Applied Optimization for Wireless, Machine Learning, Big-Data - Prof. Aditya K. Jagannatham
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This course includes
- 34.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 80 lessons • 34.5 hours of video
Applied Optimization for Wireless, Machine Learning, Big-Data - Prof. Aditya K. Jagannatham
80 lessons
• 34.5 hours
Applied Optimization for Wireless, Machine Learning, Big-Data - Prof. Aditya K. Jagannatham
80 lessons
• 34.5 hours
- Introduction - Applied Optimization for Wireless- Prof Aditya Jagannatham 08:37
- Lec 01 | Applied Optimization | Properties of Vectors and Matrices | IIT Kanpur 32:04
- Lec 02 | Applied Optimization | Eigenvectors and Eigenvalues | IIT Kanpur 27:19
- Lec 03 | Applied Optimization | Positive Semidefinite (PSD) Matrices | IIT Kanpur 40:27
- Lec 04 | Applied Optimization | Inner Product Space and its Properties-I | IIT Kanpur 40:02
- Lec 05 | Applied Optimization | Inner Product Space and its Properties -II | IIT Kanpur 24:12
- Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur 27:32
- Lec 07 | Applied Optimization | Gram Schmidt Orthogonalization | IIT Kanpur 30:35
- Lec 08 | Applied Optimization | Null Space, Trace of a Matrix | IIT Kanpur 30:41
- Lec 09 | Applied Optimization | Eigenvalue Decomposition (EVD) | IIT Kanpur 29:18
- Lec 10 | Applied Optimization | Matrix Inversion Lemma(Woodbury identity) | IIT Kanpur 13:45
- Lec 11 | Applied Optimization | Convex Sets and its Properties | IIT Kanpur 20:15
- Lec 12 | Applied Optimization | Examples of Affine set | IIT Kanpur 27:51
- Lec 13 | Applied Optimization | Norm Ball and its Application | IIT Kanpur 27:00
- Lec 14 | Applied Optimization | Ellipsoid and its Application | IIT Kanpur 28:36
- Lec 15 | Applied Optimization | Norm Cone, Polyhedron and its Application | IIT Kanpur 28:06
- Lec 16 | Applied Optimization | Cooperative Cellular Transmission | IIT Kanpur 17:24
- Lec 17 | Applied Optimization | Positive semidefinite (PSD) Cone | IIT Kanpur 30:26
- Lec 18 | Applied Optimization | Affine functions and , l2 , lp , l1 norm balls | IIT Kanpur 22:28
- Lec 19 | Applied Optimization | l∞, l0 norm balls and Matrix propertie | IIT Kanpur 28:25
- Lec 20 | Applied Optimization | Example problems - I | IIT Kanpur 28:23
- Lec 21 | Applied Optimization | Example problems - II | IIT Kanpur 24:12
- Lec 22 | Applied Optimization | Example problems - III | IIT Kanpur 31:21
- Lec 23 | Applied Optimization | Convex and Concave Functions | IIT Kanpur 25:47
- Lec 24 | Applied Optimization | Convex Functions: Properties and examples | IIT Kanpur 26:40
- Lec 25 | Applied Optimization | Test for Convexity | IIT Kanpur 26:49
- Lec 26 | Applied Optimization | MIMO Receiver Design (LS problem) | IIT Kanpur 23:54
- Lec 27 | Applied Optimization | Jensen's Inequality and its Application-I | IIT Kanpur 24:54
- Lec 28 | Applied Optimization | Jensen's Inequality and its Application-II | IIT Kanpur 22:05
- Lec 29 | Applied Optimization | Operations that preserve Convexity | IIT Kanpur 24:18
- Lec 30 | Applied Optimization | Conjugate Function , Test for Convexity:Examples | IIT Kanpur 24:36
- Lec 31 | Applied Optimization | Operations preserving Convexity: Examples | IIT Kanpur 25:38
- Lec 32 | Applied Optimization | Test for Convexity, Quasi-Convexity: Examples | IIT Kanpur 26:05
- Lec 33 | Applied Optimization | Examples on Convex functions| IIT Kanpur 34:48
- Lec 34 | Applied Optimization | Beamforming in Multi-antenna Wireless Communication | IIT Kanpur 24:26
- Lec 35 | Applied Optimization | Maximal Ratio Combiner for Wireless Systems | IIT Kanpur 23:10
- Lec 36 | Applied Optimization | Multi-antenna Beamforming with Interfering User | IIT Kanpur 25:16
- Lec 37 | Applied Optimization | Zero-Forcing (ZF) Beamforming with Interfering User | IIT Kanpur 25:46
- noc18-ee31-Lecture 38-Practical Application 23:10
- noc18-ee31-Lecture 39-Practical Application 18:21
- noc18-ee31-Lecture 40- Practical Application 26:58
- noc18-ee31-Lec 41 | Applied Optimization | Least Squares problem | IIT Kanpur 19:48
- noc18-ee31-Lec 42 | Applied Optimization | Geometric Intuition forLeast Squares | IIT Kanpur 24:15
- noc18-ee31-Lec 43 | Applied Optimization | Multi Antenna Channel Estimation | IIT Kanpur 15:13
- noc18-ee31-Lec 44 | Applied Optimization | Image Deblurring | IIT Kanpur 15:42
- noc18-ee31-Lec 45 | Applied Optimization | Least Norm Signal Estimation | IIT Kanpur 17:09
- noc18-ee31-Lec 46 | Applied Optimization | Regularization | IIT Kanpur 17:37
- noc18-ee31-Lec 47 | Applied Optimization | Convex Optimization Problem: Representations | IIT Kanpur 24:58
- noc18-ee31-Lec 48 | Applied Optimization | Linear Program and its Application | IIT Kanpur 32:14
- noc18-ee31-Lec 49 - Applied Optimization | Stochastic Linear Program, Gaussian Uncertainty 30:56
- noc18-ee31-Lec 50 -Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -I 16:58
- noc18-ee31-Lec 51- Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -II 23:33
- noc18-ee31-Lec 52 -Applied Optimization | Co-operative Communication -I 27:32
- noc18-ee31-Lec 53 -Applied Optimization | Co-operative Communication -II 29:16
- noc18-ee31-Lec 54 -Applied Optimization | Co-operative Communication -III 21:31
- noc18-ee31-Lec 55 -Applied Optimization | Compressive Sensing -I 26:13
- noc18-ee31-Lec 56 | Applied Optimization | Compressive Sensing -II 33:21
- noc18-ee31-Lec 57 | Applied Optimization | Orthogonal Matching Pursuit (OMP) algorithm 23:08
- noc18-ee31-Lec 58 | Applied Optimization | Example problem on OMP algorithm 29:19
- noc18-ee31-Lec 59 | Applied Optimization | Compressive Sensing via L1 norm minimization 26:39
- noc18-ee31-Lec 60 | Applied Optimization | Linear Classification Problem-I 30:57
- noc18-ee31-Lec 61 | Applied Optimization | Linear Classification Problem-II 27:48
- noc18-ee31 Lecture 62-Practical Application: Approximate Classifier Design 24:34
- noc18-ee31 Lecture 63-Concept of Duality 19:25
- noc18-ee31 Lecture 64-Relation between optimal value of Primal & Dual Problems 29:30
- noc18-ee31 Lecture 65-Example problem on Strong Duality 29:14
- noc18-ee31 Lecture 66-Karush-Kuhn-Tucker(KKT) condition 26:52
- noc18-ee31 Lecture 67-Application of KKT condition:Optimal MIMO power allocation(Waterfilling) 34:41
- noc18-ee31 lec 68-Optimal MIMO Power allocation(Waterfilling)-II 31:35
- noc18-ee31 lec 69-Example problem on Optimal MIMO Power allocation(Waterfilling)) 25:58
- noc18-ee31 lec 70-Examples : Linear objective with box constraints, Linear Programming 25:09
- noc18-ee31 lec 71-Examples:/1 minimization with /x norm constraints , Network Flow problem 25:10
- noc18-ee31 lec 72-Examples on Quadratic Optimization 32:54
- noc18-ee31 lec 73-Examples on Duality: Dual Norm, Dual of Linear Program(LP) 27:29
- noc18-ee31 Lecture 74-Examples on Duality: Min-Max problem, Analytic Centering 29:39
- noc18-ee31 Lecture 75-semi Definite Program(SDP) and its application:MIMO symbol vector decoding 25:59
- noc18-ee31 Lecture 76-Application:SDP for MIMO Maximum Likelihood(ML) Detection 26:02
- noc18-ee31 Lecture 77-Introduction to big Data: Online Recommender System(Netflix) 22:54
- noc18-ee31 Lecture 78-matrix Completion Problem in Big Data: Netflix-I 24:36
- noc18-ee31 Lecture 79-Matrix Completion Problem in Big Data: Netflix-II 28:20
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