Overview

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. - PATHFINDING ALGORITHMS -Section 1 - Breadth-First Search (BFS)what is breadth-first search algorithmwhy to use graph algorithms in AISection 2 - Depth-First Search (DFS)what is depth-first search algorithmimplementation with iteration and with recursiondepth-first search stack memory visualizationmaze escape applicationSection 3 - Iterative Deepening Depth-First Search (IDDFS)what is iterative deepening depth-first search algorithmSection 4 - A* Search Algorithmwhat is A* search algorithmwhat is the difference between Dijkstra's algorithm and A* searchwhat is a heuristicManhattan distance and Euclidean distance - OPTIMIZATION -Section 5 - Optimization Approachesbasic optimization algorithmsbrute-force searchhill climbing algorithm- META-HEURISTICS -Section 6 - Simulated Annealingwhat is simulated annealinghow to find the extremum of functionshow to solve combinatorial optimization problemstravelling salesman problem (TSP)Section 7 - Genetic Algorithmswhat are genetic algorithmsartificial evolution and natural selectioncrossover and mutationsolving the knapsack problemSection 8 - Particle Swarm Optimization (PSO)what is swarm intelligencewhat is the Particle Swarm Optimization algorithm- GAMES AND GAME TREES -Section 9 - Game Treeswhat are game treeshow to construct game treesSection 10 - Minimax Algorithm and Game Engineswhat is the minimax algorithmwhat is the problem with game trees?using the alpha-beta pruning approachchess problemSection 11 - Tic Tac Toe with MinimaxTic Tac Toe game and its implementationusing minimax algorithmIn the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.The last topic will be about minimax algorithm and how to use this technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toe game together in the end.Thanks for joining the course, let's get started!

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