{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Template_for CIS521_midterm_2_notes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brNDGX-kvzds"
      },
      "source": [
        "# Template for Notes for CIS 521 Midterm 2\n",
        "\n",
        "The midterm exam for CIS 521 will be **open notes**. No other matierals are allowed for the exam - you may not use the textbook, the internet, a Python envionement, or communicate with anyone during the exam. \n",
        "\n",
        "This python notebook is where you should save all of your notes.  It provides a structured template that allows you to add summaries for each of the textbook sections that are part of the required reading, plus room for lecture notes and definitions.\n",
        "\n",
        "Your summaries must be written in your own words.  Do not copy and paste from the textbook.  Do not share your notes with classmates, or copy any of your classmates' notes.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPtM8g7R0Pds"
      },
      "source": [
        "# Instructions\n",
        "\n",
        "1. Make a copy of this Google Colab file. From the colab menu, pick **File > Save a copy in Drive**.  That will save a copy of this notebook in your Google drive.\n",
        "2. Go through each section of the notebook, and fill in all the summaries for each chapter and its corresponding sections.  Be sure to save your work frequently!\n",
        "3. Read the notes below about how you can format your summaries.\n",
        "4. In addition to these notes, you are allowed 1 page (both sides) for figures or handwritten notes.\n",
        "5. Submit your version of this notebook to Gradescope before you start the exam.\n",
        "6. If you are taking the exam in person, then you must print a copy of your notes for yourself.   Here's how to print: \n",
        "* Run the last cell of this notebook\n",
        "* Click the \"Mirror cell in tab\" button (this is located in the upper right corner of the cell, immediately to the right of the trash can).  This will open the cell.  \n",
        "* In Chrome, you can then print your notes using the **File > Print** option from the browser's menu.  \n",
        "* In the print options, change the layout from Landscape to Portrait. \n",
        "* Clicking the Save, will save a PDF that you can then print.\n",
        "\n",
        "## Formatting Notes\n",
        "\n",
        "1. The max length for each section's summary is 500 words / 3000 characters.\n",
        "2. You can also write a 2000 word summary of the lectures for each module.\n",
        "3. You may also write definitions of concepts for each module.  The definitions should be written in your own words (not copy and pasted), and they should be no more than 20 words long for each concept. \n",
        "2. You can use Markdown formatting for your summaries. \n",
        "3. You can use Latex formatting for equations. Put two dollar signs before and after your equation like this: \n",
        "$$ \\epsilon = \\frac{1}{mc^2} $$\n",
        "4. When you write python code or psuedocode algorithms, you should surround it with triple backticks like this:\n",
        "```\n",
        "def function():\n",
        "      print('hello world')\n",
        "```\n",
        "5. Write \\ whenever you want to keep writing on the next line without a newline being inserted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw5IpDLCZa4W"
      },
      "source": [
        "## Example summary\n",
        "\n",
        "This is a sample summary of [Section 2.1.1](https://web.stanford.edu/~jurafsky/slp3/2.pdf ) of Jurfasky and Martin's Speech and Language Processing's book\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzaavW96tcxM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54580f1e-22eb-4657-8e14-104a615b6276"
      },
      "source": [
        "chapter_2_1_example = \"\"\"\n",
        "Regular expressions are an algebraic notation for characterizing \\\n",
        "a set of strings that are useful for searching for a pattern or through a corpus.\\\n",
        "Only the first matching result will be shown. The search string is case sensitive, \\\n",
        "but it can consist of a single character or a sequence of characters. \\\n",
        "While square braces specify a disjunction of characters, the dash can more easily look \\\n",
        "for characters over a range. The caret specifies the non-existence of a character. \\\n",
        "Additionally, the (?) is used to for flexibility on a single character, meaning \\\n",
        "\"the preceding character or nothing\". More importantly, the Kleene star means zero or \\\n",
        "more occurrences of the immediately previous character or regular expression in square braces.\\\n",
        "...\n",
        "\"\"\"\n",
        "\n",
        "length = len(chapter_2_1_example.split(\" \"))\n",
        "print(\"The example summary is {len} words long, so you could write an additional \\\n",
        "{remaining} words.\".format(len=length, remaining=(500-length)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The example summary is 116 words long, so you could write an additional 384 words.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3Uk9gQTevcj"
      },
      "source": [
        "## Example summary length\n",
        "\n",
        "The maximum summary length for each section is 500 words / 3000 characters.  Hopefully you'll find that to be quite generous. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DvZ57A-djo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cd4c8a2-805b-483a-8fae-409ce38e0560"
      },
      "source": [
        "# This is an example of a 500 word placeholder\n",
        "placeholder = \"\"\"ultrices vitae auctor eu augue ut lectus arcu bibendum at varius \\\n",
        " vel pharetra vel turpis nunc eget lorem dolor sed viverra ipsum nunc aliquet \\\n",
        " bibendum enim facilisis gravida neque convallis a cras semper auctor neque vitae \\\n",
        " tempus quam pellentesque nec nam aliquam sem et tortor consequat id porta nibh \\\n",
        " venenatis cras sed felis eget velit aliquet sagittis id consectetur purus ut \\\n",
        " faucibus pulvinar elementum integer enim neque volutpat ac tincidunt vitae semper \\ \n",
        " quis lectus nulla at volutpat diam ut venenatis tellus in metus vulputate eu \\\n",
        " scelerisque felis imperdiet proin fermentum leo vel orci porta non pulvinar neque \\ \n",
        " laoreet suspendisse interdum consectetur libero id faucibus nisl tincidunt eget \\\n",
        " nullam non nisi est sit amet facilisis magna etiam tempor orci eu lobortis \\\n",
        " elementum nibh tellus molestie nunc non blandit massa enim nec dui nunc mattis \\\n",
        " enim ut tellus elementum sagittis vitae et leo duis ut diam quam nulla porttitor \\\n",
        " massa id neque aliquam vestibulum morbi blandit cursus risus at ultrices mi tempus \\\n",
        " imperdiet nulla malesuada pellentesque elit eget gravida cum sociis natoque \\\n",
        " penatibus et magnis dis parturient montes nascetur ridiculus mus mauris vitae \\\n",
        " ultricies leo integer malesuada nunc vel risus commodo viverra maecenas accumsan \\\n",
        " lacus vel facilisis volutpat est velit egestas dui id ornare arcu odio ut sem nulla \\\n",
        " pharetra diam sit amet nisl suscipit adipiscing bibendum est ultricies integer \\\n",
        " quis auctor elit sed vulputate mi sit amet mauris commodo quis imperdiet massa \\\n",
        " tincidunt nunc pulvinar sapien et ligula ullamcorper malesuada proin libero nunc \\\n",
        " consequat interdum varius sit amet mattis vulputate enim nulla aliquet porttitor \\\n",
        " lacus luctus accumsan tortor posuere ac ut consequat semper viverra nam libero \\\n",
        " justo laoreet sit amet cursus sit amet dictum sit amet justo donec enim diam \\\n",
        " vulputate ut pharetra sit amet aliquam id diam maecenas ultricies mi eget mauris \\\n",
        " pharetra et ultrices neque ornare aenean euismod elementum nisi quis eleifend quam \\\n",
        " adipiscing vitae proin sagittis nisl rhoncus mattis rhoncus urna neque viverra justo \\\n",
        " nec ultrices dui sapien eget mi proin sed libero enim sed faucibus turpis in \\\n",
        " eu mi bibendum neque egestas congue quisque egestas diam in arcu cursus euismod \\\n",
        " quis viverra nibh cras pulvinar mattis nunc sed blandit libero volutpat sed cras \\\n",
        " ornare arcu dui vivamus arcu felis bibendum ut tristique et egestas quis ipsum \\\n",
        " suspendisse ultrices gravida dictum fusce ut placerat orci nulla pellentesque \\\n",
        " dignissim enim sit amet venenatis urna cursus eget nunc scelerisque viverra mauris \\\n",
        " in aliquam sem fringilla ut morbi tincidunt augue interdum velit euismod in \\\n",
        " pellentesque massa placerat duis ultricies lacus sed turpis tincidunt id aliquet \\\n",
        " risus feugiat in ante metus dictum at tempor commodo ullamcorper a lacus vestibulum \\\n",
        " sed arcu non odio euismod lacinia at quis risus sed vulputate odio ut enim blandit \\\n",
        " volutpat maecenas volutpat blandit aliquam etiam erat velit scelerisque in \\\n",
        " dictum non consectetur a erat nam at lectus urna\"\"\"\n",
        "\n",
        "print(\"The length in words is\", len(placeholder.split(' ')))\n",
        "print(\"The length in characters is\", len(placeholder))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The length in words is 500\n",
            "The length in characters is 3030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yG3-YncJfTB"
      },
      "source": [
        "# Permissions to use your summaries\n",
        "\n",
        "We'd like to use your summaries to help develop better educational resources like [remember.school](https://remember.school) or a question-answering system about the textbook.  \n",
        "\n",
        "1. Please enter your name.  \n",
        "\n",
        "2. If you grant permission for us to use your summaries under a [Creative Commons License](https://creativecommons.org/licenses/by/4.0/), please set ```permission = True```.  If you do not grant permission, please set it to false. Your answer won't affect your grade.\n",
        "\n",
        "3. If you grant us permission, and you'd like to remain anonymous, set ```anonymous = True```.  We'll remove your name before sharing your summaries with anyone. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RECnJArJew4"
      },
      "source": [
        "name = \"\"\n",
        "permission = True\n",
        "anonymous = False"
      ],
      "execution_count": null,
      "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 = \"\"\" 1. Markov Decision Processes, fully-observable, not deterministic but stochastic (e.g. 0.8 right direction, 0.2 other direction)\\n\n",
        "2. transition function: T(s,a,s'), also explicitly as probability P(s'|s,a), s' destination, s start point, a: action\\n\n",
        "3. utility function/reward function: R(s,a,s')(e.g. -0.04 for every move)\\n\n",
        "4. MDP: a sequential decision problem, a fully observable environment, with a stochastic transition model, that has additive rewards\\n\n",
        "5. MDP need define: a set of states, a set of actions, transition function T, reward function R, initial state s0, one or more terminal states\\n\n",
        "6. policy: a solution was a sequence of action that corresponded to the shortest path, but because of non-determinism, the solution to MDP is a policy, maps from a state onto the action to take if agent is in that state.(pi(s)=a)\\n\n",
        "7. performance of agent in MDP: sum of the rewards for the transitions it takes.\\n\n",
        "8. discounting: apply discount factor for later rewards to helps algorithms converge, e.g.sum_r=0.5^0*1+0.5^1*1+0.5^2*1+0.5^3*1.... 0.5 is the discounting factor.\\n\n",
        "9.!!!! value of a state s =V*(s), value of a q-state(s,a)= Q*(s,a). Q*(s,a)=sum_s' (T(s,a,s')(R(s,a,s')+discount* V*(s'))), V*(s)=max Q*(s,a)\n",
        "10. value iteration: V=max_a Q(s,a)\n",
        "11. problem with value iteration: slow O(s^2 * A); max at each state rarely change; policy often converge long before the value \\n\n",
        "12. policy iteration: step1: use fixed policy calculate value V_temp. step2: use V_temp udpate policy, pi(s)=argmax_a (V_temp). step3: repeat until policy converge\\n\n",
        "13. a rational agent should choose the action that maximuzes its expected utility, given its knowledge\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",
        "\"etc.\": \"your definition here\",\n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "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": 921
        },
        "outputId": "e5896957-ac71-499c-e539-6d887aa7522e"
      },
      "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": null,
      "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 1. Markov Decision Processes, fully-observable, not deterministic but stochastic (e.g. 0.8 right direction, 0.2 other direction)\n\n2. transition function: T(s,a,s'), also explicitly as probability P(s'|s,a), s' destination, s start point, a: action\n\n3. utility function/reward function: R(s,a,s')(e.g. -0.04 for every move)\n\n4. MDP: a sequential decision problem, a fully observable environment, with a stochastic transition model, that has additive rewards\n\n5. MDP need define: a set of states, a set of actions, transition function T, reward function R, initial state s0, one or more terminal states\n\n6. policy: a solution was a sequence of action that corresponded to the shortest path, but because of non-determinism, the solution to MDP is a policy, maps from a state onto the action to take if agent is in that state.(pi(s)=a)\n\n7. performance of agent in MDP: sum of the rewards for the transitions it takes.\n\n8. discounting: apply discount factor for later rewards to helps algorithms converge, e.g.sum_r=0.5^0*1+0.5^1*1+0.5^2*1+0.5^3*1.... 0.5 is the discounting factor.\n\n9.!!!! value of a state s =V*(s), value of a q-state(s,a)= Q*(s,a). Q*(s,a)=sum_s' (T(s,a,s')(R(s,a,s')+discount* V*(s'))), V*(s)=max Q*(s,a)\n10. value iteration: V=max_a Q(s,a)\n11. problem with value iteration: slow O(s^2 * A); max at each state rarely change; policy often converge long before the value \n\n12. policy iteration: step1: use fixed policy calculate value V_temp. step2: use V_temp udpate policy, pi(s)=argmax_a (V_temp). step3: repeat until policy converge\n\n13. a rational agent should choose the action that maximuzes its expected utility, given its knowledge\n\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 = \"\"\" 1. MDP: offline planning: determine all quantities through computation; need to know the details of the MDP; do not actually play the game\\n\n",
        "2. reinforcement learning, important ideas: exploration, exploitation, regret, sampling difficulty\\n\n",
        "3. RL is online learning, different with MDP, do not know T, R,\\n\n",
        "4. RL: model based Learning:input policy, try get result, estimate T,R, then solve RL as MDP.\n",
        "5. RL: model free learning: passive reinforcement learning, use fixed policy,pi. every step record the discounted rewards, sum and average get the Value of the state.\\n\n",
        "6. passive reinforcement learning: bad: wastes information about state connections; each state must be learned separately; takes long time to learn\\n\n",
        "7.RL: temporal difference learning: update V(s) each time we experience a transition. TD is model-free way to do policy evaluation, mimicking bellman update when run sample averages\\n\n",
        "8: TD learning bad: cannot turn values into policy, no Q value\\n\n",
        "9. Active RL: goal: learn the optimal policy/value.   Q Learning: converge to optimal policy, off-policy learning \\n\n",
        "10. improve Q-Learning for EXPLORATION VS EXPLOITATION, use greedy factor, with small probability, act randomly, large probability, act on current policy, random exploration has higher regret\\n\n",
        "11. approximate Q-learning, use feature and weight\"\"\"\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module9_definitions = {\n",
        "\"exploration\" : \"try unknow actions to get information\",\n",
        "\" Exploitation\" : \"you have to use what you know\",\n",
        "\" regret\" : \"even if you learn intelligently, you make mistakes\",\n",
        "\" Sampling\" : \"because of chance, you have to try things repeatedly\",\n",
        "\" Difficulty\" : \"learning can be much harder than solving a known MDP\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOK__lwWgdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "cellView": "form",
        "outputId": "92823f82-5499-49d0-9b4f-d922db892f8a"
      },
      "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": null,
      "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 1. MDP: offline planning: determine all quantities through computation; need to know the details of the MDP; do not actually play the game\n\n2. reinforcement learning, important ideas: exploration, exploitation, regret, sampling difficulty\n\n3. RL is online learning, different with MDP, do not know T, R,\n\n4. RL: model based Learning:input policy, try get result, estimate T,R, then solve RL as MDP.\n5. RL: model free learning: passive reinforcement learning, use fixed policy,pi. every step record the discounted rewards, sum and average get the Value of the state.\n\n6. passive reinforcement learning: bad: wastes information about state connections; each state must be learned separately; takes long time to learn\n\n7.RL: temporal difference learning: update V(s) each time we experience a transition. TD is model-free way to do policy evaluation, mimicking bellman update when run sample averages\n\n8: TD learning bad: cannot turn values into policy, no Q value\n\n9. Active RL: goal: learn the optimal policy/value.   Q Learning: converge to optimal policy, off-policy learning \n\n10. improve Q-Learning for EXPLORATION VS EXPLOITATION, use greedy factor, with small probability, act randomly, large probability, act on current policy, random exploration has higher regret\n\n11. approximate Q-learning, use feature and weight\n\n## Module 9 Definitions\n* **Exploration** - try unknow actions to get information\n* ** exploitation** - you have to use what you know\n* ** regret** - even if you learn intelligently, you make mistakes\n* ** sampling** - because of chance, you have to try things repeatedly\n* ** difficulty** - learning can be much harder than solving a known MDP\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46MQLR-Gx8dZ"
      },
      "source": [
        "#Module 10 - Probabilities and Language Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BN0b1eVYx-nd"
      },
      "source": [
        "# Russell and Norvig, AIMA 4th Edition, AIMA Chapter 12 “Quantifying Uncertainty” (Sections 12.1-12.7)\n",
        "chapter12_1 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_2 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_3 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_4 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_5 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_6 = \"\"\" Your summary here \"\"\"\n",
        "chapter12_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Jurafsky and Martin, Speech and Language Processing Chapter 3 “N-Gram Language Models”  (Sections 3.1-3.5,3.7)\n",
        "SLP_chapter3_1 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_2 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_3 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_4 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_5 = \"\"\" Your summary here \"\"\"\n",
        "SLP_chapter3_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "\n",
        "# Lecture notes \n",
        "module10_lecture_notes = \"\"\" 1. probabilistic Language Models (PLM), assign a probability to a sentence..compute the probability of a sentence or sequence of words, probability of an upcoming word \\n\n",
        "2. Markov Assumption: the probability of word only depends on the previous k words, not the whole context, unigram=P(w1), bigram=P(w_i|w_i-1), trigram, 4-gram\"\"\"\n",
        "\n",
        "\n",
        "# Each definition should be at most 25 words long.\n",
        "module10_definitions = {\n",
        "\"term\" : \"your definition here\",\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvtN3p7ET3Tx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "254d6c2c-0c51-4511-d1e0-e9da56bc3fe7"
      },
      "source": [
        "#@title Preview of your summary\n",
        "#@markdown You don't need to modify anything in this cell. Just press play.\n",
        "module10_sections = [\n",
        "  (\"AIMA Chapter 12.1 Acting under Uncertainty\", chapter12_1),\n",
        "  (\"AIMA Chapter 12.2 Basic Probability Notation\", chapter12_2),\n",
        "  (\"AIMA Chapter 12.3 Inference Using Full Joint Distributions\", chapter12_3),\n",
        "  (\"AIMA Chapter 12.4 Independence\", chapter12_4),\n",
        "  (\"AIMA Chapter 12.5 Bayes’ Rule and Its Use\", chapter12_5),\n",
        "  (\"AIMA Chapter 12.6 Naive Bayes Models\", chapter12_6),\n",
        "  (\"AIMA Chapter 12.7 The Wumpus World Revisited\", chapter12_7),\n",
        "  (\"SLP Chapter 3.1 N-Grams\", SLP_chapter3_1),\n",
        "  (\"SLP Chapter 3.2 Evaluating Language Models\", SLP_chapter3_2),\n",
        "  (\"SLP Chapter 3.3 Sampling sentences from a language model\", SLP_chapter3_3),\n",
        "  (\"SLP Chapter 3.4 Generalization and Zeros\", SLP_chapter3_4),\n",
        "  (\"SLP Chapter 3.5 Smoothing\", SLP_chapter3_5),\n",
        "  (\"SLP Chapter 3.7 Huge Language Models and Stupid Backoff\", SLP_chapter3_7),\n",
        "  (\"Module 10 Lecture Notes\", module10_lecture_notes),\n",
        "]\n",
        "\n",
        "module10_markdown = format_module(10, \"Probabilies and Language Models\", module10_sections, module10_definitions)\n",
        "display(Markdown(module10_markdown))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n 1. probabilistic Language Models (PLM), assign a probability to a sentence..compute the probability of a sentence or sequence of words, probability of an upcoming word \n\n2. Markov Assumption: the probability of word only depends on the previous k words, not the whole context, unigram=P(w1), bigram=P(w_i|w_i-1), trigram, 4-gram\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 = \"\"\"  \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module11_definitions = {\n",
        "\"term\" : \"your definition here\", \n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqtEs0gHs9sO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "cellView": "form",
        "outputId": "1bdbc5c6-0efa-48fa-cddd-fdadba21e5b0"
      },
      "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": null,
      "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  \n\n## Module 11 Definitions\n* **Term** - your definition here\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 = \"\"\" 1. machine learning: how to acquire a model from data/experience. a computer observes data, builds a model, uses the model as a hypothesis about the world,and as software that can solve problems \\n\n",
        " 2. model-based classification with Naive Bayes and Perceptrons\\n\n",
        " 3.to solve overfit problem, need to smooth or regularize the estimates\\n\n",
        " 4.Bayes rule lets us do diagnostic queries with causal probabilities; The naïve Bayes assumption takes all features to be independent given the class label;We can build classifiers out of a naïve Bayes model using training data;Smoothing estimates is important in real systems;\\n\n",
        " 5. Linear classifiers: inputs are feature values, each feature has a weight, sum is the activation, activation(x)_i=sum(w_i*f(x)_i), if activation is positive, output +1, negative, output -1\\n\n",
        " 6. comparison, naive bayes: builds a model training data; gives prediction probabilities; strong assumptions about feature independence; one pass through data\\n\n",
        " perceptrons: make less assumptions about data; mistake-driven learning; multiple passes through data(prediction); ofen more accurate\\n\n",
        " 7. a. In the perceptron training algorithm, training is error-driven- weight are only updated when the predicted label is incorrect\\n\n",
        "b. Inference procedure for the perceptron classification: compute a weighted sum, then apply a threshold.\\n\n",
        "c. Locally structured systems: the structure grows linearly in complexity\\n \"\"\"\n",
        "\n",
        "# Definitions\n",
        "module12_definitions = {\n",
        "\"data\" : \" labeled instances, training set, held out set(practice exam), test set(final exam)\",  \n",
        "\"Features\" : \" attribute-value pairs which characterize each x\",   \n",
        "\"Overfitting\" : \" fitting the training data very closely, but not generalizing well\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUc_LgzDvWf-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "881f3348-719d-4c05-8b77-3ac15d0544c4"
      },
      "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": null,
      "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 1. machine learning: how to acquire a model from data/experience. a computer observes data, builds a model, uses the model as a hypothesis about the world,and as software that can solve problems \n\n 2. model-based classification with Naive Bayes and Perceptrons\n\n 3.to solve overfit problem, need to smooth or regularize the estimates\n\n 4.Bayes rule lets us do diagnostic queries with causal probabilities; The naïve Bayes assumption takes all features to be independent given the class label;We can build classifiers out of a naïve Bayes model using training data;Smoothing estimates is important in real systems;\n\n 5. Linear classifiers: inputs are feature values, each feature has a weight, sum is the activation, activation(x)_i=sum(w_i*f(x)_i), if activation is positive, output +1, negative, output -1\n\n 6. comparison, naive bayes: builds a model training data; gives prediction probabilities; strong assumptions about feature independence; one pass through data\n\n perceptrons: make less assumptions about data; mistake-driven learning; multiple passes through data(prediction); ofen more accurate\n\n 7. a. In the perceptron training algorithm, training is error-driven- weight are only updated when the predicted label is incorrect\n\nb. Inference procedure for the perceptron classification: compute a weighted sum, then apply a threshold.\n\nc. Locally structured systems: the structure grows linearly in complexity\n \n\n## Module 12 Definitions\n* **Data** -  labeled instances, training set, held out set(practice exam), test set(final exam)\n* **Features** -  attribute-value pairs which characterize each x\n* **Overfitting** -  fitting the training data very closely, but not generalizing well\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVSQhTVSyYYO"
      },
      "source": [
        "# Module 13 - Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm4mfW82yaRH"
      },
      "source": [
        "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
        "\n",
        "slp_chapter5_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
        "\n",
        "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_2 = \"\"\" 1. XOR is non linear, percepton can solve and OR(linear), but cannot solve XOR\\n\n",
        "2. XOR can be solved by layered network; the hidden layer make it possible to linearly separate the positive and negative cases of XOR\\n\n",
        " \"\"\"\n",
        "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_5 = \"\"\" nerual language model: one-hot vector: V5=[0,0,0,0,1,0,0,...], only the index 5 location is 1. use the embedding weight matrix E x V5 get d dimensions embedding for the V5 word, \"\"\"\n",
        "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
        "\n",
        "# Lecture notes \n",
        "module13_lecture_notes = \"\"\" 1.gradient descent is a method that finds a minimum of a function by figuring out in which direction(in space of the parameters theta) the function's slope is rising the most steeply, and moving in the opposite direction\\n\n",
        "2. logistic regression, loss function is conveniently convex, has just one minimum. \\n\n",
        "3. a. Linear classifier: can bee used for both regression and classification\\n\n",
        "b. Difference between parametric and nonparametric models: a parametric model has a fixed size on the number of parameters.\\n\n",
        "c. In neural networks: nonlinear activation functions: make it possible to do the gradient calculation in backpropagation, as opposed to using step function which is not differentiable.\\n\n",
        "d. Help to learn nonlinear decision boundaries.\\n\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module13_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKWbCsbfxL3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "ce4c91c1-e38d-4c0d-9370-f9faf009ebd7"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n 1. XOR is non linear, percepton can solve and OR(linear), but cannot solve XOR\n\n2. XOR can be solved by layered network; the hidden layer make it possible to linearly separate the positive and negative cases of XOR\n\n \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 nerual language model: one-hot vector: V5=[0,0,0,0,1,0,0,...], only the index 5 location is 1. use the embedding weight matrix E x V5 get d dimensions embedding for the V5 word, \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 1.gradient descent is a method that finds a minimum of a function by figuring out in which direction(in space of the parameters theta) the function's slope is rising the most steeply, and moving in the opposite direction\n\n2. logistic regression, loss function is conveniently convex, has just one minimum. \n\n3. a. Linear classifier: can bee used for both regression and classification\n\nb. Difference between parametric and nonparametric models: a parametric model has a fixed size on the number of parameters.\n\nc. In neural networks: nonlinear activation functions: make it possible to do the gradient calculation in backpropagation, as opposed to using step function which is not differentiable.\n\nd. Help to learn nonlinear decision boundaries.\n\n\n## Module 13 Definitions\n* **Concept** - definition\n",
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYESi2kMybL8"
      },
      "source": [
        "# Module 14 - Natural Language Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIRJ4YYCzNHL"
      },
      "source": [
        "# Jurafsky and Martin, Chapter 9 “Sequence Processing with Recurrent Networks” (Sections 9.1-9.3)\n",
        "\n",
        "slp_chapter9_1 = \"\"\" perplexity to measure the quality of a LM \"\"\"\n",
        "slp_chapter9_2 = \"\"\" RNN, recurrent Neural Networks, is any network that contains a cycle within its network connections, the value of some unit is directly, or indirectly dependent on its own earlier outputs as input\\n\n",
        " \"\"\"\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 = \"\"\" the idea of vector semantics is s to represent a word as a point in a multidimensional semantic space that is derived (in ways we’ll see) from the distributions of word neighbors \"\"\"\n",
        "slp_chapter6_3 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_4 = \"\"\" Your summary here \"\"\"\n",
        "slp_chapter6_8 = \"\"\" before is a sparse long vector with dimensions corresponding to words in the vocabulary or documents in a collection; but EMBEDDINGS, short dense vectors\\n\n",
        "2. skip-gram with negative sampling (SGNS) algorithms,\\n\n",
        "3. The intuition of word2vec is that instead of counting how often each word w oc\u0002curs near, say, apricot, we’ll instead train a classifier on a binary prediction task: “Is word w likely to show up near apricot?” \\n\n",
        "4. We’ll see how to do neural networks in the next chapter, but word2vec is a much simpler model than the neural network language model, in two ways. First, word2vec simplifies the task (making it binary classification instead of word pre\u0002diction). Second, word2vec simplifies the architecture (training a logistic regression\n",
        "classifier instead of a multi-layer neural network with hidden layers that demand more sophisticated training algorithms). The intuition of skip-gram is:\\n\n",
        "a. Treat the target word and a neighboring context word as positive examples\\n\n",
        "b.  Randomly sample other words in the lexicon to get negative samples.\\n\n",
        "c.  Use logistic regression to train a classifier to distinguish those two cases.\\n\n",
        "d.  Use the learned weights as the embeddings.\\n\n",
        "5. Maximize the similarity of the target word, context word pairs (w, cpos) drawn from the positive examples; Minimize the similarity of the (w, cneg) pairs from the negative examples;\\n\n",
        "6. the noise words are the negative samples.\\n\n",
        "\n",
        "\"\"\"\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 = \"\"\" 1. Neural Language models advantages over n-gram LM: do not need smoothing; can handle much longer histories; can generalize over contexts of similar words; neural LMs tend to have much higher predictive accuracy than n-gram LMs\\n\n",
        " 2. neural LMs,  prior context is represented by embeddings, allow neural LMs to generalize better than n-gram LMs.(NN know cat and dog are similar embeddings, n gram cannot if no train)\\n\n",
        " 3.NN LMs trained using cross-entropy loss. training examples are simply word k-grams from the corpus, the first k-1 words are used as features, the last word is used as target label for the classification\\n\n",
        " 4. advantages: better results; larger N; they generalize across contexts;\n",
        " disadvantage: too many parapeters to accurately estimate; n-gram fail to generalize to related words; dimensionality/sparse statistics too large\\n\n",
        " 5. NLP: vector semantics. synonyms_words that have similar meanings; antonyms_words that have opposite meanings; connotations_words that have opposite meanings; semantic roles- buy, sell, pay are different parts of the same underlying purchasing event\\n\n",
        " 6. word net, vactor space models; polysemy: word that has multiple meanings.\\n\n",
        " 7. it is possible that no two words have absolutely identical meaning\\n\n",
        " 8. dense vectors, easier to use as features in machine learning, generalize better than storing explicit counts; do better at capturing synonymy;\n",
        " 9. a. benefit of using log probabilities instead of probabilities with a Categorical distribution: In certain instances, it’s easier to optimize over sums than products; Underflow results from dealing with small floating point numbers, which is avoided using log probabilities.\\n\n",
        "b. The stationary distribution of a Markov chain describes the distribution of Xt after a sufficiently long time that the distribution of Xt does not change any longer.\\n\n",
        "c. Cross Entropy: is an upper bound on the entropy. The more accurate a model is, the lower the cross entropy.\\n\n",
        "d. Vector representations of words using term-document matrices and word-word matrices, could only compare words that have similar meaning, but it cannot handle the problem that single word has multiple meanings (polysemy).\\n\n",
        "e. The purpose of laplace smoothing is to account for unseen words.\\n\"\"\"\n",
        "\n",
        "# Definitions\n",
        "module14_definitions = {\n",
        "\"concept\" : \"definition\",    \n",
        "}\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXNcHuKjyNXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cdf35a33-a206-4909-c612-c69c7e58539f"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/markdown": "# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n perplexity to measure the quality of a LM \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n RNN, recurrent Neural Networks, is any network that contains a cycle within its network connections, the value of some unit is directly, or indirectly dependent on its own earlier outputs as input\n\n \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 the idea of vector semantics is s to represent a word as a point in a multidimensional semantic space that is derived (in ways we’ll see) from the distributions of word neighbors \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 before is a sparse long vector with dimensions corresponding to words in the vocabulary or documents in a collection; but EMBEDDINGS, short dense vectors\n\n2. skip-gram with negative sampling (SGNS) algorithms,\n\n3. The intuition of word2vec is that instead of counting how often each word w oc\u0002curs near, say, apricot, we’ll instead train a classifier on a binary prediction task: “Is word w likely to show up near apricot?” \n\n4. We’ll see how to do neural networks in the next chapter, but word2vec is a much simpler model than the neural network language model, in two ways. First, word2vec simplifies the task (making it binary classification instead of word pre\u0002diction). Second, word2vec simplifies the architecture (training a logistic regression\nclassifier instead of a multi-layer neural network with hidden layers that demand more sophisticated training algorithms). The intuition of skip-gram is:\n\na. Treat the target word and a neighboring context word as positive examples\n\nb.  Randomly sample other words in the lexicon to get negative samples.\n\nc.  Use logistic regression to train a classifier to distinguish those two cases.\n\nd.  Use the learned weights as the embeddings.\n\n5. Maximize the similarity of the target word, context word pairs (w, cpos) drawn from the positive examples; Minimize the similarity of the (w, cneg) pairs from the negative examples;\n\n6. the noise words are the negative samples.\n\n\n\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 1.gradient descent is a method that finds a minimum of a function by figuring out in which direction(in space of the parameters theta) the function's slope is rising the most steeply, and moving in the opposite direction\n\n2. logistic regression, loss function is conveniently convex, has just one minimum. \n\n3. a. Linear classifier: can bee used for both regression and classification\n\nb. Difference between parametric and nonparametric models: a parametric model has a fixed size on the number of parameters.\n\nc. In neural networks: nonlinear activation functions: make it possible to do the gradient calculation in backpropagation, as opposed to using step function which is not differentiable.\n\nd. Help to learn nonlinear decision boundaries.\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": "ee569ad7-a074-42c2-bb9d-da190e323271"
      },
      "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": null,
      "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 1. Markov Decision Processes, fully-observable, not deterministic but stochastic (e.g. 0.8 right direction, 0.2 other direction)\n\n2. transition function: T(s,a,s'), also explicitly as probability P(s'|s,a), s' destination, s start point, a: action\n\n3. utility function/reward function: R(s,a,s')(e.g. -0.04 for every move)\n\n4. MDP: a sequential decision problem, a fully observable environment, with a stochastic transition model, that has additive rewards\n\n5. MDP need define: a set of states, a set of actions, transition function T, reward function R, initial state s0, one or more terminal states\n\n6. policy: a solution was a sequence of action that corresponded to the shortest path, but because of non-determinism, the solution to MDP is a policy, maps from a state onto the action to take if agent is in that state.(pi(s)=a)\n\n7. performance of agent in MDP: sum of the rewards for the transitions it takes.\n\n8. discounting: apply discount factor for later rewards to helps algorithms converge, e.g.sum_r=0.5^0*1+0.5^1*1+0.5^2*1+0.5^3*1.... 0.5 is the discounting factor.\n\n9.!!!! value of a state s =V*(s), value of a q-state(s,a)= Q*(s,a). Q*(s,a)=sum_s' (T(s,a,s')(R(s,a,s')+discount* V*(s'))), V*(s)=max Q*(s,a)\n10. value iteration: V=max_a Q(s,a)\n11. problem with value iteration: slow O(s^2 * A); max at each state rarely change; policy often converge long before the value \n\n12. policy iteration: step1: use fixed policy calculate value V_temp. step2: use V_temp udpate policy, pi(s)=argmax_a (V_temp). step3: repeat until policy converge\n\n13. a rational agent should choose the action that maximuzes its expected utility, given its knowledge\n\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 1. MDP: offline planning: determine all quantities through computation; need to know the details of the MDP; do not actually play the game\n\n2. reinforcement learning, important ideas: exploration, exploitation, regret, sampling difficulty\n\n3. RL is online learning, different with MDP, do not know T, R,\n\n4. RL: model based Learning:input policy, try get result, estimate T,R, then solve RL as MDP.\n5. RL: model free learning: passive reinforcement learning, use fixed policy,pi. every step record the discounted rewards, sum and average get the Value of the state.\n\n6. passive reinforcement learning: bad: wastes information about state connections; each state must be learned separately; takes long time to learn\n\n7.RL: temporal difference learning: update V(s) each time we experience a transition. TD is model-free way to do policy evaluation, mimicking bellman update when run sample averages\n\n8: TD learning bad: cannot turn values into policy, no Q value\n\n9. Active RL: goal: learn the optimal policy/value.   Q Learning: converge to optimal policy, off-policy learning \n\n10. improve Q-Learning for EXPLORATION VS EXPLOITATION, use greedy factor, with small probability, act randomly, large probability, act on current policy, random exploration has higher regret\n\n11. approximate Q-learning, use feature and weight\n\n## Module 9 Definitions\n* **Exploration** - try unknow actions to get information\n* ** exploitation** - you have to use what you know\n* ** regret** - even if you learn intelligently, you make mistakes\n* ** sampling** - because of chance, you have to try things repeatedly\n* ** difficulty** - learning can be much harder than solving a known MDP\n\n\n# Module 10: Probabilies and Language Models\n## AIMA Chapter 12.1 Acting under Uncertainty\n Your summary here \n\n## AIMA Chapter 12.2 Basic Probability Notation\n Your summary here \n\n## AIMA Chapter 12.3 Inference Using Full Joint Distributions\n Your summary here \n\n## AIMA Chapter 12.4 Independence\n Your summary here \n\n## AIMA Chapter 12.5 Bayes’ Rule and Its Use\n Your summary here \n\n## AIMA Chapter 12.6 Naive Bayes Models\n Your summary here \n\n## AIMA Chapter 12.7 The Wumpus World Revisited\n Your summary here \n\n## SLP Chapter 3.1 N-Grams\n Your summary here \n\n## SLP Chapter 3.2 Evaluating Language Models\n Your summary here \n\n## SLP Chapter 3.3 Sampling sentences from a language model\n Your summary here \n\n## SLP Chapter 3.4 Generalization and Zeros\n Your summary here \n\n## SLP Chapter 3.5 Smoothing\n Your summary here \n\n## SLP Chapter 3.7 Huge Language Models and Stupid Backoff\n Your summary here \n\n## Module 10 Lecture Notes\n 1. probabilistic Language Models (PLM), assign a probability to a sentence..compute the probability of a sentence or sequence of words, probability of an upcoming word \n\n2. Markov Assumption: the probability of word only depends on the previous k words, not the whole context, unigram=P(w1), bigram=P(w_i|w_i-1), trigram, 4-gram\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  \n\n## Module 11 Definitions\n* **Term** - your definition here\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 1. machine learning: how to acquire a model from data/experience. a computer observes data, builds a model, uses the model as a hypothesis about the world,and as software that can solve problems \n\n 2. model-based classification with Naive Bayes and Perceptrons\n\n 3.to solve overfit problem, need to smooth or regularize the estimates\n\n 4.Bayes rule lets us do diagnostic queries with causal probabilities; The naïve Bayes assumption takes all features to be independent given the class label;We can build classifiers out of a naïve Bayes model using training data;Smoothing estimates is important in real systems;\n\n 5. Linear classifiers: inputs are feature values, each feature has a weight, sum is the activation, activation(x)_i=sum(w_i*f(x)_i), if activation is positive, output +1, negative, output -1\n\n 6. comparison, naive bayes: builds a model training data; gives prediction probabilities; strong assumptions about feature independence; one pass through data\n\n perceptrons: make less assumptions about data; mistake-driven learning; multiple passes through data(prediction); ofen more accurate\n\n 7. a. In the perceptron training algorithm, training is error-driven- weight are only updated when the predicted label is incorrect\n\nb. Inference procedure for the perceptron classification: compute a weighted sum, then apply a threshold.\n\nc. Locally structured systems: the structure grows linearly in complexity\n \n\n## Module 12 Definitions\n* **Data** -  labeled instances, training set, held out set(practice exam), test set(final exam)\n* **Features** -  attribute-value pairs which characterize each x\n* **Overfitting** -  fitting the training data very closely, but not generalizing well\n\n\n# Module 13: Neural Networks\n## SLP Chapter 5.1 Classification: the sigmoid\n Your summary here \n\n## SLP Chapter 5.2 Learning in Logistic Regression\n Your summary here \n\n## SLP Chapter 5.3 The cross-entropy loss function\n Your summary here \n\n## SLP Chapter 5.4 Gradient Descent\n Your summary here \n\n## SLP Chapter 5.5 Regularization\n Your summary here \n\n## SLP Chapter 5.6 Multinomial logistic regression\n Your summary here \n\n## SLP Chapter 7.1 Units\n Your summary here \n\n## SLP Chapter 7.2 The XOR problem\n 1. XOR is non linear, percepton can solve and OR(linear), but cannot solve XOR\n\n2. XOR can be solved by layered network; the hidden layer make it possible to linearly separate the positive and negative cases of XOR\n\n \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 nerual language model: one-hot vector: V5=[0,0,0,0,1,0,0,...], only the index 5 location is 1. use the embedding weight matrix E x V5 get d dimensions embedding for the V5 word, \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 1.gradient descent is a method that finds a minimum of a function by figuring out in which direction(in space of the parameters theta) the function's slope is rising the most steeply, and moving in the opposite direction\n\n2. logistic regression, loss function is conveniently convex, has just one minimum. \n\n3. a. Linear classifier: can bee used for both regression and classification\n\nb. Difference between parametric and nonparametric models: a parametric model has a fixed size on the number of parameters.\n\nc. In neural networks: nonlinear activation functions: make it possible to do the gradient calculation in backpropagation, as opposed to using step function which is not differentiable.\n\nd. Help to learn nonlinear decision boundaries.\n\n\n## Module 13 Definitions\n* **Concept** - definition\n\n\n# Module 14: Natural Language Processing\n## SLP Chapter 9.1 Language Models Revisited\n perplexity to measure the quality of a LM \n\n## SLP Chapter 9.2 Recurrent Neural Networks\n RNN, recurrent Neural Networks, is any network that contains a cycle within its network connections, the value of some unit is directly, or indirectly dependent on its own earlier outputs as input\n\n \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 the idea of vector semantics is s to represent a word as a point in a multidimensional semantic space that is derived (in ways we’ll see) from the distributions of word neighbors \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 before is a sparse long vector with dimensions corresponding to words in the vocabulary or documents in a collection; but EMBEDDINGS, short dense vectors\n\n2. skip-gram with negative sampling (SGNS) algorithms,\n\n3. The intuition of word2vec is that instead of counting how often each word w oc\u0002curs near, say, apricot, we’ll instead train a classifier on a binary prediction task: “Is word w likely to show up near apricot?” \n\n4. We’ll see how to do neural networks in the next chapter, but word2vec is a much simpler model than the neural network language model, in two ways. First, word2vec simplifies the task (making it binary classification instead of word pre\u0002diction). Second, word2vec simplifies the architecture (training a logistic regression\nclassifier instead of a multi-layer neural network with hidden layers that demand more sophisticated training algorithms). The intuition of skip-gram is:\n\na. Treat the target word and a neighboring context word as positive examples\n\nb.  Randomly sample other words in the lexicon to get negative samples.\n\nc.  Use logistic regression to train a classifier to distinguish those two cases.\n\nd.  Use the learned weights as the embeddings.\n\n5. Maximize the similarity of the target word, context word pairs (w, cpos) drawn from the positive examples; Minimize the similarity of the (w, cneg) pairs from the negative examples;\n\n6. the noise words are the negative samples.\n\n\n\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 1.gradient descent is a method that finds a minimum of a function by figuring out in which direction(in space of the parameters theta) the function's slope is rising the most steeply, and moving in the opposite direction\n\n2. logistic regression, loss function is conveniently convex, has just one minimum. \n\n3. a. Linear classifier: can bee used for both regression and classification\n\nb. Difference between parametric and nonparametric models: a parametric model has a fixed size on the number of parameters.\n\nc. In neural networks: nonlinear activation functions: make it possible to do the gradient calculation in backpropagation, as opposed to using step function which is not differentiable.\n\nd. Help to learn nonlinear decision boundaries.\n\n\n## Module 14 Definitions\n* **Concept** - definition\n",
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