CS229 Machine Learning
Week 1
Lecture 1: Introduction and Basic Concepts
- Introduction and Basic Concepts
Lecture 2: Supervised Learning Setup. Linear Regression
- Supervised Learning
- Linear Regression
- Discriminative Algorithms
Week 2
Lecture 3: Logistic Regression
- Weighted Least Squares
- Logistic Regression
Lecture 4: Netwon’s Method Perceptron
- Netwon’s Method Perceptron
- Exponential Family
- Generalized Linear Models
- Generative Algorithms
Week 3
Lecture 5: Gaussian Discriminant Analysis
- Gaussian Discriminant Analysis
- Naive Bayes.
Lecture 6: Laplace Smoothing
- Laplace Smoothing
- Support Vector Machines.
Week 4
Lecture 7: Support vector machines
- Support Vector Machines
- Kernels.
Lecture 8: Bias-Variance tradeoff
- Bias-Variance tradeoff
- Regularization and model/feature selection
Week 5
Lecture 9: Tree Ensembles
- Decision trees
- Ensembling methods
Lecture 10: Neural Networks: Basics
- Deep learning
- Backpropagation
Week 6
Lecture 11: Neural Networks: Training
- Neural Networks: Training
Lecture 12: Practical Advice for ML projects
- Practical Advice for ML projects
Week 7
Lecture 13: Neural Networks: Training
- K-means
- Mixture of Gaussians
- Expectation Maximization
Lecture 14: Factor Analysis.
- Factor Analysis
Week 8
Lecture 15: Principal Component Analysis
- Principal Component Analysis
- Independent Component Analysis
Lecture 16: MDPs. Bellman Equations.
- MDPs. Bellman Equations.
Week 9
Lecture 17: Value Iteration and Policy Iteration. LQR. LQG.
- Value Iteration and Policy Iteration
- LQR
- LQG
Lecture 18: Q-Learning. Value function approximation.
- Q-Learning
- Value function approximation.
Week 10
Lecture 19: Policy Search. REINFORCE. POMDPs.
- Policy Search
- REINFORCE
- POMDPs
Lecture 20: Optional topic. Wrap-up.
- Optional topic. Wrap-up