Course Structure Overview
The Bachelor of Artificial Intelligence program at Technocrats Institute of Technology Computer Science and Engineering is structured over eight semesters, providing a comprehensive and progressive learning experience that builds foundational knowledge before advancing into specialized AI disciplines. Each semester integrates core courses, departmental electives, science electives, and laboratory components designed to foster both theoretical understanding and practical application.
First Year
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester I | CS101 | Programming Fundamentals | 3-0-2-4 | None |
| Semester I | CS102 | Data Structures and Algorithms | 3-0-2-4 | CS101 |
| Semester I | MATH101 | Calculus and Analytical Geometry | 4-0-0-4 | None |
| Semester I | MATH102 | Linear Algebra and Vector Calculus | 4-0-0-4 | None |
| Semester I | PHYS101 | Physics for Computer Science | 3-0-0-3 | None |
| Semester I | ENGG101 | Engineering Graphics and Design | 2-0-0-2 | None |
| Semester I | COMM101 | Communication Skills | 2-0-0-2 | None |
| Semester II | CS201 | Object-Oriented Programming with Java | 3-0-2-4 | CS101 |
| Semester II | CS202 | Databases and SQL | 3-0-2-4 | CS101 |
| Semester II | MATH201 | Differential Equations and Probability | 4-0-0-4 | MATH101 |
| Semester II | MATH202 | Statistics and Numerical Methods | 4-0-0-4 | MATH101 |
| Semester II | PHYS201 | Modern Physics and Electronics | 3-0-0-3 | PHYS101 |
| Semester II | ENGG201 | Electrical Circuits and Networks | 3-0-0-3 | PHYS101 |
| Semester II | ENGG202 | Engineering Ethics and Professional Practice | 2-0-0-2 | None |
Second Year
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester III | CS301 | Operating Systems | 3-0-2-4 | CS201, CS202 |
| Semester III | CS302 | Computer Networks | 3-0-2-4 | ENGG201, CS202 |
| Semester III | CS303 | Software Engineering | 3-0-2-4 | CS201, CS202 |
| Semester III | CS304 | Machine Learning Fundamentals | 3-0-2-4 | MATH201, MATH202 |
| Semester III | CS305 | Artificial Intelligence Concepts | 3-0-2-4 | MATH201, CS201 |
| Semester III | CS306 | Research Methodology and Ethics | 2-0-0-2 | ENGG202 |
| Semester IV | CS401 | Deep Learning and Neural Networks | 3-0-2-4 | CS304, CS305 |
| Semester IV | CS402 | Data Mining and Big Data Analytics | 3-0-2-4 | CS304, CS305 |
| Semester IV | CS403 | Computer Vision and Image Processing | 3-0-2-4 | CS305, MATH201 |
| Semester IV | CS404 | Natural Language Processing | 3-0-2-4 | CS305, MATH201 |
| Semester IV | CS405 | Reinforcement Learning | 3-0-2-4 | CS304, CS305 |
| Semester IV | CS406 | Project Management and Innovation | 2-0-0-2 | ENGG202 |
Third Year
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester V | CS501 | Advanced Machine Learning | 3-0-2-4 | CS401, CS402 |
| Semester V | CS502 | Robotics and Automation | 3-0-2-4 | CS301, CS403 |
| Semester V | CS503 | Knowledge Representation and Reasoning | 3-0-2-4 | CS305, CS405 |
| Semester V | CS504 | AI Ethics and Governance | 2-0-0-2 | CS305 |
| Semester V | CS505 | Human-Computer Interaction with AI | 3-0-2-4 | CS305, CS404 |
| Semester V | CS506 | Explainable AI (XAI) | 3-0-2-4 | CS401, CS405 |
| Semester VI | CS601 | Research and Development in AI | 3-0-2-4 | CS501, CS502 |
| Semester VI | CS602 | Neural Architecture Search and AutoML | 3-0-2-4 | CS501, CS401 |
| Semester VI | CS603 | AI in Healthcare and Biomedical Applications | 3-0-2-4 | CS501, CS503 |
| Semester VI | CS604 | AI in Smart Cities and IoT | 3-0-2-4 | CS501, CS502 |
| Semester VI | CS605 | Capstone Project Preparation | 2-0-0-2 | CS501, CS503 |
Fourth Year
| Semester | Course Code | Course Title | Credit Structure (L-T-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| Semester VII | CS701 | Capstone Project I | 4-0-2-6 | CS605 |
| Semester VII | CS702 | Advanced Topics in AI | 3-0-2-4 | CS501, CS601 |
| Semester VII | CS703 | Internship Preparation and Training | 2-0-0-2 | None |
| Semester VIII | CS801 | Capstone Project II | 4-0-2-6 | CS701 |
| Semester VIII | CS802 | Entrepreneurship and Innovation in AI | 2-0-0-2 | CS701 |
Advanced Departmental Elective Courses
The program includes several advanced departmental electives that provide students with specialized knowledge and skills relevant to emerging trends in AI. These courses are designed to deepen understanding, foster innovation, and prepare students for real-world applications.
Advanced Machine Learning (CS501)
This course explores advanced topics in machine learning such as ensemble methods, clustering algorithms, dimensionality reduction techniques, and deep learning architectures. Students will learn to design, train, and evaluate complex models using frameworks like TensorFlow and PyTorch. The course emphasizes practical implementation through hands-on lab sessions and project-based assignments.
Robotics and Automation (CS502)
Students are introduced to robotics systems, control mechanisms, sensor integration, and automation technologies. Through theoretical lectures and laboratory experiments, they gain hands-on experience with robot operating systems (ROS), microcontrollers, and embedded programming. The course culminates in a project involving the development of an autonomous robot capable of performing specific tasks.
Knowledge Representation and Reasoning (CS503)
This course covers formalisms for representing knowledge and reasoning about uncertain or incomplete information. Topics include propositional logic, first-order predicate logic, Bayesian networks, semantic web technologies, and expert systems. Students develop applications using tools like Prolog and Python-based knowledge representation libraries.
AI Ethics and Governance (CS504)
This course addresses ethical challenges in AI development and deployment, including bias mitigation, fairness, transparency, accountability, and regulatory compliance. Through case studies and group discussions, students learn to apply ethical principles in real-world scenarios. The course also explores governance frameworks and policy implications of AI technologies.
Human-Computer Interaction with AI (CS505)
This course focuses on designing interfaces that leverage AI capabilities to enhance user experiences. Students study user-centered design principles, interaction design patterns, accessibility standards, and usability testing methods. Projects involve creating interactive systems using AI-driven features such as voice recognition, gesture control, and personalized recommendations.
Explainable AI (XAI) (CS506)
This course introduces techniques for making AI models interpretable and trustworthy. Students learn about SHAP, LIME, attention visualization, and other explainability methods. Through practical exercises, they develop models that provide insights into decision-making processes, enhancing transparency in critical applications like healthcare diagnostics and financial risk assessment.
Research and Development in AI (CS601)
This course provides an overview of current research trends in AI, including theoretical foundations and experimental methodologies. Students engage with literature reviews, propose research ideas, and participate in workshops led by faculty and industry experts. The course prepares students for advanced research or thesis work.
Neural Architecture Search and AutoML (CS602)
This elective explores automated machine learning techniques, including neural architecture search (NAS), hyperparameter optimization, and automated feature engineering. Students use NAS tools like AutoGLM and NAS-Bench to design efficient models and evaluate performance across datasets.
AI in Healthcare and Biomedical Applications (CS603)
This course examines how AI is transforming healthcare through diagnostics, drug discovery, genomics, and personalized medicine. Students work on projects involving medical imaging, patient data analysis, and clinical decision support systems using real-world datasets.
AI in Smart Cities and IoT (CS604)
This course explores the integration of AI in urban infrastructure, smart transportation, environmental monitoring, and public services. Students study IoT platforms, edge computing, sensor networks, and data analytics for city planning and resource management.
Project-Based Learning Philosophy
The Bachelor of Artificial Intelligence program places significant emphasis on project-based learning as a core pedagogical strategy. This approach encourages students to apply theoretical concepts in practical settings, fostering creativity, collaboration, and problem-solving skills essential for professional success.
Mini-projects are integrated throughout the curriculum, starting from the second year. These projects allow students to explore topics of personal interest, work in teams, and receive guidance from faculty mentors. Projects typically span 2-3 months and involve phases such as problem identification, literature review, design, implementation, testing, and documentation.
The final-year capstone project is a major undertaking that requires students to tackle a complex, real-world problem using AI methodologies. Students select their projects in consultation with faculty mentors based on their interests and career goals. The project involves extensive research, model development, experimentation, and presentation of findings.
Assessment criteria for both mini-projects and capstone projects include technical proficiency, innovation, teamwork, communication, and adherence to deadlines. Regular milestones and progress reports ensure continuous evaluation and feedback throughout the project lifecycle.