Covers machine learning, deep learning, generative AI, natural language processing, computer vision, and intelligent systems.
These are the required courses that define the major specialization journey.
History and foundations of AI, The Turing Test and Chinese Room Argument. Intelligent Agents: Agents and Environments, The Concept of Rationality, The Nature of Environments (Fully/Partially observable, Deterministic/...
Core concepts and taxonomy of machine learning (supervised, unsupervised, reinforcement learning), Bias-variance tradeoff and model capacity, Overfitting, underfitting, and the No Free Lunch theorem, Evaluation metric...
Perceptron and multi-layer perceptron (MLP), Activation functions (sigmoid, tanh, ReLU, Leaky ReLU, Swish), Forward propagation and backpropagation algorithm derivation, Gradient descent optimization (SGD, momentum, A...
Biological evolution principles and Darwinian natural selection, Genetic algorithms as population-based stochastic optimization, Search space representation and fitness landscapes, Schema theorem and building block hy...
Language modeling and n-gram models, Regular expressions for tokenization, Sentence segmentation and normalization, Stemming, lemmatization, and part-of-speech tagging, Stopword removal and text normalization, Bag-of-...
Image formation and digitization (sampling, quantization), Pixel representations and color spaces (RGB, HSV, Lab, grayscale), Spatial domain filtering (linear, nonlinear filters, smoothing, sharpening), Edge detection...
These electives are available within the same major specialization pathway.
Markov Decision Processes (MDPs) formalization (states, actions, transition probabilities, rewards), Bellman equations and optimality principle, Model-free vs. model-based RL, Exploration vs. exploitation tradeoff ( -...
ML project lifecycle stages (data, training, validation, deployment, monitoring), MLOps maturity levels (manual, ML automation, continuous ML), Model drift types (concept drift, data drift, upstream drift), Golden ML...
Generative vs. discriminative models, Maximum likelihood estimation for density estimation, Autoregressive models and sequential generation, Latent variable models, Evaluation metrics (log-likelihood, FID, IS scores),...
Agent definitions and properties (autonomy, reactivity, proactivity, social ability), Multi-agent vs. single-agent systems, Agent architectures (reactive, deliberative, hybrid, BDI), Environments (accessible, determin...
Explainability vs. interpretability vs. transparency, Scope of explanations (local/global, model-agnostic/model-specific), Explanation types (counterfactuals, prototypes, saliency maps), Human-centered XAI design prin...
Directed (Bayesian networks) vs. undirected (Markov Random Fields) models, Factorization of joint distributions, Conditional independence and d-separation, Markov properties and Hammersley-Clifford theorem, Graphical...