Prerequisite : None
Taught by : Sudeshna Sarkar, Mahesh Mohan, Somdyuti Paul
Teaching Assistents :
Trishita Mukherjee, Rajkrishan Ghosh, Subhankar Maity, Deepayan Chakraborty, Ankit Katewa, B. Trisha, Shreyash Ranveerkar, Yashasvi Rathore
Supervised Learning: K-nearest Neighbor, Linear and Logistic Regression, Neural Networks, Naive Bayes, Decision Tree, Random Forest, Bagging, Adaboost, Perceptron and Support Vector Machine
Unsupervised Learning: K-means, agglomerative and hierarchical clustering, Spectral clustering, Gaussian Mixture Model
Other topics: semi-supervised learning, dimensionality reduction, Bayesian Network