This course is a graduate-level artificial intelligence course completely taught in English. This course provide a broad understanding of basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. The students will learn the theory, algorithms, and their applications.
This repo contains my solutions to the homework as well as the final project.
Professor: Jane Yung-jen Hsu, department of computer science and information @ National Taiwan University
- HW0: DFS practice
- HW1: implement DFS and BFS in pacman project
- HW2: implement reflex agent, minimax agent, minimax with alpha-beta pruning agent and expectimax agent in pacman project
- HW3: Object detection - implement non maximum surpression (NMS) algorithm
PART I - Introduction + Problem Solving and Search
- Chapter 1: Introduction to AI, history of AI
- Chapter 2: Intelligent agents
- Chapter 3: Uninformed search, heuristic search, A* algorithm
- Chapter 4: Beyond classical search
- Chapter 5: Adversarial search, games
- Chapter 6: Constraint Satisfaction Problems
PART II - Data-Driven AI
- Machine Learning: Basic concepts
- Chapter 18: Learning from examples
- Linear models: linear regression, perceptron, K-nearest neighbors
- Decision trees
- Statistical machine learning: Support Vector Machines
- Neural networks
PART III - Decision Making
- Chapter 7: Logical agents
- Chapter 13: Quantifying uncertainty
- Chapter 14: Bayesian networks
- Markov Decision Process
- Chapter 21: Reinforcement Learning
PART IV - Advanced Topics
- Natural Language Processing
- Computer Vision
- Robotics