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.

Human visual system and image perception; monochrome and color vision models; image digitization, display, and storage; 2‐D signals and systems; image transforms‐ 2D DFT, DCT, KLT, Harr transform and discrete wavelet transform; image enhancement: histogram processing.

Feature Detection and Characterization, Scale Space idea, Laplacian and Gaussian Derivatives, Differential Invariant Structure–Nonlinear Scale Space, Anisotropic Diffusion, PDE for image processing

Image Enhancement-Noise models, image de-noising using linear filters, order statistics-based filters and wavelet shrinkage methods, image sharpening, image super-resolution using Bayesian methods

Image segmentation: Graph-based techniques, Active Contours, Active Shape Models, Shape Analysis

PR overview - Feature extraction - Statistical Pattern Recognition - Supervised Learning - Parametric methods - non-parametric methods; ML estimation - Bayes estimation - KNN approaches. Dimensionality reduction, data normalization. Regression, and time series analysis. Linear discriminant functions. Fisher's linear discriminant and linear perceptron. Kernel methods and Support vector machine. Decision trees for classification. Unsupervised learning and clustering. K-means and hierarchical clustering. Decision Trees for classification.