| Attribute | Details |
|---|---|
| Course Duration | 3 Years |
| Course Level | Undergraduate |
| Course Tuition Fees | Varies by Institution |
| Mode of Study | Full-time/Part-time |
| Institute Type | Public/Private |
| Semester | Subjects |
|---|---|
| Semester 1 | Introduction to Programming, Mathematics for Computer Science, Basics of Artificial Intelligence, English for Communication |
| Semester 2 | Data Structures and Algorithms, Database Management Systems, Principles of Machine Learning, Statistics for Data Science |
| Semester 3 | Object-Oriented Programming, Linear Algebra for Computing, Neural Networks and Deep Learning, Web Technologies |
| Semester 4 | Operating Systems, Computer Networks, Advanced Machine Learning, Natural Language Processing |
| Semester 5 | Big Data Analytics, Cloud Computing for AI, Reinforcement Learning, Project Work I |
| Semester 6 | Computer Vision, Robotics and AI, Ethical and Social Issues in AI, Project Work II |
| Module | Topics |
|---|---|
| Introduction to Machine Learning | History of AI, Definitions, Types of Models and Algorithms, Applications |
| Data Preprocessing | Data Cleaning, Normalization, Transformation, Feature Selection and Extraction |
| Supervised Learning | Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines |
| Unsupervised Learning | K-Means Clustering, Hierarchical Clustering, Principal Component Analysis |
| Neural Networks and Deep Learning | Perceptrons, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks |
| Reinforcement Learning | Markov Decision Processes, Q-Learning, Policy Gradient Methods |
| Evaluation Metrics | Accuracy, Precision, Recall, F1 Score, Confusion Matrix |
| Advanced Topics | Natural Language Processing, Generative Adversarial Networks, Autoencoders |
