Course Duration | Course Level | Course Tuition Fees | Mode of Study | Institute Type |
---|---|---|---|---|
3 Years | Undergraduate | Varies | Full Time | Private |
Semester | Subjects | Topics |
---|---|---|
Semester 1 | 1. Fundamentals of IT 2. Programming in C 3. Mathematics for Computing 4. English Communication 5. Environmental Studies | Basics of Computers, Introduction to Programming, Discrete Mathematics, Communication Skills, Environmental Awareness |
Semester 2 | 1. Data Structures 2. Object-Oriented Programming in C++ 3. Digital Electronics & Logic Design 4. Principles of Management 5. Database Management Systems | Data Organizing, OOP Concepts, Logic Circuits, Management Fundamentals, DBMS Basics |
Semester 3 | 1. Operating Systems 2. Web Technologies 3. Computer Networks 4. Business Accounting 5. Mathematics-II | OS Concepts, HTML/CSS/JavaScript, Networking Basics, Financial Accounting, Advanced Mathematics |
Semester 4 | 1. Software Engineering 2. Java Programming 3. Computer Graphics 4. Microprocessor & Assembly Language 5. Organizational Behavior | Software Development Life Cycle, Java Basics, Graphics Principles, Assembly Language Programming, Study of Organizational Behavior |
Semester 5 | 1. Internet Technologies 2. Multimedia Applications 3. Advanced Database Management Systems 4. Project Work (Part-1) 5. Elective-1 | Web Development Advanced Concepts, Multimedia Tools, Advanced DBMS, Project Research, Specialized Study based on Elective |
Semester 6 | 1. Information Security 2. Cloud Computing 3. Project Work (Part-2) 4. Elective-2 5. Elective-3 | Security Practices, Cloud Services, Project Implementation, Specialized Study based on Electives |
Module | Topics |
---|---|
Introduction to Artificial Intelligence | History of AI, Foundations, and State of the Art |
Machine Learning Basics | Supervised vs Unsupervised Learning, Regression, Classification, Overfitting, Underfitting |
Deep Learning Fundamentals | Neural Networks, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) |
Natural Language Processing (NLP) | Text Processing, Sentiment Analysis, Machine Translation |
Reinforcement Learning | Markov Decision Processes, Q-Learning, Policy Gradient Methods |
AI Ethics and Society | Ethical Implications, Bias in AI, Future of Work |
Practical Machine Learning Projects | Project Design, Data Collection, Model Training, Evaluation |