Curriculum Overview
The curriculum of the Artificial Intelligence and Machine Learning program at KUDSIT is meticulously designed to provide students with a balanced mix of foundational knowledge, advanced theoretical understanding, and practical implementation skills. The program spans eight semesters over four academic years, each structured to progressively build upon previous learning.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | CS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | CS102 | Mathematics for Computer Science | 3-0-0-3 | - |
| 1 | PH101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CH101 | Chemistry for Engineering | 3-0-0-3 | - |
| 1 | EE101 | Basic Electrical Engineering | 3-0-0-3 | - |
| 1 | HS101 | English Communication Skills | 2-0-0-2 | - |
| 2 | CS201 | Data Structures and Algorithms | 3-0-0-3 | CS101 |
| 2 | CS202 | Digital Logic and Computer Organization | 3-0-0-3 | - |
| 2 | MA201 | Probability and Statistics | 3-0-0-3 | CS102 |
| 2 | PH201 | Electromagnetic Waves and Optics | 3-0-0-3 | PH101 |
| 2 | CH201 | Organic Chemistry | 3-0-0-3 | CH101 |
| 2 | HS201 | Critical Thinking and Problem Solving | 2-0-0-2 | - |
| 3 | CS301 | Database Systems | 3-0-0-3 | CS201 |
| 3 | CS302 | Operating Systems | 3-0-0-3 | CS201 |
| 3 | CS303 | Machine Learning Fundamentals | 3-0-0-3 | MA201, CS201 |
| 3 | CS304 | Linear Algebra for AI | 3-0-0-3 | CS102 |
| 3 | PH301 | Quantum Physics and Applications | 3-0-0-3 | PH101 |
| 3 | CH301 | Physical Chemistry | 3-0-0-3 | CH201 |
| 4 | CS401 | Advanced Machine Learning | 3-0-0-3 | CS303 |
| 4 | CS402 | Neural Networks and Deep Learning | 3-0-0-3 | CS303 |
| 4 | CS403 | Natural Language Processing | 3-0-0-3 | CS303 |
| 4 | CS404 | Computer Vision | 3-0-0-3 | CS303 |
| 4 | CS405 | Reinforcement Learning | 3-0-0-3 | CS303 |
| 4 | MA401 | Optimization Techniques | 3-0-0-3 | MA201 |
| 5 | CS501 | AI in Healthcare Applications | 3-0-0-3 | CS401, CS402 |
| 5 | CS502 | Robotics and Autonomous Systems | 3-0-0-3 | CS401, CS402 |
| 5 | CS503 | AI Ethics and Fairness | 3-0-0-3 | CS401 |
| 5 | CS504 | Computational Biology | 3-0-0-3 | CS401, CS402 |
| 5 | CS505 | Financial Engineering | 3-0-0-3 | CS401 |
| 5 | MA501 | Stochastic Processes | 3-0-0-3 | MA201 |
| 6 | CS601 | Industry Project Development | 3-0-0-3 | CS501, CS502 |
| 6 | CS602 | Capstone Research Project | 3-0-0-3 | CS501, CS502 |
| 6 | CS603 | Advanced Topics in AI | 3-0-0-3 | CS501, CS502 |
| 6 | CS604 | Internship Preparation | 3-0-0-3 | - |
| 6 | CS605 | Research Methodology | 3-0-0-3 | CS501, CS502 |
| 6 | MA601 | Advanced Optimization | 3-0-0-3 | MA401 |
| 7 | CS701 | Specialized Elective I | 3-0-0-3 | CS601 |
| 7 | CS702 | Specialized Elective II | 3-0-0-3 | CS601 |
| 7 | CS703 | Specialized Elective III | 3-0-0-3 | CS601 |
| 7 | CS704 | Final Year Project | 3-0-0-3 | CS601, CS602 |
| 7 | CS705 | Professional Ethics and Leadership | 3-0-0-3 | - |
| 8 | CS801 | Advanced Capstone Project | 3-0-0-3 | CS704 |
| 8 | CS802 | Research Thesis | 3-0-0-3 | CS704 |
| 8 | CS803 | Industry Internship | 3-0-0-3 | - |
| 8 | CS804 | Graduation Seminar | 3-0-0-3 | - |
| 8 | CS805 | Career Planning and Placement Preparation | 3-0-0-3 | - |
Advanced Departmental Elective Courses:
Neural Networks and Deep Learning
This course provides a comprehensive overview of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will implement models using frameworks like TensorFlow and PyTorch and gain hands-on experience with real-world datasets.
Reinforcement Learning
Reinforcement learning is a branch of machine learning concerned with how intelligent agents ought to take actions in an environment to maximize cumulative reward. This course covers theoretical foundations, algorithms like Q-learning and policy gradients, and practical applications in robotics, game-playing, and autonomous systems.
Natural Language Processing
NLP enables computers to understand, interpret, and generate human language. Topics include tokenization, sentiment analysis, named entity recognition, machine translation, and dialogue systems. Students will work with large-scale language models and apply them to real-world tasks.
Computer Vision
Computer vision involves enabling machines to interpret and understand visual information from the world. This course covers image processing techniques, object detection, segmentation, and recognition methods using deep learning approaches like CNNs and U-Net architectures.
Machine Learning for Healthcare
This elective explores how AI can be applied to healthcare diagnostics, drug discovery, genomics, and personalized medicine. Students will work with medical datasets and develop predictive models for diagnosing diseases or predicting patient outcomes.
AI Ethics and Fairness
As AI systems become more pervasive, ethical considerations are paramount. This course examines bias in machine learning, fairness metrics, transparency, and accountability. Students will learn to design ethically sound AI systems and evaluate existing ones for potential harm.
Financial Engineering
This course bridges finance and AI by teaching students how to apply machine learning techniques to financial markets, risk assessment, portfolio optimization, and algorithmic trading. Students will build predictive models using real financial data.
Computational Biology
Integrating biology with computational methods, this course focuses on applying AI to genomics, proteomics, and drug discovery. Students will learn bioinformatics tools and use machine learning to analyze biological sequences and structures.
Robotics and Autonomous Systems
This elective introduces students to robotics engineering and autonomous navigation systems. Topics include sensor fusion, path planning, SLAM algorithms, and control systems for robotic platforms. Practical projects involve building and programming robots for various tasks.
Advanced Optimization Techniques
Optimization is central to many AI applications. This course covers linear and nonlinear optimization methods, convex optimization, gradient descent variants, and evolutionary algorithms. Students will apply these techniques to solve complex AI problems.
Quantitative Finance with AI
This advanced course explores how machine learning models can be used in quantitative finance for risk management, derivatives pricing, and market prediction. Students will use Python libraries like QuantLib and scikit-learn to model financial instruments.
Generative Models
Generative models create new data samples similar to existing datasets. This course covers variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models. Students will experiment with creating realistic images, text, and audio using these methods.
Time Series Forecasting
This course focuses on analyzing temporal data for forecasting future trends. Students will learn ARIMA, LSTM-based forecasting, and seasonal decomposition techniques to predict economic indicators, stock prices, weather patterns, etc.
Explainable AI (XAI)
As AI systems become more complex, interpretability becomes crucial. This course covers techniques for explaining black-box models, including LIME, SHAP, attention mechanisms, and visualization methods. Students will evaluate the explainability of their own models.
Edge Computing and Mobile AI
This elective addresses deploying AI models on resource-constrained devices like smartphones and IoT sensors. Topics include model compression, quantization, and mobile-first design principles for AI applications.
Human-Computer Interaction with AI
Designing systems that integrate AI seamlessly into user experiences requires understanding both human behavior and technological capabilities. This course covers interaction design, user research, and the development of intelligent interfaces.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is grounded in the belief that students learn best when they are actively engaged in solving real-world problems. Projects serve as vehicles for integrating theoretical knowledge with practical implementation.
The structure of project-based learning begins with mini-projects in the second year, where students work in small teams to apply concepts learned in core courses. These projects are typically 2-3 months long and involve working with actual datasets or simulation environments.
As students progress into the third year, they engage in more substantial projects that often require collaboration with industry partners or faculty research groups. These projects span 4-6 months and culminate in presentations to peers, faculty, and external stakeholders.
The final-year capstone project is the most significant component of the program's experiential learning approach. Students select a topic aligned with their interests and career goals, often involving original research or innovative applications of AI techniques. The process includes proposal development, literature review, experimentation, analysis, and documentation.
Faculty mentors guide students throughout the project lifecycle, providing feedback on methodology, technical execution, and presentation skills. Evaluation criteria include the novelty of the approach, quality of results, clarity of documentation, and effectiveness of communication.
Students have the flexibility to choose projects based on their preferences or be assigned topics that align with ongoing faculty research initiatives. This dual approach ensures both personal interest and academic rigor in project selection.