Computer Science and Engineering (AI & ML)
Program Overview: B.Tech CSE (AI & ML)
Duration: 4 Years (8 Semesters)
Total Credits: 160
Offered by: Department of Computer Science & Engineering (Artificial Intelligence (AI) and Machine Learning (ML)), School of Engineering, DSU
This program is designed to provide a strong foundation in core computer science principles while emphasizing Artificial Intelligence (AI) and Machine Learning (ML) — the most in-demand technologies driving Industry 4.0 and beyond.
Curriculum Structure
Semesters 1–2: Foundational Engineering and Programming Skills
- Core courses: Engineering Physics, Chemistry, Mathematics, Electrical & Mechanical Engineering, and English.
- Programming foundations: Object-Oriented Programming, C Programming for Problem Solving.
- Soft skill and technical training through Cognitive and Technical Skills I–II.
Semesters 3–4: Core Computing and AI Foundations
- Mathematical & Analytical base: Probability & Statistics, Transform and Numerical Techniques.
- Core CS subjects: Data Structures, Computer Networks, Database Management Systems, Design and Analysis of Algorithms, Theory of Computation, System Software.
- AI-oriented courses: Artificial Intelligence, AI for Sustainable development, Full Stack Development.
- Skill-based learning via Java Programming and Unix & Shell Programming.
Semesters 5–6: AI & ML Specialization
- Core AI/ML courses:
- Machine Learning
- Deep Learning
- GenAI and Prompt Engineering, Agentic AI
- Natural Language Models
- Image Processing & Computer Vision
- MLOps for Enterprises
- Skill Enhancement Courses like Cloud Computing (AWS cloud Platform).
- Choice-based Professional Electives (PECs) and Open Electives (OECs) across AI domains.
Semesters 7–8: Research, Projects & Industry Integration
- Capstone Project (Phase I & II): Long-term applied research or product development.
- Internship: 6-week industry internship.
- Professional Electives: Advanced domain electives, including Explainable AI, Quantum ML, Robotics, FinTech, Blockchain, and AI Ethics.
- Encourages innovation, entrepreneurship, and real-world application.
Elective Domains
| Domain | Focus Area | Sample Electives |
| AI & Language Perception | Core AI, NLP | Optimization Techniques, Explainable AI, Quantum ML, AI Ethics |
| Robotics & Automation | Robotics, RL | Fundamentals of Robotics, Reinforcement Learning, ROS, Industry 5.0 |
| Architecture & Security | IoT, Cybersecurity | IoT, Cryptography, GPU Architecture, Blockchain |
| Data Analytics | Data Science, FinTech | Data Science & Analytics, Predictive Analytics, Big Data Analytics |
Open Electives: Industrial Robotics, ML for Healthcare, Responsible AI & Ethics.
Unique Features
- Integrated Professional Core Courses (IPCC): Combine theory + lab for hands-on learning.
- Skill Enhancement Courses (SEC): Industry-oriented skill training each semester.
- Cognitive & Technical Skills (CTS): Continuous employability and aptitude training.
- Capstone Projects & Internships: Foster innovation and real-world problem-solving.
- GenAI & Prompt Engineering: Modern AI skillset aligned with emerging technologies.
Career Opportunities
Graduates can pursue roles in tech-driven industries, startups, or research organizations such as Google, Microsoft, Amazon, IBM, TCS, Infosys, and AI startups.
Top Job Roles:
- AI Engineer / ML Engineer
- Data Scientist / Data Analyst
- Computer Vision Engineer
- NLP Engineer / Prompt Engineer
- Deep Learning Specialist
- MLOps Engineer
- AI Research Associate
- Cloud & AI Integration Specialist
- AI Product Developer / Innovator
Sectors Hiring AI & ML Graduates:
- Information Technology & Software Services
- Healthcare and Bioinformatics
- Finance and FinTech
- Robotics and Automation
- Smart Manufacturing (Industry 4.0)
- Cybersecurity and Blockchain
- Data Centers and Cloud Infrastructure
- Research Labs and Academia
Program Outcomes
By the end of this program, students will:
- Master AI/ML algorithms, data analytics, and system design.
- Gain expertise in GenAI, Agentic AI, Deep Learning, NLP, and MLOps.
- Apply AI solutions ethically across real-world domains.
- Be industry-ready with strong coding, analytical, and problem-solving skills.
- Build innovation-driven solutions through projects and internships.
