LEARN • PRACTICE • BUILD • RESEARCH
Explore structured academic courses across Mathematics, Programming, Computer Science, Artificial Intelligence and Data Science — supported by notes, assignments, quizzes, projects and practical learning resources.
COURSE CATALOG
Select an academic area to quickly navigate to relevant courses and learning materials.
Calculus, Linear Algebra, Discrete Mathematics, Statistics, Numerical Analysis and Modeling.
Programming, algorithms, data structures, databases and computational thinking.
Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision and NLP.
Data Analytics, Statistics, Visualization, Data Mining and Machine Learning.
ACADEMIC AREA
Mathematical foundations for computing, data science, artificial intelligence and scientific problem solving.
Limits, continuity, differentiation, integration, applications of calculus and mathematical problem solving.
Vectors, matrices, systems of linear equations, vector spaces, eigenvalues and applications in computing and AI.
Logic, sets, relations, functions, combinatorics, graphs, trees and mathematical reasoning for computing.
ACADEMIC AREA
Programming and computational foundations for software development and intelligent systems.
Python fundamentals, programming concepts, problem solving, functions, data structures and practical projects.
Arrays, linked lists, stacks, queues, trees, graphs, searching, sorting and algorithmic problem solving.
Database concepts, relational models, SQL, normalization and practical database applications.
ACADEMIC AREA
Intelligent systems, machine learning and modern AI concepts from foundations to practical applications.
Intelligent agents, problem solving, search strategies, knowledge representation, reasoning and learning.
Supervised and unsupervised learning, feature engineering, model evaluation, regression, classification and practical ML.
Neural networks, deep learning architectures, optimization, computer vision and modern deep learning applications.
ACADEMIC AREA
Learn how to transform data into insights using statistics, visualization, analytics and machine learning.
Data preparation, exploratory data analysis, visualization, statistical analysis and data-driven decision making.
Principles of data visualization, dashboards, charts, storytelling and communicating analytical insights.
LEARNING RESOURCES
PMAI Lab courses are designed to connect theoretical foundations with practical learning and project-based experience.
Structured course outlines, topics, semester information and learning objectives.
Faculty-provided lecture notes and organized academic learning materials.
Practice assignments designed to strengthen concepts and problem-solving skills.
Revision quizzes and assessment resources for effective exam preparation.
Previous examination papers and practice resources where available.
Recommended references, external resources, tutorials and supplementary materials.
FOR FACULTY & ACADEMIC CONTRIBUTORS
Faculty members can contribute approved course outlines, lecture notes, assignments, quizzes, projects and other academic resources to the PMAI Lab learning platform.
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