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Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English		Video and Text Based

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Fees Informations	Certificate Availability	Certificate Providing Authority
INR 1000	yes	IIT Kanpur

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Applied Optimization for Wireless, Machine Learning, Big Data

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The Syllabus

Week 1 : Introduction to properties of Vectors, Norms, Positive Semi-Definite matrices, Gaussian Random Vectors

Week 1 : Introduction to properties of Vectors, Norms, Positive Semi-Definite matrices, Gaussian Random Vectors

Week 2 : Introduction to Convex Optimization – Convex sets, Hyperplanes/ Half-spaces etc. Application: Power constraints in Wireless Systems

Week 2 : Introduction to Convex Optimization – Convex sets, Hyperplanes/ Half-spaces etc. Application: Power constraints in Wireless Systems

Week 3 : Convex/ Concave Functions, Examples, Conditions for Convexity. Application: Beamforming in Wireless Systems, Multi-User Wireless, Cognitive Radio Systems

Week 3 : Convex/ Concave Functions, Examples, Conditions for Convexity. Application: Beamforming in Wireless Systems, Multi-User Wireless, Cognitive Radio Systems

Week 4 : Convex Optimization problems, Linear Program, Application: Power allocation in Multi-cell cooperative OFDM

Week 4 : Convex Optimization problems, Linear Program, Application: Power allocation in Multi-cell cooperative OFDM

Week 5 : QCQP, SOCP Problems, Application: Channel shortening for Wireless Equalization, Robust Beamforming in Wireless Systems

Week 5 : QCQP, SOCP Problems, Application: Channel shortening for Wireless Equalization, Robust Beamforming in Wireless Systems

Week 6 : Duality Principle and KKT Framework for Optimization. Application: Water-filling power allocation,Optimization for MIMO Systems, OFDM Systems and MIMO-OFDM systems

Week 6 : Duality Principle and KKT Framework for Optimization. Application: Water-filling power allocation,Optimization for MIMO Systems, OFDM Systems and MIMO-OFDM systems

Week 7 : Optimization for signal estimation, LS, WLS, Regularization. Application: Wireless channel estimation, Image Reconstruction-Deblurring

Week 7 : Optimization for signal estimation, LS, WLS, Regularization. Application: Wireless channel estimation, Image Reconstruction-Deblurring

Week 8 : Application: Convex optimization for Machine Learning, Principal Component Analysis (PCA), Support Vector Machines

Week 8 : Application: Convex optimization for Machine Learning, Principal Component Analysis (PCA), Support Vector Machines

Week 9 : Application: Cooperative Communication, Optimal Power Allocation for cooperative Communication, Geometric Program

Week 9 : Application: Cooperative Communication, Optimal Power Allocation for cooperative Communication, Geometric Program

Week 10 : Application: Compressive Sensing, Sparse Signal Processing, OMP (Orthogonal Matching Pursuit), LASSO (Least Absolute Shrinkage and Selection Operator) for signal estimation

Week 10 : Application: Compressive Sensing, Sparse Signal Processing, OMP (Orthogonal Matching Pursuit), LASSO (Least Absolute Shrinkage and Selection Operator) for signal estimation

Week 11 : Application: Radar for target detection, Array Processing, MUSIC, MIMO-Radar Schemes for Enhanced Target Detection

Week 11 : Application: Radar for target detection, Array Processing, MUSIC, MIMO-Radar Schemes for Enhanced Target Detection

Week 12 : Application: Convex optimization for Big Data Analytics, Recommender systems, User Rating Prediction, Optimization for Finance

Week 12 : Application: Convex optimization for Big Data Analytics, Recommender systems, User Rating Prediction, Optimization for Finance

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