Curriculum Overview
The Artificial Intelligence program at Alard University Pune follows a rigorous, semester-wise curriculum designed to progressively build knowledge and practical skills. The program spans 8 semesters, with each semester carrying a specific credit structure and focus area.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | CS101 | Introduction to Programming | 3-0-2-4 | None |
| I | MATH101 | Calculus and Analytical Geometry | 4-0-0-4 | None |
| I | PHYS101 | Physics for Engineers | 3-0-2-4 | None |
| I | CHEM101 | Chemistry for Engineers | 3-0-2-4 | None |
| I | ENG101 | English Communication Skills | 2-0-0-2 | None |
| I | EE101 | Basic Electrical Engineering | 3-0-2-4 | None |
| II | CS201 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| II | MATH201 | Linear Algebra and Differential Equations | 4-0-0-4 | MATH101 |
| II | PHYS201 | Modern Physics | 3-0-2-4 | PHYS101 |
| II | CHEM201 | Organic Chemistry | 3-0-2-4 | CHEM101 |
| II | CS202 | Object-Oriented Programming | 3-0-2-4 | CS101 |
| II | ENG201 | Technical Writing and Presentation | 2-0-0-2 | ENG101 |
| III | CS301 | Database Management Systems | 3-0-2-4 | CS201 |
| III | MATH301 | Probability and Statistics | 4-0-0-4 | MATH201 |
| III | CS302 | Computer Architecture | 3-0-2-4 | EE101 |
| III | PHYS301 | Quantum Mechanics | 3-0-2-4 | PHYS201 |
| III | CS303 | Operating Systems | 3-0-2-4 | CS202 |
| IV | CS401 | Machine Learning Fundamentals | 3-0-2-4 | MATH301, CS301 |
| IV | CS402 | Artificial Neural Networks | 3-0-2-4 | CS401 |
| IV | CS403 | Data Mining and Big Data Analytics | 3-0-2-4 | MATH301, CS301 |
| IV | CS404 | Natural Language Processing | 3-0-2-4 | CS401 |
| IV | CS405 | Computer Vision | 3-0-2-4 | CS401 |
| V | CS501 | Advanced Machine Learning | 3-0-2-4 | CS401 |
| V | CS502 | Reinforcement Learning | 3-0-2-4 | CS401 |
| V | CS503 | Deep Reinforcement Learning | 3-0-2-4 | CS502 |
| V | CS504 | AI Ethics and Responsible AI | 3-0-2-4 | CS401 |
| V | CS505 | Research Methodology in AI | 3-0-2-4 | CS401 |
| VI | CS601 | Special Topics in AI | 3-0-2-4 | CS501 |
| VI | CS602 | AI for Healthcare Applications | 3-0-2-4 | CS501 |
| VI | CS603 | Autonomous Systems and Robotics | 3-0-2-4 | CS501 |
| VI | CS604 | Cybersecurity in AI Systems | 3-0-2-4 | CS501 |
| VI | CS605 | Human-AI Interaction Design | 3-0-2-4 | CS501 |
| VII | CS701 | Mini Project I | 2-0-0-2 | CS601 |
| VIII | CS801 | Final Year Thesis/Capstone Project | 4-0-0-4 | CS701 |
Advanced Departmental Electives
The following are advanced departmental elective courses that offer in-depth exploration of specialized topics within the AI domain:
- Computer Vision and Image Processing: This course explores fundamental concepts in image processing, feature extraction, object detection, and segmentation using deep learning techniques. Students learn to implement real-time computer vision systems for applications like surveillance, medical imaging, and autonomous vehicles.
- Natural Language Understanding and Generation: Focuses on building systems that can understand, generate, and interact with human language naturally. Topics include transformer architectures, language modeling, text summarization, question answering systems, and dialog management.
- Reinforcement Learning Algorithms: Covers theoretical foundations and practical implementations of reinforcement learning algorithms such as Q-learning, policy gradients, actor-critic methods, and deep deterministic policy gradients (DDPG). Students implement agents in simulated environments and real-world scenarios.
- Deep Learning for Signal Processing: Explores the application of neural networks to audio, video, and biomedical signals. Students learn to design architectures for speech recognition, emotion detection, ECG analysis, and other signal processing tasks.
- AI in Healthcare Technologies: Addresses challenges in developing AI solutions for healthcare delivery. Topics include medical imaging diagnostics, drug discovery, personalized medicine, telehealth systems, and clinical decision support tools.
- Robotic Systems and Control: Combines principles of robotics with control theory to build autonomous robotic systems. Students learn about kinematics, dynamics, sensor fusion, navigation algorithms, and machine learning for robot control.
- Adversarial Machine Learning: Examines vulnerabilities in AI models and strategies for defending against adversarial attacks. Includes discussions on robustness, privacy-preserving machine learning, and secure deployment of AI systems.
- Explainable Artificial Intelligence (XAI): Focuses on making AI decisions interpretable and transparent. Students learn techniques such as attention visualization, LIME, SHAP, and other methods to explain complex model outputs to stakeholders.
- AI for Sustainable Development: Explores how artificial intelligence can be applied to address global challenges such as climate change, food security, water management, and energy optimization. Students work on projects aligned with the United Nations Sustainable Development Goals (SDGs).
- Cognitive Architectures and Human-Machine Interaction: Investigates how AI systems can mimic human cognitive processes and facilitate seamless interaction between humans and machines. Topics include memory models, decision-making frameworks, and user experience design for intelligent interfaces.
Project-Based Learning Philosophy
The program strongly emphasizes project-based learning as a cornerstone of educational excellence. Students engage in both mandatory mini-projects and a comprehensive final-year thesis/capstone project that bridges theory with real-world applications.
Mini Projects: In the seventh semester, students undertake a two-credit mini-project under faculty supervision. These projects typically last for 4-6 weeks and involve working on a specific AI challenge using existing datasets or developing novel algorithms. Students present their findings in a formal report and oral defense session.
Final Year Thesis/Capstone Project: The capstone project in the eighth semester is a significant research endeavor that spans 12-16 weeks. Students select a topic aligned with their interests and career goals, collaborate closely with faculty mentors, and produce a high-quality research paper or product demonstration. This project often leads to publication opportunities, patent applications, or startup ventures.
The evaluation criteria for these projects include technical depth, innovation, documentation quality, presentation skills, and peer feedback. Faculty members from diverse backgrounds guide students through the process, ensuring that each project meets industry standards and academic rigor.