COURSE INFORMATION
COURSE
Basics and Games in Java
DESCRIPTION
This course is about the fundamental concepts of artificial intelligence. For example, learning algorithms can recognize patterns which can help detect cancer. We may construct algorithms that can make very good guesses about stocks movement in the market.
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PREREQUISITES
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Basic Java (SE)
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Some basic algorithms (maximum/ minimum finding)
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Basic math (functions)
CURRICULUM
INTRODUCTION
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Introduction -
What is AI good for?
GRAPH-SEARCH ALGORITHMS
- Breadth-first search introduction
- 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 introduction
BASIC SEARCH & OPTIMIZATION ALGORITHMS
- Brute-force search introduction
- Brute-force search example
- Stochastic search introduction
- Stochastic search example
- Hill climbing introduction
- Hill climbing example
META-HEURISTIC OPTIMIZATION METHODS
- Heuristics VS meta-heuristics
- Tabu search introduction
- 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 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 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
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
- Alpha-beta pruning example
- Chess problem
BUILDING TIC-TAC-TOE
- About the game
- Cell
- Constants and Player
- Game implementation I
- Game implementation II
- Board implementation I
- Board implementationj II - isWinning()
- Board implementation III
- Minimax algorithm
- Running tic-tac-toe
INTERVIEW: SINGAPOREAN EXPERT
- Background of Expert
- Information and Communication Technology in Singapore
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COURSE FEES
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INSTRUCTIONS FOR ENROLMENT