Course Details

Subject {L-T-P / C} : EC6604 : Machine Intelligence { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Prof. Samit Ari

Syllabus

Introduction to Machine Learning: Types of machine learning, Supervised learning, Unsupervised learning, basic concepts in machine learning, K Nearest Neighbors. A brief review of probability theory: Bayes rule, Generative models for discrete data, Bayesian concept learning, Likelihood, Prior, Posterior. Linear Models of Regression: linear basis function models, Maximum likelihood estimation (least squares), Bayesian linear regression, parametric distribution, predictive distribution. Mixture Models and EM algorithm: k-means clustering, Mixtures of Gaussians, The EM algorithm, Basic idea, EM for GMMs, EM for mixture of experts. Kernels and SVM: Kernel functions, Optimal Hyperplane for linearly patterns, Optimal Hyperplane for non-separable patterns, SVMs for classification. Dimensionality Reduction: Subset Selection, Principal Component Analysis (PCA), linear discriminant analysis (LDA). Introduction to Deep learning: Introduction to Neural Networks, Deep generative models, Deep directed networks, Deep belief networks, Deep neural networks, Deep auto-encoders, Applications of deep networks.

Course Objectives

Course Outcomes

1. Understand the fundamental concepts of machine learning including supervised and unsupervised learning. <br />2. Learn the K Nearest Neighbors algorithm and Bayesian rule for probability theory. <br />3. Study linear regression models, mixture models, and the EM algorithm. <br />4. Acquire knowledge of kernel functions, SVM, and dimensionality reduction techniques. <br />5. Gain an understanding of deep learning, including deep neural networks and their applications.

Essential Reading

Supplementary Reading