Mathematics for Artificial Intelligence
Focuses on discrete structures and mathematical models for AI, covering combinatorics, graph search algorithms (DFS, BFS), function optimization methods, dynamical models analysis/simulation, and probabilistic modeling.
TA Responsibilities- Draft and grade hands-on laboratory assignments and practical exercises.
Data Structures and Algorithms
Explores recursion, Big-O complexity analysis, sorting algorithms (O(n²), O(n log n), Radix), linear structures (Lists, Stacks, Queues), trees (BST, B-trees, self-balancing trees), graphs (traversals, MST, shortest path), and hash tables.
TA Responsibilities- Instruct weekly laboratory and hands-on programming sessions.
- Draft and grade practical assignments, coding exercises, and projects.
- Organize continuous assessment quizzes and coordinate midterm examinations.
Fundamentals of Programming
Provides basic and advanced C/C++ programming concepts, covering control flow, functions, structs, multi-file projects, pointers, dynamic memory management, binary file handling, and basic data structures (linked lists, stacks, queues).
TA Responsibilities- Instruct weekly laboratory and hands-on programming sessions.
- Draft and grade practical assignments, coding exercises, and projects.
- Organize continuous assessment quizzes and coordinate midterm examinations.
Fundamentals of Programming for AI
Introduces foundational Python programming for AI contexts, covering control flow, functions, built-in structures (lists, dicts, tuples, sets), string operations, text/structured files, recursion, basic dynamic programming, and data manipulation using NumPy.
TA Responsibilities- Instruct weekly laboratory and hands-on programming sessions.
- Draft and grade practical assignments, coding exercises, and projects.
Computational Thinking
Introduces core computational thinking pillars—decomposition, pattern recognition, abstraction, and algorithm design—using Python, covering algorithmic problem formulation, rapid UI prototyping (Streamlit/Gradio), modern API integrations (Hugging Face, FastAPI, OpenStreetMap), and agile project-based development.
TA Responsibilities- Instruct weekly laboratory sessions covering Git/GitHub, FastAPI, Hugging Face APIs, and rapid prototyping.
- Mentor student teams through project milestones (Proof of Concept, MVP, and final product deployment).
- Draft and grade practical laboratory assignments, weekly quizzes, and project evaluations.
Introduction to Statistical Linguistics
Provides an overview of statistical linguistics and corpus engineering, covering probability distributions, parameter estimation, hypothesis testing, vocabulary analysis, planning and sampling monolingual/bilingual corpora, text preprocessing, linguistic labeling, and applications in language research.
TA Responsibilities- Design a hands-on challenge and evaluate student submissions.
Social Network Analysis
Covers graph theory fundamentals, network metrics, social network modeling (small-world, Barabási–Albert), community detection, information diffusion, node influence, connectivity analysis, and graph-based recommendation systems.
TA Responsibilities- Instruct weekly laboratory sessions and practical social network analysis exercises.
- Draft and grade hands-on programming projects and real-world network data challenges.
Text Mining and Applications
Provides an overview of text corpus mining, covering text representation, classification, clustering, representation-based learning, semantic and opinion mining, vector space queries, and information extraction from large textual databases.
TA Responsibilities- Design a hands-on challenge and evaluate student submissions.
Knowledge Engineering Seminars
Explores fundamental artificial intelligence concepts, spoken language processing solutions, and cryptography applications in security and blockchain systems, while training students in literature review, academic proposal writing, and technical presentations.
TA Responsibilities- Draft and grade hands-on laboratory assignments and research project milestones.
