{
 "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",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lzaavW96tcxM",
    "outputId": "f95775db-bd33-4aff-8211-81c6eb75fb31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The example summary is 116 words long, so you could write an additional 384 words.\n"
     ]
    }
   ],
   "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)))"
   ]
  },
  {
   "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",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8DvZ57A-djo1",
    "outputId": "dfdd04be-842a-4fc3-8aae-39ec44fe3bc7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length in words is 499\n",
      "The length in characters is 3027\n"
     ]
    }
   ],
   "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 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))"
   ]
  },
  {
   "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",
   "execution_count": 3,
   "metadata": {
    "id": "2RECnJArJew4"
   },
   "outputs": [],
   "source": [
    "name = \"\"\n",
    "permission = True\n",
    "anonymous = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GZZXyrV-xuAp"
   },
   "source": [
    "# Module 8 - Markov Decision Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Y2NfE1CKxzS-"
   },
   "outputs": [],
   "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 = \"\"\" MDP is sequential decision problems in stochastic environment.\\\n",
    "defined by transition model, needs probabilistic outcome of action, reward function for reward in each state \"\"\"  + sample_equation\n",
    "chapter16_2 =  \"\"\" Value iteration solves set of equations relating the utlity of each state, to its neightbors.,/ \"\"\"\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 = \"\"\" markov decision process:1.set of state S, start state S0,<br>\n",
    "set of action A, transtion T(s,a,s'), rewardsR(s,a,s')(and optional discount y)<br>\n",
    "MDP quantities, policy=Choice of action at each state, utility=sum of (discounted)reward(+or-)<br>\n",
    "non-deterministic search problems<br>\n",
    "value iteration: every iteration updates both values and implicitiy the policy<br>\n",
    "take the max over actions implicitly recomputes it, don't track policy<br>\n",
    "O(S^2 A)<br>\n",
    "policy iteration O(S^3): serveral passes that update utilities with fixed policy.each pass is fast since consider 1 action<br>\n",
    "choose new policy after policy is evaluated, new policy is better<br>\n",
    "good for compute for particular policy or turn values into policy<br>\n",
    "both computes optimal values and use 1-step lookahead expectimax fragment<br>\n",
    "differ in pulg in a fixed policy or max over actions<br>\n",
    "policy iteration converges in fewer iteration with greater cost.\n",
    "<br>\n",
    "proper policy might not always exists<br>\n",
    "MEU: agenet should maximize the weighted average of their utilities\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 of state\" : \"exoected syn if rewards when an optimal policy is executed from that state\",\n",
    "\"utility function\" : \"not correct since user defined but optimal if it describes state properly\",\n",
    "\"MDP\" : \"a seqiemtoa; decision prob for a fully observale environment with a stochastic transition model with additive rewards\",\n",
    "\"policy iteration\": \"alternate between calculating utility of states under curr policy\\\n",
    "improve curr policy with respect to curr utilities\",\n",
    "\"policy\": \"solution of an MDP associates a decisin with every state that agent might reach\",\n",
    "}\n",
    "\n"
   ]
  },
  {
   "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",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 921
    },
    "id": "gYYeNcdM_maE",
    "outputId": "e6843fd5-5808-4d76-9dba-b71025cc4a12"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Module 8: Markov Decision Processes\n",
       "## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n",
       " MDP is sequential decision problems in stochastic environment.defined by transition model, needs probabilistic outcome of action, reward function for reward in each state  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n",
       "\n",
       "## AIMA Chapter 16.2 The Basis of Utility Theory\n",
       " Value iteration solves set of equations relating the utlity of each state, to its neightbors.,/ \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",
       " markov decision process:1.set of state S, start state S0,<br>\n",
       "set of action A, transtion T(s,a,s'), rewardsR(s,a,s')(and optional discount y)<br>\n",
       "MDP quantities, policy=Choice of action at each state, utility=sum of (discounted)reward(+or-)<br>\n",
       "non-deterministic search problems<br>\n",
       "value iteration: every iteration updates both values and implicitiy the policy<br>\n",
       "take the max over actions implicitly recomputes it, don't track policy<br>\n",
       "O(S^2 A)<br>\n",
       "policy iteration O(S^3): serveral passes that update utilities with fixed policy.each pass is fast since consider 1 action<br>\n",
       "choose new policy after policy is evaluated, new policy is better<br>\n",
       "good for compute for particular policy or turn values into policy<br>\n",
       "both computes optimal values and use 1-step lookahead expectimax fragment<br>\n",
       "differ in pulg in a fixed policy or max over actions<br>\n",
       "policy iteration converges in fewer iteration with greater cost.\n",
       "<br>\n",
       "proper policy might not always exists<br>\n",
       "MEU: agenet should maximize the weighted average of their utilities\n",
       " \n",
       "\n",
       "## Module 8 Definitions\n",
       "* **Utility of state** - exoected syn if rewards when an optimal policy is executed from that state\n",
       "* **Utility function** - not correct since user defined but optimal if it describes state properly\n",
       "* **Mdp** - a seqiemtoa; decision prob for a fully observale environment with a stochastic transition model with additive rewards\n",
       "* **Policy iteration** - alternate between calculating utility of states under curr policyimprove curr policy with respect to curr utilities\n",
       "* **Policy** - solution of an MDP associates a decisin with every state that agent might reach\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xRbdqDox2zD"
   },
   "source": [
    "#Module 9 - Reinforcement Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "DpBvu7EMx7sy"
   },
   "outputs": [],
   "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 = \"\"\" exploration\" : try unknown actions to get info,<br>\n",
    "explitation : eventually use what you know,<br>\n",
    "regret : learn intelligently but still makes mistakes, mesure of total mistake cost, whcich is\\\n",
    "diff between your expected reward+suboptimality and optimal(expected) rewards<br>\n",
    "sampling : try things repeatedly,<br>\n",
    "difficulty\" : \"learning harder than solving a known MDP<br>\n",
    "reinforcement learning receive feedback in the form of rewards<br>\n",
    "agent utility defined by reward function<br>\n",
    "must act to maximize expected rewards<br>\n",
    "learning based on boserved samples of outcome<br>\n",
    "assume set of state, actions, model T, reward R<br>\n",
    "Model based learning learn an approx model based on experience. solve values as if the learned model is correct<br>\n",
    "1.learn empirical MDP model 2. solve the learned MDP<br>\n",
    "Direct evluaiton(direct utility estimation) goal is to compute values for each state under policy pie by average observed sample values<br>\n",
    "Good:easy to understand, no need to know T or R, compute correct avg val using just sample transtions<br>\n",
    "Bad: wasite info about state connection. each state must learn separately. long time<br>\n",
    "passiva reinfor. learning don't know Transtion or reward, goal is to learn state values. not offline training, take action.<br>\n",
    "compute value for each state under policy pi, needs pi and all the states can be reached by agent<br>\n",
    "active reinfor. learning is full reinfor learning: optimal policy like value iteration<br>\n",
    "don't know R or T, goal is learn the optimal policy and value. user take actions<br>\n",
    "Q-learning converges to optimal policy even if you act suboptimally. called off policy learning<br>\n",
    "basic Q keeps a table of all q-values<br>\n",
    "Q: need to explore enough, eventually make learning rate small enough but not decresing too quickly<br>\n",
    "linear value function use a feature representation and write a func for any state using few weights.<br>\n",
    "adv: experience is summed up in a few powerful num<br>\n",
    "disadv: states may share features but very diff in values<br>\n",
    "Approximate Q Learning<br>\n",
    "no guarantee that q learning with linear function approx to converge to optilmal policy<br>\n",
    "adpative dynamic programming(ADP) and temporal difference(TD) programming, the utility of a sate is adjused locally wo agree\\\n",
    "with the utility of at least one success state<br>\n",
    "\n",
    " \"\"\"\n",
    "\n",
    "# Each definition should be at most 25 words long.\n",
    "module9_definitions = {\n",
    "\"deterministic policy\" : \"a mapping from state sto actions\",\n",
    "\"exploration\" : \"your definition here\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ydOK__lwWgdp",
    "outputId": "466e53cc-a98e-40e9-e719-d1dcdf206a54"
   },
   "outputs": [
    {
     "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",
       " exploration\" : try unknown actions to get info,<br>\n",
       "explitation : eventually use what you know,<br>\n",
       "regret : learn intelligently but still makes mistakes, mesure of total mistake cost, whcich isdiff between your expected reward+suboptimality and optimal(expected) rewards<br>\n",
       "sampling : try things repeatedly,<br>\n",
       "difficulty\" : \"learning harder than solving a known MDP<br>\n",
       "reinforcement learning receive feedback in the form of rewards<br>\n",
       "agent utility defined by reward function<br>\n",
       "must act to maximize expected rewards<br>\n",
       "learning based on boserved samples of outcome<br>\n",
       "assume set of state, actions, model T, reward R<br>\n",
       "Model based learning learn an approx model based on experience. solve values as if the learned model is correct<br>\n",
       "1.learn empirical MDP model 2. solve the learned MDP<br>\n",
       "Direct evluaiton(direct utility estimation) goal is to compute values for each state under policy pie by average observed sample values<br>\n",
       "Good:easy to understand, no need to know T or R, compute correct avg val using just sample transtions<br>\n",
       "Bad: wasite info about state connection. each state must learn separately. long time<br>\n",
       "passiva reinfor. learning don't know Transtion or reward, goal is to learn state values. not offline training, take action.<br>\n",
       "compute value for each state under policy pi, needs pi and all the states can be reached by agent<br>\n",
       "active reinfor. learning is full reinfor learning: optimal policy like value iteration<br>\n",
       "don't know R or T, goal is learn the optimal policy and value. user take actions<br>\n",
       "Q-learning converges to optimal policy even if you act suboptimally. called off policy learning<br>\n",
       "basic Q keeps a table of all q-values<br>\n",
       "Q: need to explore enough, eventually make learning rate small enough but not decresing too quickly<br>\n",
       "linear value function use a feature representation and write a func for any state using few weights.<br>\n",
       "adv: experience is summed up in a few powerful num<br>\n",
       "disadv: states may share features but very diff in values<br>\n",
       "Approximate Q Learning<br>\n",
       "no guarantee that q learning with linear function approx to converge to optilmal policy<br>\n",
       "adpative dynamic programming(ADP) and temporal difference(TD) programming, the utility of a sate is adjused locally wo agreewith the utility of at least one success state<br>\n",
       "\n",
       " \n",
       "\n",
       "## Module 9 Definitions\n",
       "* **Deterministic policy** - a mapping from state sto actions\n",
       "* **Exploration** - your definition here\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "46MQLR-Gx8dZ"
   },
   "source": [
    "#Module 10 - Probabilities and Language Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "BN0b1eVYx-nd"
   },
   "outputs": [],
   "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 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "\n",
    "# Each definition should be at most 25 words long.\n",
    "module10_definitions = {\n",
    "\"observed variables(evidence)\" : \"agent knows certain things about the state of the world like sensor\",\n",
    "\"unobserved variables(state)\" : \"agent reason about other aspect like where an object is\",\n",
    "\"unobserved variables(state)\" : \"agent knows somthing about how known variable relate to unknown variables\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lvtN3p7ET3Tx",
    "outputId": "4bcc20c5-50ff-4674-a3bf-eeb1b47e4ac1"
   },
   "outputs": [
    {
     "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",
       " Your summary here \n",
       "\n",
       "## Module 10 Definitions\n",
       "* **Observed variables(evidence)** - agent knows certain things about the state of the world like sensor\n",
       "* **Unobserved variables(state)** - agent knows somthing about how known variable relate to unknown variables\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WHREsHSSyA-h"
   },
   "source": [
    "#Module 11 - Probabilistic Reasoning and Bayes’ Nets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "tuFC8w7CyCns"
   },
   "outputs": [],
   "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 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Definitions\n",
    "module11_definitions = {\n",
    "\"concept\" : \"definition\",    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "vqtEs0gHs9sO",
    "outputId": "dac96afb-d3db-41d0-f4e0-e7e90de3edfe"
   },
   "outputs": [
    {
     "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",
       " Your summary here \n",
       "\n",
       "## Module 11 Definitions\n",
       "* **Concept** - definition\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F3FPMzG9yUls"
   },
   "source": [
    "#Module 12 - Naive Bayes and Perceptrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "iEIOpL7vyWcc"
   },
   "outputs": [],
   "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 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Definitions\n",
    "module12_definitions = {\n",
    "\"concept\" : \"definition\",    \n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KUc_LgzDvWf-",
    "outputId": "828a35e6-905f-41fa-8215-d2068769fad6"
   },
   "outputs": [
    {
     "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",
       " Your summary here \n",
       "\n",
       "## Module 12 Definitions\n",
       "* **Concept** - definition\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qVSQhTVSyYYO"
   },
   "source": [
    "# Module 13 - Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Wm4mfW82yaRH"
   },
   "outputs": [],
   "source": [
    "# Jurafsky and Martin, Speech and Language Processing Chapter 5 “Logistic Regression” (5.1-5.6)\n",
    "\n",
    "slp_chapter5_1 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter5_2 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter5_3 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter5_4 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter5_5 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter5_6 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Jurafsky and Martin, Chapter 7 “Neural Networks and Neural Language Models”\n",
    "\n",
    "slp_chapter7_1 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_2 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_3 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_4 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_5 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_6 = \"\"\" Your summary here \"\"\"\n",
    "slp_chapter7_7 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Lecture notes \n",
    "module13_lecture_notes = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Definitions\n",
    "module13_definitions = {\n",
    "\"concept\" : \"definition\",    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "kKWbCsbfxL3R",
    "outputId": "38568f12-7a4b-4c6b-88db-1d14fd140a10"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Module 13: Neural Networks\n",
       "## SLP Chapter 5.1 Classification: the sigmoid\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.2 Learning in Logistic Regression\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.3 The cross-entropy loss function\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.4 Gradient Descent\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.5 Regularization\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.6 Multinomial logistic regression\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.1 Units\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.2 The XOR problem\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.3 Feedforward Neural Networks\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.5 Feedforward Neural Language Modeling\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.6 Training Neural Nets\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.7 Training the neural language model\n",
       " Your summary here \n",
       "\n",
       "## Module 13 Lecture Notes\n",
       " Your summary here \n",
       "\n",
       "## Module 13 Definitions\n",
       "* **Concept** - definition\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RYESi2kMybL8"
   },
   "source": [
    "# Module 14 - Natural Language Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "qIRJ4YYCzNHL"
   },
   "outputs": [],
   "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 = \"\"\" Your summary here \"\"\"\n",
    "\n",
    "# Definitions\n",
    "module14_definitions = {\n",
    "\"concept\" : \"definition\",    \n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 945
    },
    "id": "lXNcHuKjyNXa",
    "outputId": "50b191c2-c1af-4ac0-a9ae-f7ec24dd2bd9"
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    {
     "data": {
      "text/markdown": [
       "# Module 14: Natural Language Processing\n",
       "## SLP Chapter 9.1 Language Models Revisited\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 9.2 Recurrent Neural Networks\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 9.3 RNNs as Language Models\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.1 Lexical Semantics\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.2 Vector Semantics\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.3 Words and Vectors\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.4 Cosine for measuring similarity\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.8 Word2vec\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.10 Semantic properties of embeddings\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.11 Bias and Embeddings\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 6.12 Evaluating Vector Models\n",
       " Your summary here \n",
       "\n",
       "## Module 13 Lecture Notes\n",
       " Your summary here \n",
       "\n",
       "## Module 14 Definitions\n",
       "* **Concept** - definition\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "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))"
   ]
  },
  {
   "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",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "T3cm1WV542_a",
    "outputId": "a2d8bed4-ccf5-4794-ad40-b869bf2a07f1"
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Module 8: Markov Decision Processes\n",
       "## AIMA Chapter 16.1 Combining Beliefs and Desires under Uncertainty\n",
       " MDP is sequential decision problems in stochastic environment.defined by transition model, needs probabilistic outcome of action, reward function for reward in each state  $P(\\textnormal{RESULT}(a)=s') = \\sum_s P(s)P(s'|s,a)$ \n",
       "\n",
       "## AIMA Chapter 16.2 The Basis of Utility Theory\n",
       " Value iteration solves set of equations relating the utlity of each state, to its neightbors.,/ \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",
       " markov decision process:1.set of state S, start state S0,<br>\n",
       "set of action A, transtion T(s,a,s'), rewardsR(s,a,s')(and optional discount y)<br>\n",
       "MDP quantities, policy=Choice of action at each state, utility=sum of (discounted)reward(+or-)<br>\n",
       "non-deterministic search problems<br>\n",
       "value iteration: every iteration updates both values and implicitiy the policy<br>\n",
       "take the max over actions implicitly recomputes it, don't track policy<br>\n",
       "O(S^2 A)<br>\n",
       "policy iteration O(S^3): serveral passes that update utilities with fixed policy.each pass is fast since consider 1 action<br>\n",
       "choose new policy after policy is evaluated, new policy is better<br>\n",
       "good for compute for particular policy or turn values into policy<br>\n",
       "both computes optimal values and use 1-step lookahead expectimax fragment<br>\n",
       "differ in pulg in a fixed policy or max over actions<br>\n",
       "policy iteration converges in fewer iteration with greater cost.\n",
       "<br>\n",
       "proper policy might not always exists<br>\n",
       "MEU: agenet should maximize the weighted average of their utilities\n",
       " \n",
       "\n",
       "## Module 8 Definitions\n",
       "* **Utility of state** - exoected syn if rewards when an optimal policy is executed from that state\n",
       "* **Utility function** - not correct since user defined but optimal if it describes state properly\n",
       "* **Mdp** - a seqiemtoa; decision prob for a fully observale environment with a stochastic transition model with additive rewards\n",
       "* **Policy iteration** - alternate between calculating utility of states under curr policyimprove curr policy with respect to curr utilities\n",
       "* **Policy** - solution of an MDP associates a decisin with every state that agent might reach\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",
       " exploration\" : try unknown actions to get info,<br>\n",
       "explitation : eventually use what you know,<br>\n",
       "regret : learn intelligently but still makes mistakes, mesure of total mistake cost, whcich isdiff between your expected reward+suboptimality and optimal(expected) rewards<br>\n",
       "sampling : try things repeatedly,<br>\n",
       "difficulty\" : \"learning harder than solving a known MDP<br>\n",
       "reinforcement learning receive feedback in the form of rewards<br>\n",
       "agent utility defined by reward function<br>\n",
       "must act to maximize expected rewards<br>\n",
       "learning based on boserved samples of outcome<br>\n",
       "assume set of state, actions, model T, reward R<br>\n",
       "Model based learning learn an approx model based on experience. solve values as if the learned model is correct<br>\n",
       "1.learn empirical MDP model 2. solve the learned MDP<br>\n",
       "Direct evluaiton(direct utility estimation) goal is to compute values for each state under policy pie by average observed sample values<br>\n",
       "Good:easy to understand, no need to know T or R, compute correct avg val using just sample transtions<br>\n",
       "Bad: wasite info about state connection. each state must learn separately. long time<br>\n",
       "passiva reinfor. learning don't know Transtion or reward, goal is to learn state values. not offline training, take action.<br>\n",
       "compute value for each state under policy pi, needs pi and all the states can be reached by agent<br>\n",
       "active reinfor. learning is full reinfor learning: optimal policy like value iteration<br>\n",
       "don't know R or T, goal is learn the optimal policy and value. user take actions<br>\n",
       "Q-learning converges to optimal policy even if you act suboptimally. called off policy learning<br>\n",
       "basic Q keeps a table of all q-values<br>\n",
       "Q: need to explore enough, eventually make learning rate small enough but not decresing too quickly<br>\n",
       "linear value function use a feature representation and write a func for any state using few weights.<br>\n",
       "adv: experience is summed up in a few powerful num<br>\n",
       "disadv: states may share features but very diff in values<br>\n",
       "Approximate Q Learning<br>\n",
       "no guarantee that q learning with linear function approx to converge to optilmal policy<br>\n",
       "adpative dynamic programming(ADP) and temporal difference(TD) programming, the utility of a sate is adjused locally wo agreewith the utility of at least one success state<br>\n",
       "\n",
       " \n",
       "\n",
       "## Module 9 Definitions\n",
       "* **Deterministic policy** - a mapping from state sto actions\n",
       "* **Exploration** - your definition here\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",
       " Your summary here \n",
       "\n",
       "## Module 10 Definitions\n",
       "* **Observed variables(evidence)** - agent knows certain things about the state of the world like sensor\n",
       "* **Unobserved variables(state)** - agent knows somthing about how known variable relate to unknown variables\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",
       " Your summary here \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",
       " Your summary here \n",
       "\n",
       "## Module 12 Definitions\n",
       "* **Concept** - definition\n",
       "\n",
       "\n",
       "# Module 13: Neural Networks\n",
       "## SLP Chapter 5.1 Classification: the sigmoid\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.2 Learning in Logistic Regression\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.3 The cross-entropy loss function\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.4 Gradient Descent\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.5 Regularization\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 5.6 Multinomial logistic regression\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.1 Units\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.2 The XOR problem\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.3 Feedforward Neural Networks\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.4 Feedforward networks for NLP: Classification\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.5 Feedforward Neural Language Modeling\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.6 Training Neural Nets\n",
       " Your summary here \n",
       "\n",
       "## SLP Chapter 7.7 Training the neural language model\n",
       " Your summary here \n",
       "\n",
       "## Module 13 Lecture Notes\n",
       " 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 13 Lecture Notes\n",
       " Your summary here \n",
       "\n",
       "## Module 14 Definitions\n",
       "* **Concept** - definition\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
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