adds intro section to learning notebook

SnShine · SnShine · commit 551ce42c45fe · 2016-07-09T23:48:24.000+05:30
diff --git a/learning.ipynb b/learning.ipynb
@@ -1,14 +1,56 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": false
    },
+   "source": [
+    "# Learning\n",
+    "\n",
+    "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from learning module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
-    "import learning"
+    "from learning import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review\n",
+    "\n",
+    "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n",
+    "\n",
+    "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n",
+    "\n",
+    "There are three types of feedback that determine the three main types of learning:\n",
+    "\n",
+    "* **Supervised Learning**:\n",
+    "\n",
+    "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n",
+    "\n",
+    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the aggent. The agent then learns a function that maps from an input image to one of those strings.\n",
+    "\n",
+    "* **Unsupervised Learning**:\n",
+    "\n",
+    "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n",
+    "\n",
+    "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n",
+    "\n",
+    "* **Reinforcement Learning**:\n",
+    "\n",
+    "In Reinforcement Learning the agent from a series of reinforcements—rewards or punishments.\n",
+    "\n",
+    "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it."
    ]
   },
   {
@@ -38,6 +80,10 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.5.1"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.1"
   }
  },
  "nbformat": 4,
