Projects:
- Ridge Regression and Active Learning
- Bayes Classifier
- K Means and Gaussian Mixture Model
- Positive Matrix Factorization
Lectures:
- Lecture01: Overview and MLE
- Lecture02: Linear Regression and Least Squares
- Lecture03: Ridge Regression
- Lecture04: Bias-Variance and MAP
- Lecture05: Bayesian Linear Regression
- Lecture06: Sparse and LASSO
- Lecture07: KNN and Bayes Classifiers
- Lecture08: Linear Classifiers and Perceptron
- Lecture09: Logistic Regression and Laplace Approximation
- Lecture10: Kernel Methods and Gaussian Processes
- Lecture11: SVM
- Lecture12: Decision Trees
- Lecture13: Boosting
- Lecture14: K-means
- Lecture15: EM Algorithm
- Lecture16: Gaussian Mixtures
- Lecture17: Collaborative Filtering
- Lecture18: Topic Modeling and Non-negative Matrix Factorization
- Lecture19: PCA
- Lecture20: Markov Models, Ranking and Semi-supervised Classification
- Lecture21: Hidden Markov Models
- Lecture22: Kalman Filter
- Lecture23: Association Analysis
- Lecture24: Model Selection