Machine Learning (PEC-CS701E)
6 units: Supervised Learning (Regression/Classification) Basic methods, Unsupervised Learning Clustering · L-T-P 3L/week.
Detailed syllabus
Unit 1: Supervised Learning (Regression/Classification) Basic methods (10 hours)
Supervised Learning (Regression/Classification) Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes Linear models: Linear Regression, Logistic Regression, Generalized Linear Models Support Vector Machines, Nonlinearity and Kernel Methods Beyond Binary Classification: Multi-class/Structured Outputs, Ranking
Unit 2: Unsupervised Learning Clustering (7 hours)
Unsupervised Learning Clustering: K-means/Kernel K-means Dimensionality Reduction: PCA and kernel PCA Matrix Factorization and Matrix Completion Generative Models (mixture models and latent factor models)
Unit 3 (6 hours)
Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)
Unit 4 (9 hours)
Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and Feature Representation Learning
Unit 5 (9 hours)
Scalable Machine Learning (Online and Distributed Learning) A selection from some other advanced topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in Graphical Models, Introduction to Bayesian Learning and Inference
Unit 6 (5 hours)
Recent trends in various learning techniques of machine learning and classification methods
Source: official document