Week 1:Introduction to vectors, properties and applications
Week 2:Introduction to matrices and Applications – Circuits, Graphs, Social Networks, Traffic flow
Week 3: Eigenvalue decomposition, properties and Applications – Principal component analysis (PCA), Eigenfaces for facial recognition
Week 4:Singular value decomposition (SVD) and Applications – Beamforming in MIMO, Dimensionality reduction, Rate maximization in wireless, MUSIC algorithm
Week 5:Linear regression and Least Squares. Applications: System identification, linear regression, Support vector machines (SVM), kernel SVMs
Week 6:Optimal linear MMSE estimation. Applications – MMSE Receiver, Market prediction and forecasting, ARMA models
Week 7: Data analytics: Recommender systems, user rating prediction, NETFLIX problem
Week 8:Structure of FFT/ IFFT matrices, properties, System model for OFDM/ SC-FDMA, Signal processing in OFDM systems. Modeling of Dynamical systems – Examples: Robots, Chemical plants. Solution of autonomous linear dynamical systems (LDS), solution of with inputs and outputs
Week 9:Modeling of Dynamical systems – Examples: Robots, Chemical plants. Solution of autonomous linear dynamical systems (LDS), solution of with inputs and outputs
Week 10:Unsupervised learning: Centroid based clustering, probabilistic model based clustering and EM algorithm
Week 11: Linear perceptron. Training a perceptron – stochastic gradient. Compressive sensing, orthogonal matching pursuit for sparse signal estimation
Week 12:Discrete time Markov chains – Applications: supply chain management, forecasting, Operations research – resource and inventory management.
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