| Module Name | Download |
|---|---|
| noc21_ee33_assignment_Week_1 | noc21_ee33_assignment_Week_1 |
| noc21_ee33_assignment_Week_10 | noc21_ee33_assignment_Week_10 |
| noc21_ee33_assignment_Week_11 | noc21_ee33_assignment_Week_11 |
| noc21_ee33_assignment_Week_12 | noc21_ee33_assignment_Week_12 |
| noc21_ee33_assignment_Week_2 | noc21_ee33_assignment_Week_2 |
| noc21_ee33_assignment_Week_3 | noc21_ee33_assignment_Week_3 |
| noc21_ee33_assignment_Week_4 | noc21_ee33_assignment_Week_4 |
| noc21_ee33_assignment_Week_5 | noc21_ee33_assignment_Week_5 |
| noc21_ee33_assignment_Week_6 | noc21_ee33_assignment_Week_6 |
| noc21_ee33_assignment_Week_7 | noc21_ee33_assignment_Week_7 |
| noc21_ee33_assignment_Week_8 | noc21_ee33_assignment_Week_8 |
| noc21_ee33_assignment_Week_9 | noc21_ee33_assignment_Week_9 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lec 01- Vector Properties: Addition, Linear Combination, Inner Product, Orthogonality, Norm | Download |
| 2 | Lec 02- Vectors: Unit Norm Vector, Cauchy-Schwarz inequality, Radar Application | Download |
| 3 | Lec 03- Inner Product Application: Beamforming in Wireless Communication Systems | Download |
| 4 | Lec 04- Matrices, Definition, Addition and Multiplication of Matrices | Download |
| 5 | Lec 05- Matrix: Column Space, Linear Independence, Rank of Matrix, Gaussian Elimination | Download |
| 6 | Lec 06- Matrix: Determinant, Inverse Computation, Adjoint, Cofactor Concepts | Download |
| 7 | Lec 07- Applications of Matrices: Solution of System of Linear equations, MIMO Wireless Technology | Download |
| 8 | Lec 08- Applications of Matrices: Electric Circuits, Traffic flows | Download |
| 9 | Lec 09- Applications of Matrices: Graph Theory, Social Networks, Dominance Directed Graph, Influential Node | Download |
| 10 | Lec 10- Null Space of Matrix: Definition, Rank-Nullity Theorem, Application in Electric Circuits | Download |
| 11 | Lec 11- Gram-Schmidt Orthogonalization | Download |
| 12 | Lec 12- Gaussian Random Variable: Definition, Mean, Variance, Multivariate Gaussian, Covariance Matrix | Download |
| 13 | Lec 13- Linear Transformation of Gaussian Random Vectors | Download |
| 14 | Lec 14- Machine Learning Application: Gaussian Classification | Download |
| 15 | Lec 15- Eigenvalue: Definition, Characteristic Equation, Eigenvalue Decomposition | Download |
| 16 | Lec 16- Special Matrices: Rotation and Unitary Matrices, Application: Alamouti Code | Download |
| 17 | Lec 17- Positive Semi-definite (PSD) Matrices: Definition, Properties, Eigenvalue Decomposition | Download |
| 18 | Lec 18- Positive Semidefinite Matrix: Example and Illustration of Eigenvalue Decomposition | Download |
| 19 | Lec 19- Machine Learning Application: Principle Component Analysis (PCA) | Download |
| 20 | Lec 20- Computer Vision Application: Face Recognition, Eigenfaces | Download |
| 21 | Lec 21- Least Squares (LS) Solution, Pseudo-Inverse Concept | Download |
| 22 | Lec 22- Least Squares (LS) via Principle of Orthogonality, Projection Matrix, Properties | Download |
| 23 | Lec 23- Application: Pseudo-Inverse and MIMO Zero Forcing (ZF) Receiver | Download |
| 24 | Lec 24- Wireless Application: Multi-Antenna Channel Estimation | Download |
| 25 | Lec 25- Machine Learning Application: Linear Regression | Download |
| 26 | Lec 26- Computation Mathematics Application: Polynomial Fitting | Download |
| 27 | Lec 27- Least Norm Solution | Download |
| 28 | Lec 28- Wireless Application: Multi-user Beamforming | Download |
| 29 | Lec 29- Singular Value Decomposition (SVD): Definition, Properties, Example | Download |
| 30 | Lec 30- SVD Application in MIMO Wireless Technology: Spatial-Multiplexing and High Data Rates | Download |
| 31 | Lec 31- SVD for MIMO wireless optimization, water-filling algorithm, optimal power allocation | Download |
| 32 | Lec 32- SVD application for Machine Learning: Principal component analysis (PCA) | Download |
| 33 | Lec 33- Multiple signal classification (MUSIC) algorithm: system model | Download |
| 34 | Lec 34- MUSIC algorithm for Direction of Arrival (DoA) estimation | Download |
| 35 | Lec 35- Linear minimum mean square error (LMMSE) principle | Download |
| 36 | Lec 36- LMMSE estimate and error covariance matrix | Download |
| 37 | Lec 37- LMMSE estimation in linear systems | Download |
| 38 | Lec 38- LMMSE application: Wireless channel estimation and example | Download |
| 39 | Lec 39- Time-series prediction via auto-regressive (AR) model | Download |
| 40 | Lec 40- Recommender system: design and rating prediction | Download |
| 41 | Lec 41- Recommender system: Illustration via movie rating prediction example | Download |
| 42 | Lec 42- Fast Fourier transform (FFT) and Inverse fast Fourier transform (IFFT) | Download |
| 43 | Lec 43- IFFT/ FFT application in Orthogonal Frequency Division Multiplexing (OFDM) wireless technology | Download |
| 44 | Lec 44- OFDM system: Circulant matrices and properties | Download |
| 45 | Lec 45- OFDM system model: Transmitter and receiver processing | Download |
| 46 | Lec 46- Single-carrier frequency division for multiple access (SC-FDMA) technology | Download |
| 47 | Lec 47- Linear dynamical systems: definition and solution via matrix exponential | Download |
| 48 | Lec 48- Linear dynamical systems: matrix exponential via SVD | Download |
| 49 | Lec 49- Machine Learning application: Support Vector Machines (SVM) | Download |
| 50 | Lec 50- Support Vector Machines (SVM): Problem formulation via maximum hyperplane separation | Download |
| 51 | Lec 51- Sparse regression: problem formulation and relation to Compressive Sensing (CS) | Download |
| 52 | Lec 52- Sparse regression: solution via the Orthogonal Matching Pursuit (OMP) algorithm | Download |
| 53 | Lec 53- OMP Example for Sparse Regression | Download |
| 54 | Lec 54- Machine Learning Application: Clustering | Download |
| 55 | Lec 55- K-Means Clustering algorithm | Download |
| 56 | Lec 56- Introduction to Stochastic Processes and Markov Chains | Download |
| 57 | Lec 57- Discrete Time Markov Chains and Transition Probability Matrix | Download |
| 58 | Lec 58- Discrete Time Markov Chain Examples | Download |
| 59 | Lec 59- m-STEP Transition Probabilities for Discrete Time Markov Chains | Download |
| 60 | Lec 60- Limiting Behavior of Discrete Time Markov Chains | Download |
| 61 | Lec 61- Least Squares Revisited: Rank Deficient Matrix | Download |
| 62 | Lec 62- Least Squares using SVD | Download |
| 63 | Lec 63- Weighted Least Squares | Download |
| 64 | Lec 64- Weighted Least Squares Example | Download |
| 65 | Lec 65- Woodbury Matrix Identity - Matrix Inversion Lemma | Download |
| 66 | Lec 66- Woodbury Matrix Identity - Proof | Download |
| 67 | Lec 67- Conditional Gaussian Density - Mean | Download |
| 68 | Lec 68- Conditional Gaussian Density - Covariance | Download |
| 69 | Lec 69- Scalar Linear Model for Gaussian Estimation | Download |
| 70 | Lec 70- MMSE Estimate and Covariance for the Scalar Linear Model | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lec 01- Vector Properties: Addition, Linear Combination, Inner Product, Orthogonality, Norm | Download Verified |
| 2 | Lec 02- Vectors: Unit Norm Vector, Cauchy-Schwarz inequality, Radar Application | Download Verified |
| 3 | Lec 03- Inner Product Application: Beamforming in Wireless Communication Systems | Download Verified |
| 4 | Lec 04- Matrices, Definition, Addition and Multiplication of Matrices | Download Verified |
| 5 | Lec 05- Matrix: Column Space, Linear Independence, Rank of Matrix, Gaussian Elimination | Download Verified |
| 6 | Lec 06- Matrix: Determinant, Inverse Computation, Adjoint, Cofactor Concepts | Download Verified |
| 7 | Lec 07- Applications of Matrices: Solution of System of Linear equations, MIMO Wireless Technology | Download Verified |
| 8 | Lec 08- Applications of Matrices: Electric Circuits, Traffic flows | Download Verified |
| 9 | Lec 09- Applications of Matrices: Graph Theory, Social Networks, Dominance Directed Graph, Influential Node | Download Verified |
| 10 | Lec 10- Null Space of Matrix: Definition, Rank-Nullity Theorem, Application in Electric Circuits | Download Verified |
| 11 | Lec 11- Gram-Schmidt Orthogonalization | PDF unavailable |
| 12 | Lec 12- Gaussian Random Variable: Definition, Mean, Variance, Multivariate Gaussian, Covariance Matrix | PDF unavailable |
| 13 | Lec 13- Linear Transformation of Gaussian Random Vectors | PDF unavailable |
| 14 | Lec 14- Machine Learning Application: Gaussian Classification | PDF unavailable |
| 15 | Lec 15- Eigenvalue: Definition, Characteristic Equation, Eigenvalue Decomposition | PDF unavailable |
| 16 | Lec 16- Special Matrices: Rotation and Unitary Matrices, Application: Alamouti Code | PDF unavailable |
| 17 | Lec 17- Positive Semi-definite (PSD) Matrices: Definition, Properties, Eigenvalue Decomposition | PDF unavailable |
| 18 | Lec 18- Positive Semidefinite Matrix: Example and Illustration of Eigenvalue Decomposition | PDF unavailable |
| 19 | Lec 19- Machine Learning Application: Principle Component Analysis (PCA) | PDF unavailable |
| 20 | Lec 20- Computer Vision Application: Face Recognition, Eigenfaces | PDF unavailable |
| 21 | Lec 21- Least Squares (LS) Solution, Pseudo-Inverse Concept | Download Verified |
| 22 | Lec 22- Least Squares (LS) via Principle of Orthogonality, Projection Matrix, Properties | PDF unavailable |
| 23 | Lec 23- Application: Pseudo-Inverse and MIMO Zero Forcing (ZF) Receiver | PDF unavailable |
| 24 | Lec 24- Wireless Application: Multi-Antenna Channel Estimation | PDF unavailable |
| 25 | Lec 25- Machine Learning Application: Linear Regression | PDF unavailable |
| 26 | Lec 26- Computation Mathematics Application: Polynomial Fitting | PDF unavailable |
| 27 | Lec 27- Least Norm Solution | PDF unavailable |
| 28 | Lec 28- Wireless Application: Multi-user Beamforming | PDF unavailable |
| 29 | Lec 29- Singular Value Decomposition (SVD): Definition, Properties, Example | PDF unavailable |
| 30 | Lec 30- SVD Application in MIMO Wireless Technology: Spatial-Multiplexing and High Data Rates | PDF unavailable |
| 31 | Lec 31- SVD for MIMO wireless optimization, water-filling algorithm, optimal power allocation | PDF unavailable |
| 32 | Lec 32- SVD application for Machine Learning: Principal component analysis (PCA) | PDF unavailable |
| 33 | Lec 33- Multiple signal classification (MUSIC) algorithm: system model | PDF unavailable |
| 34 | Lec 34- MUSIC algorithm for Direction of Arrival (DoA) estimation | PDF unavailable |
| 35 | Lec 35- Linear minimum mean square error (LMMSE) principle | PDF unavailable |
| 36 | Lec 36- LMMSE estimate and error covariance matrix | PDF unavailable |
| 37 | Lec 37- LMMSE estimation in linear systems | PDF unavailable |
| 38 | Lec 38- LMMSE application: Wireless channel estimation and example | PDF unavailable |
| 39 | Lec 39- Time-series prediction via auto-regressive (AR) model | PDF unavailable |
| 40 | Lec 40- Recommender system: design and rating prediction | PDF unavailable |
| 41 | Lec 41- Recommender system: Illustration via movie rating prediction example | PDF unavailable |
| 42 | Lec 42- Fast Fourier transform (FFT) and Inverse fast Fourier transform (IFFT) | PDF unavailable |
| 43 | Lec 43- IFFT/ FFT application in Orthogonal Frequency Division Multiplexing (OFDM) wireless technology | PDF unavailable |
| 44 | Lec 44- OFDM system: Circulant matrices and properties | PDF unavailable |
| 45 | Lec 45- OFDM system model: Transmitter and receiver processing | PDF unavailable |
| 46 | Lec 46- Single-carrier frequency division for multiple access (SC-FDMA) technology | PDF unavailable |
| 47 | Lec 47- Linear dynamical systems: definition and solution via matrix exponential | PDF unavailable |
| 48 | Lec 48- Linear dynamical systems: matrix exponential via SVD | PDF unavailable |
| 49 | Lec 49- Machine Learning application: Support Vector Machines (SVM) | PDF unavailable |
| 50 | Lec 50- Support Vector Machines (SVM): Problem formulation via maximum hyperplane separation | PDF unavailable |
| 51 | Lec 51- Sparse regression: problem formulation and relation to Compressive Sensing (CS) | PDF unavailable |
| 52 | Lec 52- Sparse regression: solution via the Orthogonal Matching Pursuit (OMP) algorithm | PDF unavailable |
| 53 | Lec 53- OMP Example for Sparse Regression | PDF unavailable |
| 54 | Lec 54- Machine Learning Application: Clustering | PDF unavailable |
| 55 | Lec 55- K-Means Clustering algorithm | PDF unavailable |
| 56 | Lec 56- Introduction to Stochastic Processes and Markov Chains | PDF unavailable |
| 57 | Lec 57- Discrete Time Markov Chains and Transition Probability Matrix | PDF unavailable |
| 58 | Lec 58- Discrete Time Markov Chain Examples | PDF unavailable |
| 59 | Lec 59- m-STEP Transition Probabilities for Discrete Time Markov Chains | PDF unavailable |
| 60 | Lec 60- Limiting Behavior of Discrete Time Markov Chains | PDF unavailable |
| 61 | Lec 61- Least Squares Revisited: Rank Deficient Matrix | PDF unavailable |
| 62 | Lec 62- Least Squares using SVD | PDF unavailable |
| 63 | Lec 63- Weighted Least Squares | PDF unavailable |
| 64 | Lec 64- Weighted Least Squares Example | PDF unavailable |
| 65 | Lec 65- Woodbury Matrix Identity - Matrix Inversion Lemma | PDF unavailable |
| 66 | Lec 66- Woodbury Matrix Identity - Proof | PDF unavailable |
| 67 | Lec 67- Conditional Gaussian Density - Mean | PDF unavailable |
| 68 | Lec 68- Conditional Gaussian Density - Covariance | PDF unavailable |
| 69 | Lec 69- Scalar Linear Model for Gaussian Estimation | PDF unavailable |
| 70 | Lec 70- MMSE Estimate and Covariance for the Scalar Linear Model | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |