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.