Modules / Lectures
Module NameDownload


Sl.No Chapter Name MP4 Download
1Lec 01- Vector Properties: Addition, Linear Combination, Inner Product, Orthogonality, Norm Download
2Lec 02- Vectors: Unit Norm Vector, Cauchy-Schwarz inequality, Radar Application Download
3Lec 03- Inner Product Application: Beamforming in Wireless Communication Systems Download
4Lec 04- Matrices, Definition, Addition and Multiplication of Matrices Download
5Lec 05- Matrix: Column Space, Linear Independence, Rank of Matrix, Gaussian Elimination Download
6Lec 06- Matrix: Determinant, Inverse Computation, Adjoint, Cofactor Concepts Download
7Lec 07- Applications of Matrices: Solution of System of Linear equations, MIMO Wireless Technology Download
8Lec 08- Applications of Matrices: Electric Circuits, Traffic flows Download
9Lec 09- Applications of Matrices: Graph Theory, Social Networks, Dominance Directed Graph, Influential Node Download
10Lec 10- Null Space of Matrix: Definition, Rank-Nullity Theorem, Application in Electric Circuits Download
11Lec 11- Gram-Schmidt Orthogonalization Download
12Lec 12- Gaussian Random Variable: Definition, Mean, Variance, Multivariate Gaussian, Covariance Matrix Download
13Lec 13- Linear Transformation of Gaussian Random Vectors Download
14Lec 14- Machine Learning Application: Gaussian Classification Download
15Lec 15- Eigenvalue: Definition, Characteristic Equation, Eigenvalue Decomposition Download
16Lec 16- Special Matrices: Rotation and Unitary Matrices, Application: Alamouti Code Download
17Lec 17- Positive Semi-definite (PSD) Matrices: Definition, Properties, Eigenvalue Decomposition Download
18Lec 18- Positive Semidefinite Matrix: Example and Illustration of Eigenvalue Decomposition Download
19Lec 19- Machine Learning Application: Principle Component Analysis (PCA) Download
20Lec 20- Computer Vision Application: Face Recognition, Eigenfaces Download
21Lec 21- Least Squares (LS) Solution, Pseudo-Inverse Concept Download
22Lec 22- Least Squares (LS) via Principle of Orthogonality, Projection Matrix, Properties Download
23Lec 23- Application: Pseudo-Inverse and MIMO Zero Forcing (ZF) Receiver Download
24Lec 24- Wireless Application: Multi-Antenna Channel EstimationDownload
25Lec 25- Machine Learning Application: Linear RegressionDownload
26Lec 26- Computation Mathematics Application: Polynomial Fitting Download
27Lec 27- Least Norm Solution Download
28Lec 28- Wireless Application: Multi-user Beamforming Download
29Lec 29- Singular Value Decomposition (SVD): Definition, Properties, Example Download
30Lec 30- SVD Application in MIMO Wireless Technology: Spatial-Multiplexing and High Data Rates Download
31Lec 31- SVD for MIMO wireless optimization, water-filling algorithm, optimal power allocationDownload
32Lec 32- SVD application for Machine Learning: Principal component analysis (PCA)Download
33Lec 33- Multiple signal classification (MUSIC) algorithm: system model Download
34Lec 34- MUSIC algorithm for Direction of Arrival (DoA) estimation Download
35Lec 35- Linear minimum mean square error (LMMSE) principle Download
36Lec 36- LMMSE estimate and error covariance matrix Download
37Lec 37- LMMSE estimation in linear systems Download
38Lec 38- LMMSE application: Wireless channel estimation and example Download
39Lec 39- Time-series prediction via auto-regressive (AR) model Download
40Lec 40- Recommender system: design and rating prediction Download
41Lec 41- Recommender system: Illustration via movie rating prediction example Download
42Lec 42- Fast Fourier transform (FFT) and Inverse fast Fourier transform (IFFT) Download
43Lec 43- IFFT/ FFT application in Orthogonal Frequency Division Multiplexing (OFDM) wireless technologyDownload
44Lec 44- OFDM system: Circulant matrices and propertiesDownload
45Lec 45- OFDM system model: Transmitter and receiver processingDownload
46Lec 46- Single-carrier frequency division for multiple access (SC-FDMA) technology Download
47Lec 47- Linear dynamical systems: definition and solution via matrix exponential Download
48Lec 48- Linear dynamical systems: matrix exponential via SVD Download
49Lec 49- Machine Learning application: Support Vector Machines (SVM)Download
50Lec 50- Support Vector Machines (SVM): Problem formulation via maximum hyperplane separationDownload
51Lec 51- Sparse regression: problem formulation and relation to Compressive Sensing (CS) Download
52Lec 52- Sparse regression: solution via the Orthogonal Matching Pursuit (OMP) algorithmDownload
53Lec 53- OMP Example for Sparse Regression Download
54Lec 54- Machine Learning Application: Clustering Download
55Lec 55- K-Means Clustering algorithm Download
56Lec 56- Introduction to Stochastic Processes and Markov ChainsDownload
57Lec 57- Discrete Time Markov Chains and Transition Probability MatrixDownload
58Lec 58- Discrete Time Markov Chain ExamplesDownload
59Lec 59- m-STEP Transition Probabilities for Discrete Time Markov ChainsDownload
60Lec 60- Limiting Behavior of Discrete Time Markov ChainsDownload
61Lec 61- Least Squares Revisited: Rank Deficient MatrixDownload
62Lec 62- Least Squares using SVDDownload

Sl.No Chapter Name English
1Lec 01- Vector Properties: Addition, Linear Combination, Inner Product, Orthogonality, Norm PDF unavailable
2Lec 02- Vectors: Unit Norm Vector, Cauchy-Schwarz inequality, Radar Application PDF unavailable
3Lec 03- Inner Product Application: Beamforming in Wireless Communication Systems PDF unavailable
4Lec 04- Matrices, Definition, Addition and Multiplication of Matrices PDF unavailable
5Lec 05- Matrix: Column Space, Linear Independence, Rank of Matrix, Gaussian Elimination PDF unavailable
6Lec 06- Matrix: Determinant, Inverse Computation, Adjoint, Cofactor Concepts PDF unavailable
7Lec 07- Applications of Matrices: Solution of System of Linear equations, MIMO Wireless Technology PDF unavailable
8Lec 08- Applications of Matrices: Electric Circuits, Traffic flows PDF unavailable
9Lec 09- Applications of Matrices: Graph Theory, Social Networks, Dominance Directed Graph, Influential Node PDF unavailable
10Lec 10- Null Space of Matrix: Definition, Rank-Nullity Theorem, Application in Electric Circuits PDF unavailable
11Lec 11- Gram-Schmidt Orthogonalization PDF unavailable
12Lec 12- Gaussian Random Variable: Definition, Mean, Variance, Multivariate Gaussian, Covariance Matrix PDF unavailable
13Lec 13- Linear Transformation of Gaussian Random Vectors PDF unavailable
14Lec 14- Machine Learning Application: Gaussian Classification PDF unavailable
15Lec 15- Eigenvalue: Definition, Characteristic Equation, Eigenvalue Decomposition PDF unavailable
16Lec 16- Special Matrices: Rotation and Unitary Matrices, Application: Alamouti Code PDF unavailable
17Lec 17- Positive Semi-definite (PSD) Matrices: Definition, Properties, Eigenvalue Decomposition PDF unavailable
18Lec 18- Positive Semidefinite Matrix: Example and Illustration of Eigenvalue Decomposition PDF unavailable
19Lec 19- Machine Learning Application: Principle Component Analysis (PCA) PDF unavailable
20Lec 20- Computer Vision Application: Face Recognition, Eigenfaces PDF unavailable
21Lec 21- Least Squares (LS) Solution, Pseudo-Inverse Concept PDF unavailable
22Lec 22- Least Squares (LS) via Principle of Orthogonality, Projection Matrix, Properties PDF unavailable
23Lec 23- Application: Pseudo-Inverse and MIMO Zero Forcing (ZF) Receiver PDF unavailable
24Lec 24- Wireless Application: Multi-Antenna Channel EstimationPDF unavailable
25Lec 25- Machine Learning Application: Linear RegressionPDF unavailable
26Lec 26- Computation Mathematics Application: Polynomial Fitting PDF unavailable
27Lec 27- Least Norm Solution PDF unavailable
28Lec 28- Wireless Application: Multi-user Beamforming PDF unavailable
29Lec 29- Singular Value Decomposition (SVD): Definition, Properties, Example PDF unavailable
30Lec 30- SVD Application in MIMO Wireless Technology: Spatial-Multiplexing and High Data Rates PDF unavailable
31Lec 31- SVD for MIMO wireless optimization, water-filling algorithm, optimal power allocationPDF unavailable
32Lec 32- SVD application for Machine Learning: Principal component analysis (PCA)PDF unavailable
33Lec 33- Multiple signal classification (MUSIC) algorithm: system model PDF unavailable
34Lec 34- MUSIC algorithm for Direction of Arrival (DoA) estimation PDF unavailable
35Lec 35- Linear minimum mean square error (LMMSE) principle PDF unavailable
36Lec 36- LMMSE estimate and error covariance matrix PDF unavailable
37Lec 37- LMMSE estimation in linear systems PDF unavailable
38Lec 38- LMMSE application: Wireless channel estimation and example PDF unavailable
39Lec 39- Time-series prediction via auto-regressive (AR) model PDF unavailable
40Lec 40- Recommender system: design and rating prediction PDF unavailable
41Lec 41- Recommender system: Illustration via movie rating prediction example PDF unavailable
42Lec 42- Fast Fourier transform (FFT) and Inverse fast Fourier transform (IFFT) PDF unavailable
43Lec 43- IFFT/ FFT application in Orthogonal Frequency Division Multiplexing (OFDM) wireless technologyPDF unavailable
44Lec 44- OFDM system: Circulant matrices and propertiesPDF unavailable
45Lec 45- OFDM system model: Transmitter and receiver processingPDF unavailable
46Lec 46- Single-carrier frequency division for multiple access (SC-FDMA) technology PDF unavailable
47Lec 47- Linear dynamical systems: definition and solution via matrix exponential PDF unavailable
48Lec 48- Linear dynamical systems: matrix exponential via SVD PDF unavailable
49Lec 49- Machine Learning application: Support Vector Machines (SVM)PDF unavailable
50Lec 50- Support Vector Machines (SVM): Problem formulation via maximum hyperplane separationPDF unavailable
51Lec 51- Sparse regression: problem formulation and relation to Compressive Sensing (CS) PDF unavailable
52Lec 52- Sparse regression: solution via the Orthogonal Matching Pursuit (OMP) algorithmPDF unavailable
53Lec 53- OMP Example for Sparse Regression PDF unavailable
54Lec 54- Machine Learning Application: Clustering PDF unavailable
55Lec 55- K-Means Clustering algorithm PDF unavailable
56Lec 56- Introduction to Stochastic Processes and Markov ChainsPDF unavailable
57Lec 57- Discrete Time Markov Chains and Transition Probability MatrixPDF unavailable
58Lec 58- Discrete Time Markov Chain ExamplesPDF unavailable
59Lec 59- m-STEP Transition Probabilities for Discrete Time Markov ChainsPDF unavailable
60Lec 60- Limiting Behavior of Discrete Time Markov ChainsPDF unavailable
61Lec 61- Least Squares Revisited: Rank Deficient MatrixPDF unavailable
62Lec 62- Least Squares using SVDPDF unavailable


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7MarathiNot Available
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