Introduction-Recovery algorithms - greedy and convex-The theory of compressed sensing

Introduction Sparse Signal Recovery via Basis Pursuit with RIP- Sparse Signal Recovery via Basis Pursuit without RIP-Sparse Signal Recovery via Greedy Algorithms- Concentration inequalities

Matrix Completion -Robust PCA-Atomic Norms-new advances and Application

Introduction- Overview of Sampling and Reconstruction. Oversampling techniques. Fundamentals of Multi-Rate Systems-Basic building blocks–Up sampling, down sampling, aliasing. Sampling rate change and filtering, fractional sampling rate change. Inter connection of multirate DSP blocks, Polyphase decomposition, Noble Identities, efficient implementation of sampling rate conversion.

Information theory: Information – Entropy, Information rate, classification of codes, Kraft McMillanin equality, Source coding theorem, Shannon ‐ Fano coding, Huffman coding, Extended Huffman coding ‐ Joint and conditional entropies, Mutual information ‐ Discrete memory less channels – BSC, BEC – Channel capacity, Shannon limit.

Computational characteristics of DSP algorithms and applications; Architectural requirements of DSPs: high throughput, low cost, low power, small code size, embedded applications. Numerical representation of signals-word length effect and its impact. Carry free adders, multiplier. Representation of digital signal processing systems: block diagrams, signal flow graphs, data-flow graphs, dependence graphs; Techniques for enhancing computational throughput: parallelism and pipelining.

Introduction to the science and design of robots - behavior-based embodied artificial intelligence, kinematics and inverse kinematics, geometric reasoning, motion planning, mapping, and manipulation, dynamics, biologically inspired and biomimetic robotics, distributed robotics and intelligence, and some philosophical questions pertaining to the nature of intelligence in the physical world.

Introduction to Robotics, robotics Motion Control, Probability/Sensing, Basics of Kalman Filters and the invariants, Localization (SLAM,V-SLAM)

Breakthroughs in computer vision, including object, scene, and activity recognition. Deep learning and statistical techniques. Developments in new research and advances in machine learning, statistical image modeling, sparse coding, graph/information theory, and other methodologies. Surveys of the latest trends in high-level vision in terms of problems and mathematical-theory formulations.

Topics will be from:

New Theory and Methodologies in Computer Vision

New Applications of Computer Vision

Societal aspects of computer vision

Introduction of Biomedical Signals and Images: ECG, EEG, Imaging Modalities: Survey of major modalities for medical imaging: ultrasound, X-ray, CT, MRI, PET, and SPECT, MRI, FMRI, Various Applications

Introduction (ML applications)-topics in Linear system (linear regression)-Basics of Gradient Descent and its variants (logistic regression)-A detailed understanding of Projected Gradient (white-box adversarial attack)and Proximal Gradient (lasso)-Details of Conditional Gradient (recommendation system)-The Sub gradient approach (svm)-Mirror Descent and Metric Gradient methods-Acceleration (total variation denoising)-Smoothing (robust svm)-optimal transport for machine learning -Alternating (VAE)- Minimax (adversarial training)-Averaging (GANs)-Splitting (federated learning)-Extra gradient (

The Perceptron, Feed-forward networks and Multi-layer perceptron, Memory based networks like Boltzmann Machines, Hopfield Networks. State based networks like Recurrent Neural Networks, Long Short Term Memory Networks.

Introduction to Neural network and Back propagation-Basics of Tensor flow and Keras-Details on Convolutional Neural Networks and types of different convolutions-recent topics in Recurrent Neural Net-works, LSTM, GRUs-Time series Processing-Details of Transformer Networks for text and Vision- Instance and Semantic Segmentation-Generative Models, VAE-Deep Generative Adversarial Networks- Model Interpretation etc.

Review of basic probability and stochastic processes. Introduction to Markov chains. Markov models for discrete time dynamic systems, Reward, Policies, Policy evaluation, Markov decision processes, Optimality criteria, Bellman’s optimality principle, Dynamic programming, Optimality equations, Policy search, Policy iteration, Value iteration. Generalized Policy Iteration, Approximate dynamic programming.

Introduction of deep learning- Foundations of deep learning, basics aspects of machine learning, artificial intelligence, mathematics, statistics, and neurosciences (both theory and applications)- Applications in self-driving cars, new kinds of video games, AI, Automation, object detection and recognition, surveillance tracking etc.- Introduce to Neural networks and Deep learning approaches (mainly Convolutional Neural networks) and give a few typical applications.