Lecture 1: Introduction to ML, Linear Algebra Review
Lecture 2: Introduction to Regression
Lecture 3: Linear Regression: Convexity, Closed-form Solution, Gradient Descent
Lecture 4: Robust Regression, Overfitting, Regularization
Lecture 5: Basis Function Expansion, Hyper-parameter Tuning, Cross Validation, Probability Review
Lecture 6: Maximum Likelihood Estimation
Lecture 7: Bayesian Learning, Maximum A Posteriori (MAP) Estimation, Classification
- Chapter 3 and 4.3 from CB book.
Lecture 8: Logistic Regression, Parameter Learning via Maximum Likelihood, Overfitting
- Chapter 4.3 from CB book.
Lecture 9: Softmax Regression, Discriminate vs Generative Modeling, Generative Classification
- Chapter 4.2 from CB book.
Lecture 10: Generative Classification, Naive Bayes
- Chapter 4.2 from CB book.
Lecture 11: Generative Classification, Naive Bayes
- Chapter 4.2 from CB book.
Lecture 12: Convex Optimization, Lagrangian Function, KKT Conditions
- See lecture notes on piazza.
Lecture 13: Suport Vector Machines
Lecture 14: Suport Vector Machines: Vanilla SVM, Dual SVM
Lecture 15: Suport Vector Machines: Soft-Margin SVM, Kernel SVM, Multi-Class SVM
Lecture 16: Neural Networks
Lecture 17: Neural Networks: Training, Forward and Back Propagation
Lecture 18: Convolutional Neural Network