B.Tech CSE (AI & ML) Course Syllabus

The B.Tech in Computer Science Engineering (AI & ML) syllabus covers foundational computer science, machine learning, deep learning, data science, algorithms, software engineering, and applications of AI in real-world scenarios.

What Will You Study?

• Introduction to Programming
• Data Structures and Algorithms
• Database Management Systems
• Software Engineering
• Machine Learning
• Artificial Intelligence
• Deep Learning
• Computer Networks
• Operating Systems
• Web Technologies
• Mobile Application Development
• Data Science
• Natural Language Processing
• Cloud Computing
• Cybersecurity
• Software Testing
• Big Data Analytics
• Internet of Things (IoT)
• Human-Computer Interaction
• Project Work/Capstone Project

B.Tech CSE (AI & ML) Semester-wise Core Subjects:

Semester I

1. Introduction to Programming
2. Mathematics I
3. Engineering Physics
4. Engineering Chemistry
5. Professional Communication
6. Practical Lab

Semester II

1. Data Structures and Algorithms
2. Mathematics II
3. Computer Organization
4. Discrete Mathematics
5. Computer Graphics
6. Practical Lab

Semester III

1. Database Management Systems
2. Software Engineering
3. Machine Learning
4. Operating Systems
5. Elective 1
6. Practical Lab

Semester IV

1. Artificial Intelligence
2. Computer Networks
3. Web Technologies
4. Data Science
5. Elective 2
6. Practical Lab

Semester V

1. Deep Learning
2. Mobile Application Development
3. Cybersecurity
4. Internet of Things (IoT)
5. Elective 3
6. Practical Lab

Semester VI

1. Big Data Analytics
2. Natural Language Processing
3. Cloud Computing
4. Human-Computer Interaction
5. Elective 4
6. Practical Lab

Semester VII

1. Software Testing
2. Research Methodology
3. Project Work
4. Elective 5
5. Internship

Semester VIII

1. Capstone Project
2. Elective 6
3. Technical Writing
4. Industry Orientation
5. Practical Lab

B.Tech CSE (AI & ML) Projects

B.Tech CSE projects are integral to the curriculum, offering students practical exposure to the dynamic field of computer science. These projects allow students to apply theoretical knowledge to real-world challenges in areas such as artificial intelligence, machine learning, data science, and software development. By engaging in both team-based and individual projects, students enhance their analytical thinking, problem-solving skills, and technical proficiency, preparing them for successful careers in technology and innovation.

B.Tech CSE (AI & ML) Projects in Syllabus

B.Tech CSE (AI & ML) projects provide students with practical experience in applying theoretical concepts to real-world challenges in computer science and artificial intelligence. Here’s what you can expect:

1. AI Model Development: Design and implement machine learning models for tasks such as image recognition, natural language processing, or predictive analytics.

2. Machine Learning Algorithms: Develop algorithms to solve specific problems, such as classification, regression, or clustering, using popular libraries like TensorFlow or PyTorch.

3. Data Analysis Project: Conduct a project that involves collecting, processing, and analyzing large datasets to extract insights and trends.

4. Software Development Project: Create a software application addressing a particular need, focusing on user interface design, backend development, and database integration.

5. IoT Systems: Build an Internet of Things (IoT) system that collects data from sensors, processes it, and displays results on a web or mobile application.

6. Mobile App Development: Develop a mobile application using frameworks like Flutter or React Native, focusing on user experience and functionality.

7. Cybersecurity Project: Implement a project that addresses security challenges, such as developing a secure authentication system or a network security monitoring tool.

8. Robotics and Automation: Create a project involving robotics, such as programming a robot to perform specific tasks or integrating AI for autonomous decision-making.

9. Cloud Computing Solutions: Design a cloud-based application, focusing on scalability, reliability, and deployment on platforms like AWS or Azure.

10. Blockchain Application: Explore the development of a blockchain-based solution, emphasizing security and decentralization for applications like smart contracts or cryptocurrency.

Internships in B.Tech Programs

Internships in B.Tech programs are vital for connecting academic learning with practical industry experience. They offer students the opportunity to apply technical skills to real-world projects in fields such as software engineering, AI development, and data analytics. By collaborating with industry professionals, B.Tech students refine their coding, project management, and teamwork skills, gaining a competitive edge in the job market. Internships also help students establish valuable industry connections and clarify their career aspirations.

Why Internships Matter?

• Practical Exposure: Apply computer science theories to real-world projects in AI, machine learning, and software development.
• Skill Enhancement: Sharpen programming, analytical, and problem-solving skills relevant to the tech industry.
• Industry Mentorship: Learn directly from experienced professionals in the technology sector.
• Technical Problem Solving: Engage in resolving real-world technical challenges and improve decision-making capabilities.
• Networking Opportunities: Build professional relationships that can facilitate future career advancement.
• Career Clarity: Gain insights into different tech roles to make informed career decisions.
• Market Readiness: Prepare for post-B.Tech roles with hands-on experience in software development and AI applications.

B.Tech CSE (AI & ML) Research Opportunities

Research in Computer Science and Engineering, particularly in Artificial Intelligence and Machine Learning, is vital for advancing technology and addressing real-world challenges. Here are some key areas for research:

1. Machine Learning Algorithms: Explore the development of new algorithms for classification, regression, and clustering tasks, focusing on improving accuracy and efficiency.

2. Deep Learning: Investigate advanced neural network architectures for applications in image recognition, natural language processing, and generative models.

3. Computer Vision: Research techniques for image and video analysis, including object detection, facial recognition, and scene understanding.

4. Natural Language Processing (NLP): Focus on improving language models for tasks like sentiment analysis, machine translation, and text summarization.

5. Data Science and Analytics: Study methods for extracting insights from large datasets, including statistical analysis, data visualization, and big data technologies.

6. IoT and Smart Systems: Research the integration of AI and ML in Internet of Things (IoT) applications, focusing on data processing and intelligent decision-making.

7. Robotics and Autonomous Systems: Investigate the use of AI in robotics for navigation, control, and interaction with the environment.

8. Cybersecurity: Explore AI-driven solutions for enhancing cybersecurity measures, including anomaly detection and intrusion prevention systems.

9. Blockchain Technology: Research applications of AI in blockchain for improving security, transparency, and efficiency in transactions.

10. Human-Computer Interaction: Study ways to enhance user experience through intelligent interfaces, including voice recognition, gesture control, and augmented reality.

Frequently Asked Questions