Probability theory, random variables, and random processes, Bayes rule, Markov and Poisson processes, joint-marginal and conditional densities, autocorrelation, cross-correlation, linear and non-linear transformations, least-square estimation, Mote Carlo simulations.

Image Formation: Modalities, Sensing, Human Vision System, and Models, color fundamentals and color space, Histogram Processing, Spatial Filtering: Convolution, Smoothing, Sharpening, Frequency Domain analysis, Image restoration, Template Matching and Image Pyramid, Edge Detection, Line Segment Detection, Fitting Parametric Models, Harris Corner Detection, SIFT: Key point Detection and descriptor, Image classification.

Statistical Learning, Confidence Interval and Hypothesis testing, load profile analytics, EDA, Load forecasting, Linear Regression, Decision Trees, Ensemble Learning, Convex Optimization, Gurobi Optimization, Support Vector Machines, Principal Component Analysis, Clustering Methods, Big Data Applications, FCNNs, Regularization Methods, CNNs, RNNs.

ML introduction, Probability Theory, Model Selection and Evaluation, Vector Calculus and Optimization, Forward and Backward propagation, Optimization Schemes, Loss and Regularization, Universal Approximation Theorem, Convolutional Neural Networks(CNNs), Recurrent Neural Networks (RNNs), Audio Classification, Dropout and Batch Normalization, Adversarial Learning, Auto Encoders, Generative Adversarial Networks (GANs), VAEs, WGANs, Transformers, Bayesian Neural Networks.

Design of object-oriented systems, using SOLID principles such as the GRASP principles, and methodologies such as CRC cards and the Unified Modeling Language (UML), Software Design patterns, Requirements analysis, Agile Methodology, Static vs. dynamic typing, Metaprogramming, Open-source development practices and tools - refactoring and reimplementation, Test-first development, contributions to an open-source software project.

Linear, Non-linear modeling for inference and decision making, Task Learning, Pac-learning, Artificial Neural Networks (ANNs), CNNs, RNNs, LSTMs, Generalization and Latent Learning: Meta-Learning, AutoEncoders and VAE, Domain Adaptation, Energy Based Learning: Volterra NNs, Generative Models: GANs, Normalizing Flows, Diffusion Model concept, Bayesian Neural Networks, Attention and Transformers Neural Networks, Graph Neural Networks, Graph Attention Networks.

Cloud Computing basics and job roles, Datacenter Fundamentals: Microservice implementations, Tenants, Cloud Computing Analysis: IaaS, SaaS, PaaS, FaaS, private-public-multicloud, Cloud Architecture requirements, principles and practices: BRs, TRs, Cloud migration, Load balancing, networking, autoscaling, trade-offs, Cloud automation: automation and orchestration, tools such as Kubernetes, OpenShift, Ansible, nontrivial, lab-based project.

Foundations of NLP, NLP paradigms and techniques, Linguistics and AI, regular expressions, unigrams and n-grams, word embeddings, Part of Speech tagging, Grammar, syntactic and dependency parsing, semantic role labeling, Word Senses, language modeling, sentiment and affect analysis, Information extraction, question answering text-based dialogue, discourse processing, Machine Learning for Language processing: RNNs, LSTMs, GRUs, Attention and Transformers, BERT, LLMs.

Machine Learning introduction, Frequent Pattern Mining, Temporal Sequential Mining, Temporal Pattern Mining, Deep Learning, CNNs, RNNs, LSTMs, GRUs, Dimensionality Reduction, Expectation Maximization (EM), Gaussian Mixture Models (GMMs), GANs, Semi-Supervised Learning, Hidden Markov Models (HMMs), Markov Decision Processes (MDPs), Decision theory, Reinforcement Learning, Deep Reinforcement Learning, Recommendation Systems.

Statistical Machine Learning, Linear and Non-linear classification, Random variables, Bayes Decision Theory Classification, Discriminant Functions, ML and MAP parameter estimation, Mixture Models, Bayesian Inference, Parzen windows, kNN density estimation and classification, Perceptron algorithm, MMSE and least squares, SVMs, XOR problem, gradient search, backpropagation, Decision trees, Boosting, feature selection, LDA, Structural Risk Minimization, BIC, Feature generation: PCA, SVC, ICA, template matching, Bellman's principle, Clustering: Proximity measures, sequential, hierarchical, Mixture Decomposition.

Discriminative and generative modeling and MLE algorithm, Linear and Logistic regression, Mixture models, Kernels and the kernel trick, Factor analysis, Gaussian Process, classification models, Graphical Models, Models for chains and trees - inference: MAP, dynamic programming, MRF-MAP-CRF inference, Temporal Models: Kalman Filter, Particle filtering, Vision Language Models, Image Grammar Models, Probabilistic Models, Deep Learning + Monte Carlo Tree search, Visualizing and understanding CNNs, Computer Vision tools and packages.

Techniques for machine learning using probabilistic graphical models (PGM). Emphasis on Bayesian and Markov networks, and Probabilistic deep neural networks with applications to signal processing and computer vision. Bayesian Inference, Directed Graphical Models, Undirected Graphical Models, Inference algorithms, Message Passing, Variational Inference, Markov Chain Monte Carlo, Graph Neural Networks, Bayesian Neural Networks, Gaussian Processes,RBMs, Deep Belief Networks, Variational AutoEncoders, Diffusion Models, Bayesian Optimization.