Applied Optimization for Wireless, Machine Learning, Big-Data

Introduction - Applied Optimization for Wireless- Prof Aditya Jagannatham.
Lec 01 | Applied Optimization | Properties of Vectors and Matrices | IIT Kanpur.
Lec 02 | Applied Optimization | Eigenvectors and Eigenvalues | IIT Kanpur.
Lec 03 | Applied Optimization | Positive Semidefinite (PSD) Matrices | IIT Kanpur.
Lec 04 | Applied Optimization | Inner Product Space and its Properties-I | IIT Kanpur.
Lec 05 | Applied Optimization | Inner Product Space and its Properties -II | IIT Kanpur.
Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur.
Lec 07 | Applied Optimization | Gram Schmidt Orthogonalization | IIT Kanpur.
Lec 08 | Applied Optimization | Null Space, Trace of a Matrix | IIT Kanpur.
Lec 09 | Applied Optimization | Eigenvalue Decomposition (EVD) | IIT Kanpur.
Lec 10 | Applied Optimization | Matrix Inversion Lemma(Woodbury identity) | IIT Kanpur.
Lec 11 | Applied Optimization | Convex Sets and its Properties | IIT Kanpur.
Lec 12 | Applied Optimization | Examples of Affine set | IIT Kanpur.
Lec 13 | Applied Optimization | Norm Ball and its Application | IIT Kanpur.
Lec 14 | Applied Optimization | Ellipsoid and its Application | IIT Kanpur.
Lec 15 | Applied Optimization | Norm Cone, Polyhedron and its Application | IIT Kanpur.
Lec 16 | Applied Optimization | Cooperative Cellular Transmission | IIT Kanpur.
Lec 17 | Applied Optimization | Positive semidefinite (PSD) Cone | IIT Kanpur.
Lec 18 | Applied Optimization | Affine functions and , l2 , lp , l1 norm balls | IIT Kanpur.
Lec 19 | Applied Optimization | l∞, l0 norm balls and Matrix propertie | IIT Kanpur.
Lec 20 | Applied Optimization | Example problems - I | IIT Kanpur.
Lec 21 | Applied Optimization | Example problems - II | IIT Kanpur.
Lec 22 | Applied Optimization | Example problems - III | IIT Kanpur.
Lec 23 | Applied Optimization | Convex and Concave Functions | IIT Kanpur.
Lec 24 | Applied Optimization | Convex Functions: Properties and examples | IIT Kanpur.
Lec 25 | Applied Optimization | Test for Convexity | IIT Kanpur.
Lec 26 | Applied Optimization | MIMO Receiver Design (LS problem) | IIT Kanpur.
Lec 27 | Applied Optimization | Jensen's Inequality and its Application-I | IIT Kanpur.
Lec 28 | Applied Optimization | Jensen's Inequality and its Application-II | IIT Kanpur.
Lec 29 | Applied Optimization | Operations that preserve Convexity | IIT Kanpur.
Lec 30 | Applied Optimization | Conjugate Function , Test for Convexity:Examples | IIT Kanpur.
Lec 31 | Applied Optimization | Operations preserving Convexity: Examples | IIT Kanpur.
Lec 32 | Applied Optimization | Test for Convexity, Quasi-Convexity: Examples | IIT Kanpur.
Lec 33 | Applied Optimization | Examples on Convex functions| IIT Kanpur.
Lec 34 | Applied Optimization | Beamforming in Multi-antenna Wireless Communication | IIT Kanpur.
Lec 35 | Applied Optimization | Maximal Ratio Combiner for Wireless Systems | IIT Kanpur.
Lec 36 | Applied Optimization | Multi-antenna Beamforming with Interfering User | IIT Kanpur.
Lec 37 | Applied Optimization | Zero-Forcing (ZF) Beamforming with Interfering User | IIT Kanpur.
noc18-ee31-Lecture 38-Practical Application.
noc18-ee31-Lecture 39-Practical Application.
noc18-ee31-Lecture 40- Practical Application.
noc18-ee31-Lec 41 | Applied Optimization | Least Squares problem | IIT Kanpur.
noc18-ee31-Lec 42 | Applied Optimization | Geometric Intuition forLeast Squares | IIT Kanpur.
noc18-ee31-Lec 43 | Applied Optimization | Multi Antenna Channel Estimation | IIT Kanpur.
noc18-ee31-Lec 44 | Applied Optimization | Image Deblurring | IIT Kanpur.
noc18-ee31-Lec 45 | Applied Optimization | Least Norm Signal Estimation | IIT Kanpur.
noc18-ee31-Lec 46 | Applied Optimization | Regularization | IIT Kanpur.
noc18-ee31-Lec 47 | Applied Optimization | Convex Optimization Problem: Representations | IIT Kanpur.
noc18-ee31-Lec 49 - Applied Optimization | Stochastic Linear Program, Gaussian Uncertainty.
noc18-ee31-Lec 48 | Applied Optimization | Linear Program and its Application | IIT Kanpur.
noc18-ee31-Lec 50 -Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -I.
noc18-ee31-Lec 51- Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -II.
noc18-ee31-Lec 52 -Applied Optimization | Co-operative Communication -I.
noc18-ee31-Lec 53 -Applied Optimization | Co-operative Communication -II.
noc18-ee31-Lec 54 -Applied Optimization | Co-operative Communication -III.
noc18-ee31-Lec 55 -Applied Optimization | Compressive Sensing -I.
noc18-ee31-Lec 56 | Applied Optimization | Compressive Sensing -II.
noc18-ee31-Lec 57 | Applied Optimization | Orthogonal Matching Pursuit (OMP) algorithm.
noc18-ee31-Lec 58 | Applied Optimization | Example problem on OMP algorithm.
noc18-ee31-Lec 59 | Applied Optimization | Compressive Sensing via L1 norm minimization.
noc18-ee31-Lec 60 | Applied Optimization | Linear Classification Problem-I.
noc18-ee31-Lec 61 | Applied Optimization | Linear Classification Problem-II.
noc18-ee31 Lecture 62-Practical Application: Approximate Classifier Design.
noc18-ee31 Lecture 63-Concept of Duality.
noc18-ee31 Lecture 64-Relation between optimal value of Primal & Dual Problems.
noc18-ee31 Lecture 65-Example problem on Strong Duality.
noc18-ee31 Lecture 66-Karush-Kuhn-Tucker(KKT) condition.
noc18-ee31 Lecture 67-Application of KKT condition:Optimal MIMO power allocation(Waterfilling).
noc18-ee31 lec 68-Optimal MIMO Power allocation(Waterfilling)-II.
noc18-ee31 lec 69-Example problem on Optimal MIMO Power allocation(Waterfilling)).
noc18-ee31 lec 70-Examples : Linear objective with box constraints, Linear Programming.
noc18-ee31 lec 71-Examples:/1 minimization with /x norm constraints , Network Flow problem.
noc18-ee31 lec 72-Examples on Quadratic Optimization.
noc18-ee31 lec 73-Examples on Duality: Dual Norm, Dual of Linear Program(LP).
noc18-ee31 Lecture 74-Examples on Duality: Min-Max problem, Analytic Centering.
noc18-ee31 Lecture 75-semi Definite Program(SDP) and its application:MIMO symbol vector decoding.
noc18-ee31 Lecture 76-Application:SDP for MIMO Maximum Likelihood(ML) Detection.
noc18-ee31 Lecture 77-Introduction to big Data: Online Recommender System(Netflix).
noc18-ee31 Lecture 78-matrix Completion Problem in Big Data: Netflix-I.noc18-ee31 Lecture 79-Matrix Completion Problem in Big Data: Netflix-II.