| Module Name | Download |
|---|---|
| noc18_ee31_Assignment1 | noc18_ee31_Assignment1 |
| noc18_ee31_Assignment10 | noc18_ee31_Assignment10 |
| noc18_ee31_Assignment11 | noc18_ee31_Assignment11 |
| noc18_ee31_Assignment12 | noc18_ee31_Assignment12 |
| noc18_ee31_Assignment13 | noc18_ee31_Assignment13 |
| noc18_ee31_Assignment2 | noc18_ee31_Assignment2 |
| noc18_ee31_Assignment3 | noc18_ee31_Assignment3 |
| noc18_ee31_Assignment4 | noc18_ee31_Assignment4 |
| noc18_ee31_Assignment5 | noc18_ee31_Assignment5 |
| noc18_ee31_Assignment6 | noc18_ee31_Assignment6 |
| noc18_ee31_Assignment7 | noc18_ee31_Assignment7 |
| noc18_ee31_Assignment8 | noc18_ee31_Assignment8 |
| noc18_ee31_Assignment9 | noc18_ee31_Assignment9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lec 01- Vectors and Matrices- Linear Independence and Rank | Download |
| 2 | Lec 02 - Eigenvectors and Eigenvalues of Matrices and their Properties | Download |
| 3 | Lec 03 - Positive Semidefinite (PSD) and Postive Definite (PD) Matrices and their Properties | Download |
| 4 | Lec 04 - Inner Product Space and it's Properties: Linearity, Symmetry and Positive Semi-definite | Download |
| 5 | Lec 05 - Inner Product Space and its Properties: Cauchy Schwarz Inequality | Download |
| 6 | Lec 06 - Properties of Norm, Gaussian Elimination and Echleon form of matrix | Download |
| 7 | Lec 07- Gram Schmidt Orthogonalization Procedure | Download |
| 8 | Lec 08- Null Space and Trace of Matrices | Download |
| 9 | Lec 09- Eigenvalue Decomposition of Hermitian Matrices and Properties | Download |
| 10 | Lec 10- Matrix Inversion Lemma (Woodbury identity) | Download |
| 11 | Lec 11- Introduction to Convex Sets and Properties | Download |
| 12 | Lec 12- Affine Set Examples and Application | Download |
| 13 | Lec 13- Norm Ball and its Practical Applications | Download |
| 14 | Lec 14- Ellipsoid and its Practical Applications | Download |
| 15 | Lec 15- Norm Cone,Polyhedron and its Applications | Download |
| 16 | Lec 16- Applications: Cooperative Cellular Transmission | Download |
| 17 | Lec 17- Positive Semi Definite Cone And Positive Semi Definite (PSD) Matrices | Download |
| 18 | Lec 18-Introduction to Affine functions and examples | Download |
| 19 | Lecture 19-norm balls and Matrix properties:Trace,Determinant | Download |
| 20 | Lecture 20-Inverse of a Positive Definite Matrix | Download |
| 21 | Lecture 21-Example Problems: Property of Norms,Problems on Convex Sets | Download |
| 22 | Lecture 22-Problems on Convex Sets(contd.) | Download |
| 23 | Lecture 23-Introduction to Convex and Concave Functions | Download |
| 24 | Lecture 24-Properties of Convex Functions with examples | Download |
| 25 | Lec 25-Test for Convexity: Positive Semidefinite Hessian Matrix | Download |
| 26 | Lec 26-Application: MIMO Receiver Design as a Least Squares Problem | Download |
| 27 | Lec 27-Jensen's Inequality and Practical Application | Download |
| 28 | Lec 28-Jensen's Inequality application | Download |
| 29 | Lec 29 - Properties of Convex Functions | Download |
| 30 | Lec 30 - Conjugate Function and Examples to prove Convexity of various Functions | Download |
| 31 | Lec 31- Example problems: Operations preserving Convexity(log-sum, average) and Quasi Convexity | Download |
| 32 | Lec 32-Example Problems: Verify Convexity, Quasi -Convexity and Quasi- Concavity of functions | Download |
| 33 | Lec 33-Example Problems:Perspective function, Product of Convex functions and Pointwise Maximum is Convex | Download |
| 34 | Lec 34- Practical Application: Beamforming in Multi-antenna Wireless Communication | Download |
| 35 | Lec 35 - Practical Application: Maximal Ratio Combiner for Wireless Systems | Download |
| 36 | Lec 36- Practical Application: Multi-antenna Beamforming with Interfering User | Download |
| 37 | Lec 37- Practical Application: Zero-Forcing (ZF) Beamforming with Interfering User | Download |
| 38 | Lec 38- Practical Application: Robust Beamforming With Channel Uncertainity for Wireless Systems | Download |
| 39 | Lec 39- Practical Application: Robust Beamformer Design for Wireless Systems | Download |
| 40 | Lec 40 - Practical Application: Detailed Solution for Robust Beamformer Computation in Wireless Systems Text | Download |
| 41 | Lec 41- Linear modeling and Approximation Problems: Least Squares | Download |
| 42 | Lec 42-Geometric Intuition for Least Squares | Download |
| 43 | Lec 43- Practical Application: Multi antenna channel estimation | Download |
| 44 | Lec 44- Practical Application:Image deblurring | Download |
| 45 | Lec 45- Least Norm Signal Estimation | Download |
| 46 | Lec 46- Regularization: Least Squares + Least Norm | Download |
| 47 | Lec 47- Convex Optimization Problem representation: Canonical form, Epigraph form | Download |
| 48 | Lec 48-Linear Program Practical Application: Base Station Co-operation | Download |
| 49 | Lec 49- Stochastic Linear Program,Gaussian Uncertainty | Download |
| 50 | Lec 50- Practical Application: Multiple Input Multiple Output (MIMO) Beamforming | Download |
| 51 | Lec 51- Practical Application: Multiple Input Multiple Output (MIMO) Beamformer Design | Download |
| 52 | Lec 52-Practical Application: Co-operative Communication, Overview and various Protocols used | Download |
| 53 | Lec 53- Practical Application: Probability of Error Computation for Co-operative Communication | Download |
| 54 | Lec 54- Practical Application:Optimal power allocation factor determination for Co-operative Communication | Download |
| 55 | Lec 55- Practical Application: Compressive Sensing | Download |
| 56 | Lec 56- Practical Application | Download |
| 57 | Lec 57- Practical Application- Orthogonal Matching Pursuit (OMP) algorithm for Compressive Sensing | Download |
| 58 | Lec 58- Example Problem: Orthogonal Matching Pursuit (OMP) algorithm | Download |
| 59 | Lec 59- Practical Application : L1 norm minimization and regularization approach for Compressive Sensing Optimization problem | Download |
| 60 | Lec 60- Practical Application of Machine Learning and Artificial Intelligence:Linear Classification, Overview and Motivation | Download |
| 61 | Lec 61- Practical Application: Linear Classifier (Support Vector Machine) Design | Download |
| 62 | Lec 62- Practical Application: Approximate Classifier Design | Download |
| 63 | Lec 63- Concept of Duality | Download |
| 64 | Lec 64-Relation between optimal value of Primal & Dual Problems, concepts of Duality gap and Strong Duality | Download |
| 65 | Lec 65-Example problem on Strong Duality | Download |
| 66 | Lec 66- Karush-Kuhn-Tucker(KKT) conditions | Download |
| 67 | Lec 67- Application of KKT condition:Optimal MIMO power allocation(Waterfilling) | Download |
| 68 | Lec 68- Optimal MIMO Power allocation(Waterfilling)-II | Download |
| 69 | Lec 69- Example problem on Optimal MIMO Power allocation(Waterfilling) | Download |
| 70 | Lec 70- Linear objective with box constraints, Linear Programming | Download |
| 71 | Lec 71- Example Problems II | Download |
| 72 | Lec 72- Examples on Quadratic Optimization | Download |
| 73 | Lec 73- Examples on Duality: Dual Norm, Dual of Linear Program(LP) | Download |
| 74 | Lec 74- Examples on Duality: Min-Max problem, Analytic Centering | Download |
| 75 | Lec 75- Semi Definite Program(SDP) and its application:MIMO symbol vector decoding | Download |
| 76 | Lec 76- Application:SDP for MIMO Maximum Likelihood(ML) Detection | Download |
| 77 | Lec 77- Introduction to big Data: Online Recommender System(Netflix) | Download |
| 78 | Lec 78- Matrix Completion Problem in Big Data: Netflix-I | Download |
| 79 | Lec 79- Matrix Completion Problem in Big Data: Netflix-II | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lec 01- Vectors and Matrices- Linear Independence and Rank | Download Verified |
| 2 | Lec 02 - Eigenvectors and Eigenvalues of Matrices and their Properties | Download Verified |
| 3 | Lec 03 - Positive Semidefinite (PSD) and Postive Definite (PD) Matrices and their Properties | Download Verified |
| 4 | Lec 04 - Inner Product Space and it's Properties: Linearity, Symmetry and Positive Semi-definite | Download Verified |
| 5 | Lec 05 - Inner Product Space and its Properties: Cauchy Schwarz Inequality | Download Verified |
| 6 | Lec 06 - Properties of Norm, Gaussian Elimination and Echleon form of matrix | Download Verified |
| 7 | Lec 07- Gram Schmidt Orthogonalization Procedure | Download Verified |
| 8 | Lec 08- Null Space and Trace of Matrices | Download Verified |
| 9 | Lec 09- Eigenvalue Decomposition of Hermitian Matrices and Properties | Download Verified |
| 10 | Lec 10- Matrix Inversion Lemma (Woodbury identity) | Download Verified |
| 11 | Lec 11- Introduction to Convex Sets and Properties | Download Verified |
| 12 | Lec 12- Affine Set Examples and Application | Download Verified |
| 13 | Lec 13- Norm Ball and its Practical Applications | Download Verified |
| 14 | Lec 14- Ellipsoid and its Practical Applications | Download Verified |
| 15 | Lec 15- Norm Cone,Polyhedron and its Applications | Download Verified |
| 16 | Lec 16- Applications: Cooperative Cellular Transmission | Download Verified |
| 17 | Lec 17- Positive Semi Definite Cone And Positive Semi Definite (PSD) Matrices | Download Verified |
| 18 | Lec 18-Introduction to Affine functions and examples | Download Verified |
| 19 | Lecture 19-norm balls and Matrix properties:Trace,Determinant | Download Verified |
| 20 | Lecture 20-Inverse of a Positive Definite Matrix | Download Verified |
| 21 | Lecture 21-Example Problems: Property of Norms,Problems on Convex Sets | Download Verified |
| 22 | Lecture 22-Problems on Convex Sets(contd.) | Download Verified |
| 23 | Lecture 23-Introduction to Convex and Concave Functions | Download Verified |
| 24 | Lecture 24-Properties of Convex Functions with examples | Download Verified |
| 25 | Lec 25-Test for Convexity: Positive Semidefinite Hessian Matrix | Download Verified |
| 26 | Lec 26-Application: MIMO Receiver Design as a Least Squares Problem | Download Verified |
| 27 | Lec 27-Jensen's Inequality and Practical Application | Download Verified |
| 28 | Lec 28-Jensen's Inequality application | Download Verified |
| 29 | Lec 29 - Properties of Convex Functions | Download Verified |
| 30 | Lec 30 - Conjugate Function and Examples to prove Convexity of various Functions | Download Verified |
| 31 | Lec 31- Example problems: Operations preserving Convexity(log-sum, average) and Quasi Convexity | Download Verified |
| 32 | Lec 32-Example Problems: Verify Convexity, Quasi -Convexity and Quasi- Concavity of functions | Download Verified |
| 33 | Lec 33-Example Problems:Perspective function, Product of Convex functions and Pointwise Maximum is Convex | Download Verified |
| 34 | Lec 34- Practical Application: Beamforming in Multi-antenna Wireless Communication | Download Verified |
| 35 | Lec 35 - Practical Application: Maximal Ratio Combiner for Wireless Systems | Download Verified |
| 36 | Lec 36- Practical Application: Multi-antenna Beamforming with Interfering User | Download Verified |
| 37 | Lec 37- Practical Application: Zero-Forcing (ZF) Beamforming with Interfering User | Download Verified |
| 38 | Lec 38- Practical Application: Robust Beamforming With Channel Uncertainity for Wireless Systems | Download Verified |
| 39 | Lec 39- Practical Application: Robust Beamformer Design for Wireless Systems | Download Verified |
| 40 | Lec 40 - Practical Application: Detailed Solution for Robust Beamformer Computation in Wireless Systems Text | Download Verified |
| 41 | Lec 41- Linear modeling and Approximation Problems: Least Squares | Download Verified |
| 42 | Lec 42-Geometric Intuition for Least Squares | Download Verified |
| 43 | Lec 43- Practical Application: Multi antenna channel estimation | Download Verified |
| 44 | Lec 44- Practical Application:Image deblurring | Download Verified |
| 45 | Lec 45- Least Norm Signal Estimation | Download Verified |
| 46 | Lec 46- Regularization: Least Squares + Least Norm | Download Verified |
| 47 | Lec 47- Convex Optimization Problem representation: Canonical form, Epigraph form | Download Verified |
| 48 | Lec 48-Linear Program Practical Application: Base Station Co-operation | Download Verified |
| 49 | Lec 49- Stochastic Linear Program,Gaussian Uncertainty | Download Verified |
| 50 | Lec 50- Practical Application: Multiple Input Multiple Output (MIMO) Beamforming | Download Verified |
| 51 | Lec 51- Practical Application: Multiple Input Multiple Output (MIMO) Beamformer Design | Download Verified |
| 52 | Lec 52-Practical Application: Co-operative Communication, Overview and various Protocols used | Download Verified |
| 53 | Lec 53- Practical Application: Probability of Error Computation for Co-operative Communication | Download Verified |
| 54 | Lec 54- Practical Application:Optimal power allocation factor determination for Co-operative Communication | Download Verified |
| 55 | Lec 55- Practical Application: Compressive Sensing | Download Verified |
| 56 | Lec 56- Practical Application | Download Verified |
| 57 | Lec 57- Practical Application- Orthogonal Matching Pursuit (OMP) algorithm for Compressive Sensing | Download Verified |
| 58 | Lec 58- Example Problem: Orthogonal Matching Pursuit (OMP) algorithm | Download Verified |
| 59 | Lec 59- Practical Application : L1 norm minimization and regularization approach for Compressive Sensing Optimization problem | Download Verified |
| 60 | Lec 60- Practical Application of Machine Learning and Artificial Intelligence:Linear Classification, Overview and Motivation | Download Verified |
| 61 | Lec 61- Practical Application: Linear Classifier (Support Vector Machine) Design | Download Verified |
| 62 | Lec 62- Practical Application: Approximate Classifier Design | Download Verified |
| 63 | Lec 63- Concept of Duality | Download Verified |
| 64 | Lec 64-Relation between optimal value of Primal & Dual Problems, concepts of Duality gap and Strong Duality | Download Verified |
| 65 | Lec 65-Example problem on Strong Duality | Download Verified |
| 66 | Lec 66- Karush-Kuhn-Tucker(KKT) conditions | Download Verified |
| 67 | Lec 67- Application of KKT condition:Optimal MIMO power allocation(Waterfilling) | Download Verified |
| 68 | Lec 68- Optimal MIMO Power allocation(Waterfilling)-II | Download Verified |
| 69 | Lec 69- Example problem on Optimal MIMO Power allocation(Waterfilling) | Download Verified |
| 70 | Lec 70- Linear objective with box constraints, Linear Programming | Download Verified |
| 71 | Lec 71- Example Problems II | Download Verified |
| 72 | Lec 72- Examples on Quadratic Optimization | Download Verified |
| 73 | Lec 73- Examples on Duality: Dual Norm, Dual of Linear Program(LP) | Download Verified |
| 74 | Lec 74- Examples on Duality: Min-Max problem, Analytic Centering | Download Verified |
| 75 | Lec 75- Semi Definite Program(SDP) and its application:MIMO symbol vector decoding | Download Verified |
| 76 | Lec 76- Application:SDP for MIMO Maximum Likelihood(ML) Detection | Download Verified |
| 77 | Lec 77- Introduction to big Data: Online Recommender System(Netflix) | Download Verified |
| 78 | Lec 78- Matrix Completion Problem in Big Data: Netflix-I | Download Verified |
| 79 | Lec 79- Matrix Completion Problem in Big Data: Netflix-II | Download Verified |
| Sl.No | Chapter Name | Gujarati |
|---|---|---|
| 1 | Lec 01- Vectors and Matrices- Linear Independence and Rank | Download |
| 2 | Lec 02 - Eigenvectors and Eigenvalues of Matrices and their Properties | Download |
| 3 | Lec 03 - Positive Semidefinite (PSD) and Postive Definite (PD) Matrices and their Properties | Download |
| 4 | Lec 04 - Inner Product Space and it's Properties: Linearity, Symmetry and Positive Semi-definite | Download |
| 5 | Lec 05 - Inner Product Space and its Properties: Cauchy Schwarz Inequality | Download |
| 6 | Lec 06 - Properties of Norm, Gaussian Elimination and Echleon form of matrix | Download |
| 7 | Lec 07- Gram Schmidt Orthogonalization Procedure | Download |
| 8 | Lec 08- Null Space and Trace of Matrices | Download |
| 9 | Lec 09- Eigenvalue Decomposition of Hermitian Matrices and Properties | Download |
| 10 | Lec 10- Matrix Inversion Lemma (Woodbury identity) | Download |
| 11 | Lec 11- Introduction to Convex Sets and Properties | Download |
| 12 | Lec 12- Affine Set Examples and Application | Download |
| 13 | Lec 13- Norm Ball and its Practical Applications | Download |
| 14 | Lec 14- Ellipsoid and its Practical Applications | Download |
| 15 | Lec 15- Norm Cone,Polyhedron and its Applications | Download |
| 16 | Lec 16- Applications: Cooperative Cellular Transmission | Download |
| 17 | Lec 17- Positive Semi Definite Cone And Positive Semi Definite (PSD) Matrices | Download |
| 18 | Lec 18-Introduction to Affine functions and examples | Download |
| 19 | Lecture 19-norm balls and Matrix properties:Trace,Determinant | Download |
| 20 | Lecture 20-Inverse of a Positive Definite Matrix | Download |
| 21 | Lecture 21-Example Problems: Property of Norms,Problems on Convex Sets | Download |
| 22 | Lecture 22-Problems on Convex Sets(contd.) | Download |
| 23 | Lecture 23-Introduction to Convex and Concave Functions | Download |
| 24 | Lecture 24-Properties of Convex Functions with examples | Download |
| 25 | Lec 25-Test for Convexity: Positive Semidefinite Hessian Matrix | Download |
| 26 | Lec 26-Application: MIMO Receiver Design as a Least Squares Problem | Download |
| 27 | Lec 27-Jensen's Inequality and Practical Application | Download |
| 28 | Lec 28-Jensen's Inequality application | Download |
| 29 | Lec 29 - Properties of Convex Functions | Download |
| 30 | Lec 30 - Conjugate Function and Examples to prove Convexity of various Functions | Download |
| 31 | Lec 31- Example problems: Operations preserving Convexity(log-sum, average) and Quasi Convexity | Download |
| 32 | Lec 32-Example Problems: Verify Convexity, Quasi -Convexity and Quasi- Concavity of functions | Download |
| 33 | Lec 33-Example Problems:Perspective function, Product of Convex functions and Pointwise Maximum is Convex | Download |
| 34 | Lec 34- Practical Application: Beamforming in Multi-antenna Wireless Communication | Download |
| 35 | Lec 35 - Practical Application: Maximal Ratio Combiner for Wireless Systems | Download |
| 36 | Lec 36- Practical Application: Multi-antenna Beamforming with Interfering User | Download |
| 37 | Lec 37- Practical Application: Zero-Forcing (ZF) Beamforming with Interfering User | Download |
| 38 | Lec 38- Practical Application: Robust Beamforming With Channel Uncertainity for Wireless Systems | Download |
| 39 | Lec 39- Practical Application: Robust Beamformer Design for Wireless Systems | Download |
| 40 | Lec 40 - Practical Application: Detailed Solution for Robust Beamformer Computation in Wireless Systems Text | Download |
| 41 | Lec 41- Linear modeling and Approximation Problems: Least Squares | Download |
| 42 | Lec 42-Geometric Intuition for Least Squares | Download |
| 43 | Lec 43- Practical Application: Multi antenna channel estimation | Download |
| 44 | Lec 44- Practical Application:Image deblurring | Download |
| 45 | Lec 45- Least Norm Signal Estimation | Download |
| 46 | Lec 46- Regularization: Least Squares + Least Norm | Download |
| 47 | Lec 47- Convex Optimization Problem representation: Canonical form, Epigraph form | Download |
| 48 | Lec 48-Linear Program Practical Application: Base Station Co-operation | Download |
| 49 | Lec 49- Stochastic Linear Program,Gaussian Uncertainty | Download |
| 50 | Lec 50- Practical Application: Multiple Input Multiple Output (MIMO) Beamforming | Download |
| 51 | Lec 51- Practical Application: Multiple Input Multiple Output (MIMO) Beamformer Design | Download |
| 52 | Lec 52-Practical Application: Co-operative Communication, Overview and various Protocols used | Download |
| 53 | Lec 53- Practical Application: Probability of Error Computation for Co-operative Communication | Download |
| 54 | Lec 54- Practical Application:Optimal power allocation factor determination for Co-operative Communication | Download |
| 55 | Lec 55- Practical Application: Compressive Sensing | Download |
| 56 | Lec 56- Practical Application | Download |
| 57 | Lec 57- Practical Application- Orthogonal Matching Pursuit (OMP) algorithm for Compressive Sensing | Download |
| 58 | Lec 58- Example Problem: Orthogonal Matching Pursuit (OMP) algorithm | Download |
| 59 | Lec 59- Practical Application : L1 norm minimization and regularization approach for Compressive Sensing Optimization problem | Download |
| 60 | Lec 60- Practical Application of Machine Learning and Artificial Intelligence:Linear Classification, Overview and Motivation | Download |
| 61 | Lec 61- Practical Application: Linear Classifier (Support Vector Machine) Design | Download |
| 62 | Lec 62- Practical Application: Approximate Classifier Design | Download |
| 63 | Lec 63- Concept of Duality | Download |
| 64 | Lec 64-Relation between optimal value of Primal & Dual Problems, concepts of Duality gap and Strong Duality | Download |
| 65 | Lec 65-Example problem on Strong Duality | Download |
| 66 | Lec 66- Karush-Kuhn-Tucker(KKT) conditions | Download |
| 67 | Lec 67- Application of KKT condition:Optimal MIMO power allocation(Waterfilling) | Download |
| 68 | Lec 68- Optimal MIMO Power allocation(Waterfilling)-II | Download |
| 69 | Lec 69- Example problem on Optimal MIMO Power allocation(Waterfilling) | Download |
| 70 | Lec 70- Linear objective with box constraints, Linear Programming | Download |
| 71 | Lec 71- Example Problems II | Download |
| 72 | Lec 72- Examples on Quadratic Optimization | Download |
| 73 | Lec 73- Examples on Duality: Dual Norm, Dual of Linear Program(LP) | Download |
| 74 | Lec 74- Examples on Duality: Min-Max problem, Analytic Centering | Download |
| 75 | Lec 75- Semi Definite Program(SDP) and its application:MIMO symbol vector decoding | Download |
| 76 | Lec 76- Application:SDP for MIMO Maximum Likelihood(ML) Detection | Download |
| 77 | Lec 77- Introduction to big Data: Online Recommender System(Netflix) | Download |
| 78 | Lec 78- Matrix Completion Problem in Big Data: Netflix-I | Download |
| 79 | Lec 79- Matrix Completion Problem in Big Data: Netflix-II | Download |
| Sl.No | Chapter Name | Tamil |
|---|---|---|
| 1 | Lec 01- Vectors and Matrices- Linear Independence and Rank | Download |
| 2 | Lec 02 - Eigenvectors and Eigenvalues of Matrices and their Properties | Download |
| 3 | Lec 03 - Positive Semidefinite (PSD) and Postive Definite (PD) Matrices and their Properties | Download |
| 4 | Lec 04 - Inner Product Space and it's Properties: Linearity, Symmetry and Positive Semi-definite | Download |
| 5 | Lec 05 - Inner Product Space and its Properties: Cauchy Schwarz Inequality | Download |
| 6 | Lec 06 - Properties of Norm, Gaussian Elimination and Echleon form of matrix | Download |
| 7 | Lec 07- Gram Schmidt Orthogonalization Procedure | Download |
| 8 | Lec 08- Null Space and Trace of Matrices | Download |
| 9 | Lec 09- Eigenvalue Decomposition of Hermitian Matrices and Properties | Download |
| 10 | Lec 10- Matrix Inversion Lemma (Woodbury identity) | Download |
| 11 | Lec 11- Introduction to Convex Sets and Properties | Download |
| 12 | Lec 12- Affine Set Examples and Application | Download |
| 13 | Lec 13- Norm Ball and its Practical Applications | Download |
| 14 | Lec 14- Ellipsoid and its Practical Applications | Download |
| 15 | Lec 15- Norm Cone,Polyhedron and its Applications | Download |
| 16 | Lec 16- Applications: Cooperative Cellular Transmission | Download |
| 17 | Lec 17- Positive Semi Definite Cone And Positive Semi Definite (PSD) Matrices | Download |
| 18 | Lec 18-Introduction to Affine functions and examples | Download |
| 19 | Lecture 19-norm balls and Matrix properties:Trace,Determinant | Download |
| 20 | Lecture 20-Inverse of a Positive Definite Matrix | Download |
| 21 | Lecture 21-Example Problems: Property of Norms,Problems on Convex Sets | Download |
| 22 | Lecture 22-Problems on Convex Sets(contd.) | Download |
| 23 | Lecture 23-Introduction to Convex and Concave Functions | Download |
| 24 | Lecture 24-Properties of Convex Functions with examples | Download |
| 25 | Lec 25-Test for Convexity: Positive Semidefinite Hessian Matrix | Download |
| 26 | Lec 26-Application: MIMO Receiver Design as a Least Squares Problem | Download |
| 27 | Lec 27-Jensen's Inequality and Practical Application | Download |
| 28 | Lec 28-Jensen's Inequality application | Download |
| 29 | Lec 29 - Properties of Convex Functions | Download |
| 30 | Lec 30 - Conjugate Function and Examples to prove Convexity of various Functions | Download |
| 31 | Lec 31- Example problems: Operations preserving Convexity(log-sum, average) and Quasi Convexity | Download |
| 32 | Lec 32-Example Problems: Verify Convexity, Quasi -Convexity and Quasi- Concavity of functions | Download |
| 33 | Lec 33-Example Problems:Perspective function, Product of Convex functions and Pointwise Maximum is Convex | Download |
| 34 | Lec 34- Practical Application: Beamforming in Multi-antenna Wireless Communication | Download |
| 35 | Lec 35 - Practical Application: Maximal Ratio Combiner for Wireless Systems | Download |
| 36 | Lec 36- Practical Application: Multi-antenna Beamforming with Interfering User | Download |
| 37 | Lec 37- Practical Application: Zero-Forcing (ZF) Beamforming with Interfering User | Download |
| 38 | Lec 38- Practical Application: Robust Beamforming With Channel Uncertainity for Wireless Systems | Download |
| 39 | Lec 39- Practical Application: Robust Beamformer Design for Wireless Systems | Download |
| 40 | Lec 40 - Practical Application: Detailed Solution for Robust Beamformer Computation in Wireless Systems Text | Download |
| 41 | Lec 41- Linear modeling and Approximation Problems: Least Squares | Download |
| 42 | Lec 42-Geometric Intuition for Least Squares | Download |
| 43 | Lec 43- Practical Application: Multi antenna channel estimation | Download |
| 44 | Lec 44- Practical Application:Image deblurring | Download |
| 45 | Lec 45- Least Norm Signal Estimation | Download |
| 46 | Lec 46- Regularization: Least Squares + Least Norm | Download |
| 47 | Lec 47- Convex Optimization Problem representation: Canonical form, Epigraph form | Download |
| 48 | Lec 48-Linear Program Practical Application: Base Station Co-operation | Download |
| 49 | Lec 49- Stochastic Linear Program,Gaussian Uncertainty | Download |
| 50 | Lec 50- Practical Application: Multiple Input Multiple Output (MIMO) Beamforming | Download |
| 51 | Lec 51- Practical Application: Multiple Input Multiple Output (MIMO) Beamformer Design | Download |
| 52 | Lec 52-Practical Application: Co-operative Communication, Overview and various Protocols used | Download |
| 53 | Lec 53- Practical Application: Probability of Error Computation for Co-operative Communication | Download |
| 54 | Lec 54- Practical Application:Optimal power allocation factor determination for Co-operative Communication | Download |
| 55 | Lec 55- Practical Application: Compressive Sensing | Download |
| 56 | Lec 56- Practical Application | Download |
| 57 | Lec 57- Practical Application- Orthogonal Matching Pursuit (OMP) algorithm for Compressive Sensing | Download |
| 58 | Lec 58- Example Problem: Orthogonal Matching Pursuit (OMP) algorithm | Download |
| 59 | Lec 59- Practical Application : L1 norm minimization and regularization approach for Compressive Sensing Optimization problem | Download |
| 60 | Lec 60- Practical Application of Machine Learning and Artificial Intelligence:Linear Classification, Overview and Motivation | Download |
| 61 | Lec 61- Practical Application: Linear Classifier (Support Vector Machine) Design | Download |
| 62 | Lec 62- Practical Application: Approximate Classifier Design | Download |
| 63 | Lec 63- Concept of Duality | Download |
| 64 | Lec 64-Relation between optimal value of Primal & Dual Problems, concepts of Duality gap and Strong Duality | Download |
| 65 | Lec 65-Example problem on Strong Duality | Download |
| 66 | Lec 66- Karush-Kuhn-Tucker(KKT) conditions | Download |
| 67 | Lec 67- Application of KKT condition:Optimal MIMO power allocation(Waterfilling) | Download |
| 68 | Lec 68- Optimal MIMO Power allocation(Waterfilling)-II | Download |
| 69 | Lec 69- Example problem on Optimal MIMO Power allocation(Waterfilling) | Download |
| 70 | Lec 70- Linear objective with box constraints, Linear Programming | Download |
| 71 | Lec 71- Example Problems II | Download |
| 72 | Lec 72- Examples on Quadratic Optimization | Download |
| 73 | Lec 73- Examples on Duality: Dual Norm, Dual of Linear Program(LP) | Download |
| 74 | Lec 74- Examples on Duality: Min-Max problem, Analytic Centering | Download |
| 75 | Lec 75- Semi Definite Program(SDP) and its application:MIMO symbol vector decoding | Download |
| 76 | Lec 76- Application:SDP for MIMO Maximum Likelihood(ML) Detection | Download |
| 77 | Lec 77- Introduction to big Data: Online Recommender System(Netflix) | Download |
| 78 | Lec 78- Matrix Completion Problem in Big Data: Netflix-I | Download |
| 79 | Lec 79- Matrix Completion Problem in Big Data: Netflix-II | Download |