12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam.
For details of standard course structure and assessments, visit
Academics
page.
WEEK 1
|
Introduction; Unsupervised Learning - Representation learning - PCA
|
WEEK 2
|
Unsupervised Learning - Representation learning - Kernel PCA
|
WEEK 3
|
Unsupervised Learning - Clustering - K-means/Kernel K-means
|
WEEK 4
|
Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.
|
WEEK 5
|
Supervised Learning - Regression - Least Squares; Bayesian view
|
WEEK 6
|
Supervised Learning - Regression - Ridge/LASSO
|
WEEK 7
|
Supervised Learning - Classification - K-NN, Decision tree
|
WEEK 8
|
Supervised Learning - Classification - Generative Models - Naive Bayes
|
WEEK 9
|
Discriminative Models - Perceptron; Logistic Regression
|
WEEK 10
|
Support Vector Machines
|
WEEK 11
|
Ensemble methods - Bagging and Boosting (Adaboost)
|
WEEK 12
|
Artificial Neural networks: Multiclass classification.
|