| Semester | Subjects |
|---|---|
| 1 | Introduction to Programming, Mathematics for Computer Science, Introduction to Artificial Intelligence, English Communication Skills |
| 2 | Data Structures and Algorithms, Database Management Systems, Object-Oriented Programming, Principles of Machine Learning |
| 3 | Operating Systems, Computer Networks, Advanced Mathematics for AI, Machine Learning Algorithms |
| 4 | Web Technologies, Deep Learning Fundamentals, Natural Language Processing, AI & ML Project Management |
| 5 | Big Data Analytics, Robotics and Perception, Reinforcement Learning, Elective (Choose one from specialized topics in AI/ML) |
| 6 | Capstone Project in AI/ML, Industry Internship, Electives (Advanced topics in AI/ML), Seminar on Emerging Technologies in AI/ML |
| Module | Topics |
|---|---|
| Introduction to AL & ML | History of AI, Basics of Machine Learning, Difference between AI, ML, and Deep Learning |
| Data Preprocessing | Data Cleaning, Data Transformation, Feature Scaling, Handling Missing Data |
| Supervised Learning | Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines |
| Unsupervised Learning | K-means Clustering, Hierarchical Clustering, PCA (Principal Component Analysis) |
| Neural Networks and Deep Learning | Basics of Neural Networks, Deep Learning Frameworks, Convolutional Neural Networks, Recurrent Neural Networks |
| Reinforcement Learning | Introduction to Reinforcement Learning, Q-Learning, Policy Gradient Methods |
| Natural Language Processing | Text Preprocessing, Word Embeddings, Sentiment Analysis, Language Models |
| AI Ethics and Future | AI in Society, Ethical Considerations, Future of AI and ML |
