Syllabus

Introduction and fundamentals of machine learning: Basics of supervised/unsupervised/reinforcement learning, Revision of probability and statistics revision, Revision of linear algebra, Fundamentals of numerical optimization, Machine learning for wireless communications, Machine learning for physical layer design Linear Modeling: A Least Squares Approach, Linear modeling Generalization and over fitting, Regularized least squares Wireless application - MIMO zero-forcing receiver design Linear Modeling: A Maximum Likelihood Approach, Errors as noise– thinking generatively, Maximum likelihood, Bias- variance trade-off, Effect of noise on parameter estimates, Wireless application - MIMO MMSE receiver design The Bayesian Approach to Machine Learning: Exact posterior, Marginal likelihood, Hyper parameters, Bayesian Inference: Non-conjugate models, Point estimate– MAP solution, Laplace approximation, wireless application - Massive MIMO channel estimation Classification: Probabilistic classifiers– Bayes classifier, logistic regression, Non Probabilistic classifiers-K-nearest neighbors, Discriminative and generative classifiers, Wireless application - Detection in digital communication systems sparse kernel machines, Support vector machines (SVM), Sparse Bayesian learning (SBL),Wireless application, SVM for beam forming and data detection in millimeter wave systems, SBL for channel estimation in massive MIMO Clustering: General Problem, K-means clustering, Gaussian mixture models (GMM), EM algorithm –MAP estimates, Bayesian mixture models, Wireless application - Clustering for massive MIMO system using K means and GMM Principal components analysis and latent variable models, General Problem, K-means clustering, Gaussian mixture models (GMM),EM algorithm – MAP estimates, Bayesian mixture models, Wireless application - Clustering for massive MIMO system using K means and GMM Principal components analysis and latent variable models, General Problem, Latent variable model Variational Bayes Probabilistic model for PCA, Wireless application - Variational Bayes for massive IoT device detection Gaussian Processes, Gaussian Processes for regression Gaussian Processes for classification Deep Learning for Wireless Communications Deep Learning Based MIMO Communications, Deep Reinforcement Learning for Dynamic Multi channel Accessing wireless networks, Deep Reinforcement Learning Auto encoder with Noisy Feedback.