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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 detect cancer for example. We may construct algorithms that can have a very very good guess about stocks movement in the market.
In 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 these 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-toegame together in the end.
LAST UPDATE OF THE COURSE: 2016 october
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|Section 1: Introduction|
What is AI good for?
|Section 2: Graph-Search Algorithms|
Graph theory introduction
Breadth-first search introductionPreview
Breadt-first search implementation
Depth-first search introduction
Depth-first search implementation I - with stack
Depth-first search implementation II - with recursion
Enhanced search algorithms introduction
Iterative deepening depth-first search (IDDFS)
A* search implementation I
A* search implementation II
A* search implementation III
|Section 3: Basic Search / Optimization Algorithms|
Brute-force search introduction
Brute-force search example
Stochastic search introduction
Stochastic search example
Hill climbing introduction
Hill climbing example
|Section 4: Meta-Heuristic Optimization Methods|
Heuristics VS meta-heuristics
Tabu search introduction
---------------- Simulated Annealing ------------------
Simulated annealing introduction
Simulated annealing - function extremum I
Simulated annealing - function extremum II
Simulated annealing - function extremum III
Travelling salesman problem I - city
Travelling salesman problem II - tour
Travelling salesman problem III - annealing algorithm
Travelling salesman problem IV - testing
----------------- Genetic Algorithms -------------------
Genetic algorithms introduction - basics
Genetic algorithms introduction - chromosomes
Genetic algorithms introduction - crossover
Genetic algorithms introduction - mutation
Genetic algorithms introduction - the algorithm
Genetic algorithm implementation I - individual
Genetic algorithm implementation II - population
Genetic algorithm implementation III - the algorithm
Genetic algorithm implementation IV - testing
Genetic algorithm implementation V - function optimum
---------------- Swarm Optimization ------------------
Swarm intelligence intoduction
Partical swarm optimization introduction I - basics
Partical swarm optimization introduction II - the algorithm
Particle swarm optimization implementation I - particle
Particle swarm optimization implementation II - initialize
Particle swarm optimization implementation III - the algorithm
Particle swarm optimization implementation IV - testing
|Section 5: Minimax Algorithm - Game Engines|
Game trees introduction
Minimax algorithm introduction - basics
Minimax algorithm introduction - the algorithm
Minimax algorithm introduction - relation with tic-tac-toe
Alpha-beta pruning introduction
|Section 6: Tic-Tac-Toe Game|
About the game
Constants and Player
Board class I
Board class II
|Section 7: Source code|
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My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.