The following is a tentative list (a superset, actually) of the topics to be covered in the class. Note: This list will change as we get closer to the start of the semester and also as the semester progresses. For a listing of lectures and schedules, visit the lectures page.
- Supervised machine learning
- Decision trees
- Overfitting
- Boosting and ensemble learning: Adaboost, Random Forest
- Linear regression: least mean square (LMS) approaches
- Computational Learning Theory: PAC learability, Agnostic Learning, generalization error bound, sample complexity
- Linear classifier
- Perceptron
- Support vector machines (SVM)
- Cross-Validation
- Nonlinear classifier
- Kernel tricks
- kernel Perceptron
- kernel SVM: SVM Primal and Dual, Support Vectors
- Artifical Neural Networks: Feed-Forward Pass, Back-Propagation
- Probabilistic learning
- Naive Bayes classifier
- Logistic regression
- Risk Minimization: Empirical Risk Minimization and Regularized Risk Minimization
- Unsupervised learning
- K-means clustering
- Gaussian mixture models
- Practical guidance
- Empirical suggestions
- Tensorflow/PyTorch tutorials