Added Introduction

Author: snrazavi

Introduction.ipynb

@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# AI Course"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Contents\n",
+    "\n",
+    "### First part:\n",
+    "- Search problems\n",
+    "    - Uninformed search strategies:\n",
+    "        - BFS, DFS, UCS, DLS, IDS\n",
+    "    - Informed search strategies:\n",
+    "        - Gready, A-star\n",
+    "    - Local search:\n",
+    "        - Hill climbing, Simulated Annealing, Genetic algorithms\n",
+    "    - Adversarial search and games\n",
+    "        - MiniMax, alpha-beta pruning"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Second part:\n",
+    "- Machine learning\n",
+    "    - Supervised learning:\n",
+    "        - KNN, Naive Bayes, Desision Trees, SVM, Neural Nets\n",
+    "    - Unsupervised learning:\n",
+    "        - k-Means clustering\n",
+    "    - Reinforcement learning\n",
+    "        - Q-learning"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Example problems\n",
+    "- Search:\n",
+    "    - N-Puzzle, N-Queens, TSP\n",
+    "- Games:\n",
+    "    - Pacman, Chess\n",
+    "- Learning:\n",
+    "    - Pacman, Chess, etc."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Lesson 1: Uninformed (blind) search strategies\n",
+    "- Implementing data structures like stack, queue and priority queue\n",
+    "- Implementing N-Puzzle (8-Puzzle)\n",
+    "- Implementing uninformed search strategies: BFS, DFS, UCS, DLS, IDS\n",
+    "- Programming assignment"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Lesson 2: Informed search strategies\n",
+    "- Implementing informed search strategies: Greedy, A-star\n",
+    "- Programming assignment"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Lesson 3: Local search\n",
+    "- Implementing N-Queens and TSP(a graphical implementation)\n",
+    "- Implementing local search strategies: hill climbing, simulated annealing and genetic algorithms\n",
+    "- Programming assignment"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Lesson 4: Adversarial search and games\n",
+    "- Implementing Otello\n",
+    "- Implementing adversarial search algoritms: Minimax and alpha-beta prunning\n",
+    "- Programming assignment"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Prerequisite\n",
+    "- Basic knowledge of programming (Python)\n",
+    "- Basic knowledge of data structures and algorithms"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## After first part:\n",
+    "- You will have a good understanding of basic AI techniques (solving problems using searching)\n",
+    "- You will become a real python programmer and most importantly a real programmer!\n",
+    "- You will see a lot of programming challenges and you will learn how to solve them"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}