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