Artificial Intelligence

1. Decision Making

1.1. Markov Decision Processes

2. Statistical Models

2.1. Bayesian Network

2.2. Hidden Markov Model

2.3. Kalman Filtering

3. Search Method and optimization

3.1. Mathematical Formulations/Linear Programming

3.2. Breadth-first search

3.3. Tabu Search

3.4. Local Search Algm and metaheuristics

3.4.1. Genetic Algorithm

3.4.2. Swarm Intelligence

3.4.2.1. Ant Colony

3.4.2.2. Particle Swarm Optimization

4. Learning

4.1. Machine Learning

4.1.1. Supervised

4.1.1.1. Neural Network

4.1.1.2. Support Vector Machines

4.1.1.3. Random Forests

4.1.1.4. K-nearest neighbors

4.1.1.5. Linier Regression

4.1.2. Unsupervised

4.1.2.1. Principal Component Analysis

4.1.2.2. K-Means

4.1.3. Reinforcement

4.1.3.1. Q-Learning

4.1.3.2. Multi-Bandits

4.2. Learning Probabilistics

4.2.1. Bayesian Learning

4.2.2. Expectation maximization

5. Game Theory

6. Knowledge-based, reasoning and planning