Major Topics Covered
Theoretical and practical issues in modern machine learning techniques. Minimal overlap with Computer Science 270.
- Foundations
- Probability
- Statistics
- Convex Optimization
- Regularization and Learning Theory
- Kernel Methods
- Types of Learning
- Supervised Learning
- Linear Classifiers
- Neural Networks
- Support Vector Machines
- Reinforcement Learning
- Decision Theory
- Markov Decision Processes
- Reinforcement Learning Algorithms
- Unsupervised Learning
- Clustering
- PCA
- Bayes net learning
- HMM learning