"\"\n",
        "chapter13_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module11_lecture_notes = \"\"\" discretization: dividing up the possible values into a fixed set of intervals\n",
        "The basic task for any inference system is compute the posterior probability distribution for a set of query variables, given some observed event—usually, some assign- ment of values to set of evidence variables.\n",
        "variable elimination works by right-to-left order. Intermediate are stored, and summations over each variable are done for those portions of expression that depend on variable.\n",
        "These are called polytrees, and have a particularly nice property: The time of polytrees is linear in size of the network.\n",
        "Monte Carlo algorithms, that provide whose accuracy depends on number of samples.\n",
        "Rejection is a general method for producing samples from a hard-to-sample distri- bution given an easy-to-sample distribution. \n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "cellView": "form",
        "outputId": "e310ed94-f04e-4d4d-9c36-68e4cbf03606"
      },
      "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": 68,
      "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 discretization: dividing up the possible values into a fixed set of intervals\nThe basic task for any inference system is compute the posterior probability distribution for a set of query variables, given some observed event—usually, some assign- ment of values to set of evidence variables.\nvariable elimination works by right-to-left order. Intermediate are stored, and summations over each variable are done for those portions of expression that depend on variable.\nThese are called polytrees, and have a particularly nice property: The time of polytrees is linear in size of the network.\nMonte Carlo algorithms, that provide whose accuracy depends on number of samples.\nRejection is a general method for producing samples from a hard-to-sample distri- bution given an easy-to-sample distribution. \n\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 = \"\"\" 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 = \"\"\" Learning takes many forms, depending on the nature of the agent, the component to be improved, and the available feedback.\n",
        "If the available feedback provides the correct  for example inputs, then the learn- ing is called supervised. The task is to learn a function y = h(x). Learning a function whose output is a continuous or ordered value (like weight) is called regression; learning a function with a small number of possible output categories is called classification;\n",
        "Logistic regression replaces the perceptron’s hard threshold with a soft threshold de- fined by logistic. Gradient descent works well even for noisy data that are not linearly separable.\n",
        "Nonparametric models use all the data to make each prediction, rather than trying to summarize the data with a few parameters. Examples include nearest neighbors and locally weighted regression.\n",
        "Support vector machines find separators maximum margin to improve the generalization performance of the classifier. Kernel methods transform the input data into a high-dimensional space where a linear separator may exist, even if the original data are nonseparable.\n",
        "We want to learn a function not only agrees with the data but also is likely to agree with future data. We need to balance agreement with the data against simplicity of the hypothesis.\n",
        "Decision can represent all Boolean functions. The-gain provides an efficient method for a simple, decision tree.\n",
        "Sometimes not all errors are equal. A loss function tells us how bad each is; the goal is then to minimize loss a validation set.\n",
        "Computational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis space and the ease of learning.\n",
        "Linear regression is a widely used model. The optimal parameters of a linear regres- sion model can be calculated exactly, or can be found by gradient descent search, which is a technique that can be applied to models that do not have a closed-form solution.\n",
        "a linear classifier with a hard threshold—also as a perceptron—can be trained by a simple weight update rule to fit that are linearly separable. In other cases, the rule fails to converge.\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "9be7d2e5-fd0b-410d-a8ce-1a5c95711326"
      },
      "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": 70,
      "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 Learning takes many forms, depending on the nature of the agent, the component to be improved, and the available feedback.\nIf the available feedback provides the correct  for example inputs, then the learn- ing is called supervised. The task is to learn a function y = h(x). Learning a function whose output is a continuous or ordered value (like weight) is called regression; learning a function with a small number of possible output categories is called classification;\nLogistic regression replaces the perceptron’s hard threshold with a soft threshold de- fined by logistic. Gradient descent works well even for noisy data that are not linearly separable.\nNonparametric models use all the data to make each prediction, rather than trying to summarize the data with a few parameters. Examples include nearest neighbors and locally weighted regression.\nSupport vector machines find separators maximum margin to improve the generalization performance of the classifier. Kernel methods transform the input data into a high-dimensional space where a linear separator may exist, even if the original data are nonseparable.\nWe want to learn a function not only agrees with the data but also is likely to agree with future data. We need to balance agreement with the data against simplicity of the hypothesis.\nDecision can represent all Boolean functions. The-gain provides an efficient method for a simple, decision tree.\nSometimes not all errors are equal. A loss function tells us how bad each is; the goal is then to minimize loss a validation set.\nComputational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis space and the ease of learning.\nLinear regression is a widely used model. The optimal parameters of a linear regres- sion model can be calculated exactly, or can be found by gradient descent search, which is a technique that can be applied to models that do not have a closed-form solution.\na linear classifier with a hard threshold—also as a perceptron—can be trained by a simple weight update rule to fit that are linearly separable. In other cases, the rule fails to converge.\n\n\n\n## Module 12 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" A feature representation of the input. \n",
        "2. A classification function that computes ˆy, the estimated class, via p(y|x). In\n",
        "the next section we will introduce the sigmoid and softmax tools for classification.\n",
        "3. An objective function for learning, usually involving minimizing error on\n",
        "training examples. We will introduce the cross-entropy loss function.\n",
        "4. An algorithm for optimizing the objective function. We introduce the stochastic gradient descent algorithm.\n",
        " \"\"\"\n",
        "slp_chapter5_2 = \"\"\" We do this via a loss function that prefers the correct class labels of the training examples to be more likely. This is called conditional maximum likelihood\n",
        "\n",
        "function is the negative log likelihood loss, generally called the cross-entropy loss.\n",
        "estimation: we choose the parameters w,b that maximize the log probability of\n",
        "the true y labels in the training data given the observations x. The resulting loss\"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" mini-batch training: we train on a group of m examples (perhaps 512, or 1024) that is less than the whole dataset. (If m is the size of the dataset,\n",
        "then we are doing batch gradient descent; if m = 1, we are back to doing stochastic gradient descent). Mini-batch training also has the advantage of computational\n",
        "efficiency. The mini-batches can easily be vectorized, choosing the size of the minibatch based on the computational resources. This allows us to process all the examples in one mini-batch in parallel and then accumulate the loss, something that’s not\n",
        "possible with individual or batch training.\"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" In such cases we use multinomial logistic regression, also called softmax remultinomial\n",
        "logistic\n",
        "regression gression (or, historically, the maxent classifier). In multinomial logistic regression\n",
        "the target y is a variable that ranges over more than two classes; we \n",
        " \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" a feedforward network because the computation proceeds iteratively from one layer of units to the next. The use of modern neural nets is often\n",
        "deep learning called deep learning, because modern networks are often deep (have many layers).\n",
        "problem called the vanishing gradient vanishing gradient\n",
        "problem. Rectifiers don’t have this problem, since the derivative of ReLU for high\n",
        "values of z is 1 rather than very close to 0.\n",
        "\"\"\"\n",
        "slp_chapter7_2 = \"\"\"perceptron, which is a very simple neural\n",
        "unit that has a binary output and does not have a non-linear activation function. T\n",
        "\"\"\"\n",
        "slp_chapter7_3 = \"\"\" decision boundary in two-dimensional space in which the output 0 is assigned to all decision\n",
        "boundary inputs lying on one side of line, and the output to all input points lying on the\n",
        "other side of the line. \n",
        "The core of the neural network is the hidden layer h formed of hidden units,\n",
        "each of which is a neural unit as described in Section 7.1, taking a weighted sum of\n",
        "its inputs and then applying a non-linearity. In the standard architecture, each layer\n",
        "fully-connected is fully-connected, meaning that each unit in each layer takes as input the outputs\n",
        "from all the units in previous layer, and there is a link between every pair of units\n",
        "from two adjacent layers. Thus each hidden unit sums over all the input units.\n",
        "normalizing a vector of real values, meaning we are \n",
        "converting it to a vector that encodes a probability distribution\n",
        "\"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\"  A one-hot vector is\n",
        "a vector that has one element equal to 1—in the dimension corresponding to that\n",
        "word’s index in the vocabulary— while all the other elements are set to zero.\n",
        "\"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" dropout: randomly dropping some units and their connections from\n",
        "the network during training\n",
        "Error backpropagation, backward differentiation on a computation graph,\n",
        "is used to compute the gradients of the loss function for a network.\n",
        "Neural language models use a neural network as a probabilistic classifier, to\n",
        "compute the probability of the next word given the previous n words.\n",
        "Neural language models can use pretrained embeddings, or can learn embeddings from scratch in the process of language modeling.\n",
        "\"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "a839e702-cd11-4685-ed16-d03bce120605"
      },
      "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": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n A feature representation of the input. \n2. A classification function that computes ˆy, the estimated class, via p(y|x). In\nthe next section we will introduce the sigmoid and softmax tools for classification.\n3. An objective function for learning, usually involving minimizing error on\ntraining examples. We will introduce the cross-entropy loss function.\n4. An algorithm for optimizing the objective function. We introduce the stochastic gradient descent algorithm.\n \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n We do this via a loss function that prefers the correct class labels of the training examples to be more likely. This is called conditional maximum likelihood\n\nfunction is the negative log likelihood loss, generally called the cross-entropy loss.\nestimation: we choose the parameters w,b that maximize the log probability of\nthe true y labels in the training data given the observations x. The resulting loss\n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n mini-batch training: we train on a group of m examples (perhaps 512, or 1024) that is less than the whole dataset. (If m is the size of the dataset,\nthen we are doing batch gradient descent; if m = 1, we are back to doing stochastic gradient descent). Mini-batch training also has the advantage of computational\nefficiency. The mini-batches can easily be vectorized, choosing the size of the minibatch based on the computational resources. This allows us to process all the examples in one mini-batch in parallel and then accumulate the loss, something that’s not\npossible with individual or batch training.\n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n In such cases we use multinomial logistic regression, also called softmax remultinomial\nlogistic\nregression gression (or, historically, the maxent classifier). In multinomial logistic regression\nthe target y is a variable that ranges over more than two classes; we \n \n\n## SLP Chapter 7.1 Units\n a feedforward network because the computation proceeds iteratively from one layer of units to the next. The use of modern neural nets is often\ndeep learning called deep learning, because modern networks are often deep (have many layers).\nproblem called the vanishing gradient vanishing gradient\nproblem. Rectifiers don’t have this problem, since the derivative of ReLU for high\nvalues of z is 1 rather than very close to 0.\n\n\n## SLP Chapter 7.2 The XOR problem\nperceptron, which is a very simple neural\nunit that has a binary output and does not have a non-linear activation function. T\n\n\n## SLP Chapter 7.3 Feedforward Neural Networks\n decision boundary in two-dimensional space in which the output 0 is assigned to all decision\nboundary inputs lying on one side of line, and the output to all input points lying on the\nother side of the line. \nThe core of the neural network is the hidden layer h formed of hidden units,\neach of which is a neural unit as described in Section 7.1, taking a weighted sum of\nits inputs and then applying a non-linearity. In the standard architecture, each layer\nfully-connected is fully-connected, meaning that each unit in each layer takes as input the outputs\nfrom all the units in previous layer, and there is a link between every pair of units\nfrom two adjacent layers. Thus each hidden unit sums over all the input units.\nnormalizing a vector of real values, meaning we are \nconverting it to a vector that encodes a probability distribution\n\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  A one-hot vector is\na vector that has one element equal to 1—in the dimension corresponding to that\nword’s index in the vocabulary— while all the other elements are set to zero.\n\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 dropout: randomly dropping some units and their connections from\nthe network during training\nError backpropagation, backward differentiation on a computation graph,\nis used to compute the gradients of the loss function for a network.\nNeural language models use a neural network as a probabilistic classifier, to\ncompute the probability of the next word given the previous n words.\nNeural language models can use pretrained embeddings, or can learn embeddings from scratch in the process of language modeling.\n\n\n## Module 13 Lecture Notes\n Your summary here \n\n## Module 13 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter9_3 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 6 “Vector Semantics and Embeddings” (6.1-6.4, 6.8, 6.10-6.12)\n",
        "\n",
        "slp_chapter6_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_10 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_11 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_12 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module14_lecture_notes = \"\"\" Vector semantics and embeddings\n",
        "Distributional hypothesis: link between similarity in how words are distributed and similarity in what they mean – synonyms tend to occur in the same environment\n",
        "Embeddings: representations of meaning of words\n",
        "-can be static or dynamic\n",
        "Lexical semantics\n",
        "Lemma/citation form: the form of a word (eg. sing is lemma for sing, sang, sung, its wordforms)\n",
        "Synonymy: one word has a sense whose meaning is identical to a sense of another word, or nearly identical\n",
        "Same propositional meaning if two words are substitutable for one another in any sentence without changing the truth conditions of the sentence\n",
        "Word similarity: similar in terms of semantics/meaning (eg. cat related to dog)\n",
        "Word relatedness: associations (eg. coffee related to cup)\n",
        "semantic field: a set of words which cover a particular semantic domain and bear structured relations with each other (eg. domain of restaurant: food, chef, waiter)\n",
        "Semantic frame: set of words that denote perspectives or participants in a particular type of event\n",
        "Sentiment: Positive or negative evaluation language\n",
        "Words vary along three dimensions of affective meaning:\n",
        "-valence: the pleasantness of the stimulus\n",
        "-arousal: the intensity of emotion provoked by the stimulus\n",
        "-dominance: the degree of control exerted by the stimulus\n",
        "Vector semantics: standard way to represent word\n",
        "-can be dense or sparse\n",
        "Term-document matrix: each row represents a word in the vocabulary and each term-document matrix column represents a document from some collection of documents\n",
        "Term term matrix:  columns are labeled by words rather word-word matrix than documents\n",
        "Cosine can measure similarity  (normalized dot product)\n",
        "Tf-idf weighting: term frequency (in each document) + document frequency (of each term)\n",
        "Pointwise mutual information: how often two events x and y occur, compared with\n",
        "what we would expect if they were independent\n",
        "-bias toward infrequent events\n",
        "first-order co-occurrence: words that are typically nearby each other. \n",
        "second-order co-occurrence: similar second-order co-occurrence neighbors\n",
        "\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcb72d1d-7f0e-4fea-af84-5e77f4701fd9"
      },
      "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 14 Lecture Notes\", module14_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": 76,
      "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 14 Lecture Notes\n Vector semantics and embeddings\nDistributional hypothesis: link between similarity in how words are distributed and similarity in what they mean – synonyms tend to occur in the same environment\nEmbeddings: representations of meaning of words\n-can be static or dynamic\nLexical semantics\nLemma/citation form: the form of a word (eg. sing is lemma for sing, sang, sung, its wordforms)\nSynonymy: one word has a sense whose meaning is identical to a sense of another word, or nearly identical\nSame propositional meaning if two words are substitutable for one another in any sentence without changing the truth conditions of the sentence\nWord similarity: similar in terms of semantics/meaning (eg. cat related to dog)\nWord relatedness: associations (eg. coffee related to cup)\nsemantic field: a set of words which cover a particular semantic domain and bear structured relations with each other (eg. domain of restaurant: food, chef, waiter)\nSemantic frame: set of words that denote perspectives or participants in a particular type of event\nSentiment: Positive or negative evaluation language\nWords vary along three dimensions of affective meaning:\n-valence: the pleasantness of the stimulus\n-arousal: the intensity of emotion provoked by the stimulus\n-dominance: the degree of control exerted by the stimulus\nVector semantics: standard way to represent word\n-can be dense or sparse\nTerm-document matrix: each row represents a word in the vocabulary and each term-document matrix column represents a document from some collection of documents\nTerm term matrix:  columns are labeled by words rather word-word matrix than documents\nCosine can measure similarity  (normalized dot product)\nTf-idf weighting: term frequency (in each document) + document frequency (of each term)\nPointwise mutual information: how often two events x and y occur, compared with\nwhat we would expect if they were independent\n-bias toward infrequent events\nfirst-order co-occurrence: words that are typically nearby each other. \nsecond-order co-occurrence: similar second-order co-occurrence neighbors\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": 1000
        },
        "id": "T3cm1WV542_a",
        "outputId": "9c7672eb-ec11-454a-ce0a-a2f3043d4421"
      },
      "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": 77,
      "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 Your summary here \n\n## Module 8 Definitions\n* **Utility function** - Your definition here\n* **Expected utility** - your definition here\n* **Etc.** - your definition here\n\n\n# Module 9: Reinforcement Learning\n## AIMA Chapter 17.3 Bandit Problems\n 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 indexing facts in knowledge base. A simple scheme called predicate indexing puts all the\nforward chaining: The forward-chaining algorithm in Figure 9.3 is for ease, not eff ciency. There are 3 sources of inefficiency. \nFirst-order definite clauses:  \n-minimum-remaining-values (MRV) heuristic used for CSPs in Chapter 6 would suggest ordering the conjuncts to look for missiles if there fewer missiles than there are objects owned by Nono.\nRete algorithm3: preprocesses the rules in the knowledge base to construct a dataflow network in which each node is literal from premise.\nmagic set: relevant bindinggs\nbackward chainingg: depth-first search algorithm. This means that its space requirements are linear in size of proof. It means that back- ward chaining (unlike forward chaining) suffers from problems with repeated states and in- completeness. \ndynamic programming, in which the to subproblems are constructed incrementally from those of subproblems and are cached to avoid recomputation. \nconstraint logic programmingg\ngodels incompleteness theroem: there true arithmetic sentences that cant prove.\n\n\n## Module 9 Definitions\n* **Term** - your definition here\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n To make such choices, an agent must have preferences among the different possible outcomes of the various plans. An outcome is completely specified state, including such factors as whether the agent arrives on time and the length of the wait at the airport. We use utility theory to represent preferences and reason quantitatively with them. \nA fully specified probability model associates a numerical probability with each possible world.\nit is a factored representation, in which a possible world is represented by a set of variablevalue pairs. A more expressive representation is also possible, as shown in Chapter 15. probability distribution of Weather and Cavity. We can also mix variables and values; P(sun,Cavity) would be a two-element vector giving the probabilities of a cavity with a sunny day and no cavity with a sunny day.\nWe can also derive the well-known formula for the probability of a disjunction, sometimes called the inclusion–exclusion principle:\nprobabilistic inference—that is, the compu- tation of probabilities for query propositions given observed.\nThe naive Bayes model assumes that words occur independently documents, with fre- quencies determined by document category. This assumption is clearly violated in practice.\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 Your summary here \n\n## AIMA 13.3 Exact Inference in Bayesian Networks\n Your summary here \n\n## Module 11 Lecture Notes\n discretization: dividing up the possible values into a fixed set of intervals\nThe basic task for any inference system is compute the posterior probability distribution for a set of query variables, given some observed event—usually, some assign- ment of values to set of evidence variables.\nvariable elimination works by right-to-left order. Intermediate are stored, and summations over each variable are done for those portions of expression that depend on variable.\nThese are called polytrees, and have a particularly nice property: The time of polytrees is linear in size of the network.\nMonte Carlo algorithms, that provide whose accuracy depends on number of samples.\nRejection is a general method for producing samples from a hard-to-sample distri- bution given an easy-to-sample distribution. \n\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 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 Learning takes many forms, depending on the nature of the agent, the component to be improved, and the available feedback.\nIf the available feedback provides the correct  for example inputs, then the learn- ing is called supervised. The task is to learn a function y = h(x). Learning a function whose output is a continuous or ordered value (like weight) is called regression; learning a function with a small number of possible output categories is called classification;\nLogistic regression replaces the perceptron’s hard threshold with a soft threshold de- fined by logistic. Gradient descent works well even for noisy data that are not linearly separable.\nNonparametric models use all the data to make each prediction, rather than trying to summarize the data with a few parameters. Examples include nearest neighbors and locally weighted regression.\nSupport vector machines find separators maximum margin to improve the generalization performance of the classifier. Kernel methods transform the input data into a high-dimensional space where a linear separator may exist, even if the original data are nonseparable.\nWe want to learn a function not only agrees with the data but also is likely to agree with future data. We need to balance agreement with the data against simplicity of the hypothesis.\nDecision can represent all Boolean functions. The-gain provides an efficient method for a simple, decision tree.\nSometimes not all errors are equal. A loss function tells us how bad each is; the goal is then to minimize loss a validation set.\nComputational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis space and the ease of learning.\nLinear regression is a widely used model. The optimal parameters of a linear regres- sion model can be calculated exactly, or can be found by gradient descent search, which is a technique that can be applied to models that do not have a closed-form solution.\na linear classifier with a hard threshold—also as a perceptron—can be trained by a simple weight update rule to fit that are linearly separable. In other cases, the rule fails to converge.\n\n\n\n## Module 12 Definitions\n* **Concept** - definition\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n A feature representation of the input. \n2. A classification function that computes ˆy, the estimated class, via p(y|x). In\nthe next section we will introduce the sigmoid and softmax tools for classification.\n3. An objective function for learning, usually involving minimizing error on\ntraining examples. We will introduce the cross-entropy loss function.\n4. An algorithm for optimizing the objective function. We introduce the stochastic gradient descent algorithm.\n \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n We do this via a loss function that prefers the correct class labels of the training examples to be more likely. This is called conditional maximum likelihood\n\nfunction is the negative log likelihood loss, generally called the cross-entropy loss.\nestimation: we choose the parameters w,b that maximize the log probability of\nthe true y labels in the training data given the observations x. The resulting loss\n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n mini-batch training: we train on a group of m examples (perhaps 512, or 1024) that is less than the whole dataset. (If m is the size of the dataset,\nthen we are doing batch gradient descent; if m = 1, we are back to doing stochastic gradient descent). Mini-batch training also has the advantage of computational\nefficiency. The mini-batches can easily be vectorized, choosing the size of the minibatch based on the computational resources. This allows us to process all the examples in one mini-batch in parallel and then accumulate the loss, something that’s not\npossible with individual or batch training.\n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n In such cases we use multinomial logistic regression, also called softmax remultinomial\nlogistic\nregression gression (or, historically, the maxent classifier). In multinomial logistic regression\nthe target y is a variable that ranges over more than two classes; we \n \n\n## SLP Chapter 7.1 Units\n a feedforward network because the computation proceeds iteratively from one layer of units to the next. The use of modern neural nets is often\ndeep learning called deep learning, because modern networks are often deep (have many layers).\nproblem called the vanishing gradient vanishing gradient\nproblem. Rectifiers don’t have this problem, since the derivative of ReLU for high\nvalues of z is 1 rather than very close to 0.\n\n\n## SLP Chapter 7.2 The XOR problem\nperceptron, which is a very simple neural\nunit that has a binary output and does not have a non-linear activation function. T\n\n\n## SLP Chapter 7.3 Feedforward Neural Networks\n decision boundary in two-dimensional space in which the output 0 is assigned to all decision\nboundary inputs lying on one side of line, and the output to all input points lying on the\nother side of the line. \nThe core of the neural network is the hidden layer h formed of hidden units,\neach of which is a neural unit as described in Section 7.1, taking a weighted sum of\nits inputs and then applying a non-linearity. In the standard architecture, each layer\nfully-connected is fully-connected, meaning that each unit in each layer takes as input the outputs\nfrom all the units in previous layer, and there is a link between every pair of units\nfrom two adjacent layers. Thus each hidden unit sums over all the input units.\nnormalizing a vector of real values, meaning we are \nconverting it to a vector that encodes a probability distribution\n\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  A one-hot vector is\na vector that has one element equal to 1—in the dimension corresponding to that\nword’s index in the vocabulary— while all the other elements are set to zero.\n\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 dropout: randomly dropping some units and their connections from\nthe network during training\nError backpropagation, backward differentiation on a computation graph,\nis used to compute the gradients of the loss function for a network.\nNeural language models use a neural network as a probabilistic classifier, to\ncompute the probability of the next word given the previous n words.\nNeural language models can use pretrained embeddings, or can learn embeddings from scratch in the process of language modeling.\n\n\n## Module 13 Lecture Notes\n Your summary here \n\n## Module 13 Definitions\n* **Concept** - definition\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n Your summary here \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n Your summary here \n\n## SLP Chapter 9.3 RNNs as Language Models\n Your summary here \n\n## SLP Chapter 6.1 Lexical Semantics\n Your summary here \n\n## SLP Chapter 6.2 Vector Semantics\n Your summary here \n\n## SLP Chapter 6.3 Words and Vectors\n Your summary here \n\n## SLP Chapter 6.4 Cosine for measuring similarity\n Your summary here \n\n## SLP Chapter 6.8 Word2vec\n Your summary here \n\n## SLP Chapter 6.10 Semantic properties of embeddings\n Your summary here \n\n## SLP Chapter 6.11 Bias and Embeddings\n Your summary here \n\n## SLP Chapter 6.12 Evaluating Vector Models\n Your summary here \n\n## Module 14 Lecture Notes\n Vector semantics and embeddings\nDistributional hypothesis: link between similarity in how words are distributed and similarity in what they mean – synonyms tend to occur in the same environment\nEmbeddings: representations of meaning of words\n-can be static or dynamic\nLexical semantics\nLemma/citation form: the form of a word (eg. sing is lemma for sing, sang, sung, its wordforms)\nSynonymy: one word has a sense whose meaning is identical to a sense of another word, or nearly identical\nSame propositional meaning if two words are substitutable for one another in any sentence without changing the truth conditions of the sentence\nWord similarity: similar in terms of semantics/meaning (eg. cat related to dog)\nWord relatedness: associations (eg. coffee related to cup)\nsemantic field: a set of words which cover a particular semantic domain and bear structured relations with each other (eg. domain of restaurant: food, chef, waiter)\nSemantic frame: set of words that denote perspectives or participants in a particular type of event\nSentiment: Positive or negative evaluation language\nWords vary along three dimensions of affective meaning:\n-valence: the pleasantness of the stimulus\n-arousal: the intensity of emotion provoked by the stimulus\n-dominance: the degree of control exerted by the stimulus\nVector semantics: standard way to represent word\n-can be dense or sparse\nTerm-document matrix: each row represents a word in the vocabulary and each term-document matrix column represents a document from some collection of documents\nTerm term matrix:  columns are labeled by words rather word-word matrix than documents\nCosine can measure similarity  (normalized dot product)\nTf-idf weighting: term frequency (in each document) + document frequency (of each term)\nPointwise mutual information: how often two events x and y occur, compared with\nwhat we would expect if they were independent\n-bias toward infrequent events\nfirst-order co-occurrence: words that are typically nearby each other. \nsecond-order co-occurrence: similar second-order co-occurrence neighbors\n\n\n## Module 14 Definitions\n* **Concept** - definition\n",
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