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Indian Institute of Technology Kanpur

Applied Optimization for Wireless, Machine Learning, Big Data

Indian Institute of Technology Kanpur and NPTEL via Swayam


This course is focused on developing the fundamental tools/ techniques in modern optimization as well as illustrating their applications in diverse fields such as Wireless Communication, Signal Processing, Machine Learning, Big-Data and Finance. Various topics will be covered in different areas such as; Wireless: MIMO/ OFDM systems, Beamforming, Cognitive Radio and Cooperative Communication; Signal Processing: Signal Estimation, Regularization, Image Reconstruction; Compressive Sensing: Sparse estimation, OMP, LASSO techniques; Machine Learning: Principal Component Analysis (PCA), Support Vector Machines (SVM); Big-Data: Recommender systems, User-rating prediction, Latent Factor Method; Finance: Financial models, Portfolio Optimization.The course is suitable for all UG/PG students and practicing engineers/ scientists/ managers from the diverse fields mentioned above and interested in learning about the novel cutting edge applications of modern optimization technology.

INTENDED AUDIENCE : -Students in Electrical Engineering, Electronics and Communication Engineering, Mathematics, Economics, Computer Science -Practicing engineers -Technical and Non-technical managers of Telecomm companies -Students preparing for Competitive Exams with focus on Wireless Communication, Signal Processing - Students pursuing projects or research in Optimization and Wireless Communication
PREREQUISITES : Basic knowledge of Calculus, Probability, Matrices
INDUSTRY SUPPORT : Most companies in Electronics, Communication and Signal Processing. Examples are Qualcomm, Broadcom, Intel, MediaTek, Samsung etc. Companies in Machine Learning, AI, Big-Data and Finance will also find the content useful



Week 1 : Introduction to properties of Vectors, Norms, Positive Semi-Definite matrices, Gaussian Random VectorsWeek 2 : Introduction to Convex Optimization – Convex sets, Hyperplanes/ Half-spaces etc. Application: Power constraints in Wireless SystemsWeek 3 : Convex/ Concave Functions, Examples, Conditions for Convexity. Application: Beamforming in Wireless Systems, Multi-User Wireless, Cognitive Radio SystemsWeek 4 : Convex Optimization problems, Linear Program, Application: Power allocation in Multi-cell cooperative OFDMWeek 5 : QCQP, SOCP Problems, Application: Channel shortening for Wireless Equalization, Robust Beamforming in Wireless SystemsWeek 6 : Duality Principle and KKT Framework for Optimization. Application: Water-filling power allocation,Optimization for MIMO Systems, OFDM Systems and MIMO-OFDM systemsWeek 7 : Optimization for signal estimation, LS, WLS, Regularization. Application: Wireless channel estimation, Image Reconstruction-DeblurringWeek 8 : Application: Convex optimization for Machine Learning, Principal Component Analysis (PCA), Support Vector MachinesWeek 9 : Application: Cooperative Communication, Optimal Power Allocation for cooperative Communication, Geometric ProgramWeek 10 : Application: Compressive Sensing, Sparse Signal Processing, OMP (Orthogonal Matching Pursuit), LASSO (Least Absolute Shrinkage and Selection Operator) for signal estimationWeek 11 : Application: Radar for target detection, Array Processing, MUSIC, MIMO-Radar Schemes for Enhanced Target DetectionWeek 12 : Application: Convex optimization for Big Data Analytics, Recommender systems, User Rating Prediction, Optimization for Finance

Taught by

Prof. Aditya K. Jagannatham

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