B. Tech CSE in
Artificial Intelligence & Machine Learning
Other Specialization:
Overview
A Bachelor of Technology (B. Tech) in Artificial Intelligence & Machine Learning (AI & ML) is an undergraduate program designed to provide students with a strong foundation in the principles and practices of AI and ML. This program typically spans four years and combines theoretical knowledge with practical applications to prepare students for careers in technology, research, and related fields.
With state-of-the-art infrastructure, we support a dynamic academic environment conducive to research and extracurricular activities. Our faculty members, possessing extensive qualifications and experience, are dedicated to fostering the holistic development of our students.
Overall, a B. Tech in Artificial Intelligence & Machine Learning provides a comprehensive education that equips students with the skills and knowledge necessary to thrive in the rapidly evolving field of AI and ML.
Scope of B.Tech in AI & Machine Learning
- The market for AI technologies is vast, amounting to around 200 billion U.S. dollars in 2023 and is expected to grow well beyond that to over 1.8 trillion U.S. dollars by 2030.
- By 2030, AI is expected to master natural language processing, enabling more nuanced and context-aware interactions. Conversations with AI will become indistinguishable from human conversations, revolutionizing customer service and virtual assistants.
B.Tech CSE (AI+ML) Course Details
Our B.Tech program in Artificial Intelligence and Machine Learning (AI & ML) combines theoretical and practical knowledge, offering a curriculum that covers programming, data structures, machine learning, deep learning, natural language processing, and big data analytics. The program provides a strong foundation in mathematics and statistics, along with specialized AI & ML topics.
The following table outlines the comprehensive course structure of the B.Tech AI & ML program. It is organized into core, elective, interdisciplinary, and skill enhancement components. Each category specifies the type of courses, their descriptions, and the corresponding credits, ensuring a well-rounded academic experience with a balance of foundational knowledge, research exposure, and professional ethics:
| Category | Short Name | Description | Credits |
|---|---|---|---|
| Departmental Core | DC | Discipline Specific Core Courses (DSC) | 59 |
| Departmental Core | DC | Project / Dissertation / Field Study / Survey | 13 |
| Departmental Core | DC | Seminar | 3 |
| Departmental Core | DC | Internship / on Job Experience | 0 |
| Departmental Core | DC | Research Credit Course | 0 |
| Departmental Elective | DE | Discipline Specific Elective Courses(DSE) | 31 |
| Program Linked Core | PLC | Interdeciplinary | 29 |
| Generic Elective | GE | Open courses/Generic Elective (GE) | 18 |
| University Core | UC | Ability Enhancement Courses (AEC) | 4 |
| University Core | UC | Skill Enhancement Courses (SEC) | 8 |
| Value Added Courses common for all UG (VAC) – Non Graded | |||
| University Core | UC | NSS/NCC | 2 |
| University Core | UC | Envionmental Science | 2 |
| University Core | UC | Human Values and Professional Ethics | 2 |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | MAL510 | Mathematics-1 | 3 | 1 | 0 | 4 | PLC |
| 2 | CSL521 | Introduction to Artificial Intelligence and Data Science | 3 | 0 | 2 | 4 | DC |
| 3 | CSL502 | Internet technology | 3 | 0 | 2 | 4 | DC |
| 4 | PHL507 | Field & Waves | 3 | 0 | 2 | 4 | PLC |
| 5 | CSL501 | Introduction to C programming | 3 | 0 | 2 | 4 | DC |
| 6 | AML501 | Engineering Mechanics | 3 | 1 | 0 | 4 | PLC |
| 7 | University Core-1 | 2 | 0 | 2 | 3 | UC |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSL605 | Introduction to Python | 3 | 0 | 2 | 4 | DC |
| 2 | EEL501 | Fundamental of Electrical Engineering | 3 | 0 | 2 | 4 | PLC |
| 3 | MAL520 | Mathematics-II | 3 | 1 | 0 | 4 | PLC |
| 4 | CSL503 | Web development | 3 | 0 | 2 | 4 | DC |
| 5 | MEL501 | Manufacturing Practice | 3 | 0 | 2 | 4 | PLC |
| 6 | MEL502 | Graphics Science | 0 | 0 | 2 | 1 | PLC |
| 7 | University Core-2 | 2 | 0 | 2 | 3 | UC |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSL602 | Data Structures | 3 | 0 | 2 | 4 | DC |
| 2 | Departmental Elective-1 | 2 | 0 | 2 | 3 | DE | |
| 3 | CSL604 | Discrete Mathematical Structure | 3 | 1 | 0 | 4 | PLC |
| 4 | CSL605 | Programming in C++ | 3 | 0 | 2 | 4 | DC |
| 5 | University Core-3 | 2 | 0 | 2 | 3 | UC |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSL 621 | Introduction to Database Systems | 3 | 0 | 2 | 4 | DC |
| 2 | CSL622 | Operating Systems | 3 | 0 | 2 | 4 | DC |
| 3 | CSL623 | Software Engineering | 3 | 1 | 0 | 4 | DC |
| 4 | Departmental Elective-2 | 3 | 0 | 2 | 4 | DE | |
| 5 | University Core-4 | 3 | 1 | 0 | 4 | UC | |
| 6 | Departmental Elective-3 | 3 | 0 | 2 | 4 | DE |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSL701 | Theory of Computation | 3 | 1 | 0 | 4 | DC |
| 2 | CSL702 | Analysis and Design of Algorithms | 3 | 0 | 2 | 4 | DC |
| 3 | CSL703 | Computer Architecture | 3 | 0 | 0 | 3 | DC |
| 4 | Departmental Elective-4 | 3 | 0 | 2 | 4 | DE | |
| 5 | Departmental Elective-5 | 3 | 0 | 2 | 4 | DE |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSL721 | Compiler Design | 3 | 1 | 0 | 4 | DC |
| 2 | CSL722 | Computer Networks | 3 | 0 | 2 | 4 | DC |
| 3 | Departmental Elective-6 | 3 | 0 | 0 | 3 | DE | |
| 4 | Departmental Elective-7 | 2 | 0 | 2 | 3 | DE |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | Departmental Elective-8 | 2 | 0 | 2 | 3 | DE | |
| 2 | Departmental Elective-9 | 2 | 0 | 2 | 3 | DE | |
| 3 | CSD812 | Major Project Part-1 (CS) | 0 | 0 | 10 | 5 | DC |
| 4 | CST810 | Practical Training | 0 | 0 | 0 | 0 | DC |
| 5 | CSC811 | Colloquium (CS) | 0 | 3 | 0 | 3 | DC |
| S.No. | Course Code | Course | L | T | P | Total Credit | Category |
|---|---|---|---|---|---|---|---|
| 1 | CSD813 | Major Project Part-2 (CS) | 0 | 0 | 16 | 8 | DC |
| Course Code | Course | L | T | P | Credit |
|---|---|---|---|---|---|
| EGL505 | Communication Skill-1 | 2 | 0 | 2 | 3 |
| EGL555 | Communication Skill-2 | 2 | 0 | 2 | 3 |
| EGL— | University Core | 2 | 0 | 2 | 3 |
| EGL— | University Core | 3 | 1 | 0 | 4 |
| Course Code | Course | L | T | P | Credit |
|---|---|---|---|---|---|
| CSL603 | Internet of Things | 2 | 0 | 2 | 3 |
| CSL606 | Quantum Computing | 3 | 0 | 0 | 3 |
| CSL624 | Programming in Java | 3 | 0 | 2 | 4 |
| CSL626 | Robotics and Automation | 2 | 0 | 2 | 3 |
| CSL628 | Data Analysis using Python, Numpy, Pandas, Matplotlib, and Seaborn | 3 | 0 | 2 | 4 |
| CSL629 | Advance Python | 3 | 0 | 2 | 4 |
| CSL708 | Probabilistic Modelling and Reasoning with Python | 3 | 0 | 2 | 4 |
| CSL709 | Mathematics for Data Science | 3 | 1 | 0 | 4 |
| CSL710 | R Programming for Data Science and Data Analysis | 3 | 0 | 2 | 4 |
| CSL711 | Introduction to Recommendation system | 3 | 0 | 2 | 4 |
| CSL724 | Introduction to cloud computing | 3 | 0 | 0 | 3 |
| CSL729 | Machine Learning and Pattern Recognition | 2 | 0 | 2 | 3 |
| CSL730 | Big Data Analysis | 2 | 0 | 2 | 3 |
| CSL731 | Neural Networks & Deep Learning | 2 | 0 | 2 | 3 |
| CSL732 | Natural Language Processing | 2 | 0 | 2 | 3 |
| CSL805 | Data Science- Tools and Techniques | 2 | 0 | 2 | 3 |
| CSL806 | Introduction to Data bricks and pyspark | 2 | 0 | 2 | 3 |
| CSL807 | Data Visualization using Power BI | 2 | 0 | 2 | 3 |
| CSL808 | Data Visualization using Saleforce | 2 | 0 | 2 | 3 |
Department
at a Glance
- Mission and Vision
- Faculty
- Facilities and Resources
- Curriculum Overview
- Student Opportunities
- Career Services
- Research and Development
- Community and Outreach
Exclusive
Labs
- Artificial Intelligence & Machine Learning Lab
- Internet of Things (IoT) Lab
- Full Stack Development Lab
- Cloud Computing Lab
- Android Development Lab
- Open Source Technologies Lab
Your Department in a Nutshell
- Achievements
- Patents
- Research
- Expert Sessions
- Activities
- Labs
Admission Criteria
- Through Eligibility & Scholarship Test (CPU-EST). For details, Click Here
- Rolling Admission: We follow rolling admission process. To apply Click Here
Fee Details
Course Fee | |
|---|---|
Admission Fee (one time) | 5000/- (at the time of admission) |
Tuition Fee | 65000/- Per Semester |
Examination Fee | 3000/- Per Semester |
Development Fee | 8000/- Per Semester |
Caution Money (one time) | 3000/- (Refundable) |
Scholarship Criteria
Scholarship For Session 2025-26
| Till 30th June | Till 15th July | Till 31st July | After 31st July | |
|---|---|---|---|---|
| Early Admission Benefit (One Time) [INR] | 5000 | 3000 | 2000 | No EAB |
Merit Scholarship
| % in 12th Board (PCM) | Scholarship on Tuition Fees | |||
|---|---|---|---|---|
| Till 30th June | Till 15th July | Till 31st July | After 31st July | |
| Above 95% | 50% | 45% | 40% | NA |
| 90% – 94.99% | 40% | 35% | 30% | NA |
| 85% – 89.99% | 30% | 25% | 20% | NA |
| 80% – 84.99% | 20% | 15% | 10% | NA |
| 70% – 79.99% | 15% | 10% | 5% | NA |
| 60% – 69.99% | 10% | 5% | NA | NA |
| %tile in JEE Main | Scholarship on Tuition Fees | |||
|---|---|---|---|---|
| Till 30th June | Till 15th July | Till 31st July | After 31st July | |
| Above 98.0 | 60% | 60% | 50% | 40% |
| 95.0 – 98.0 | 50% | 50% | 40% | 30% |
| 90 – 94.99 | 40% | 30% | 20% | 10% |
| 85 – 89.99 | 30% | 20% | 10% | NA |
| 80 – 84.99 | 20% | 10% | 5% | NA |
For CPU Students ( Graduation Year 2024 or 2025)
| Scholarship in Tuition Fee | ||||
|---|---|---|---|---|
| Till 31st July | After 31st July | |||
| CPU Student (Graduating in 2024 or 2025) | 1. Waiver in Admisoin Fee Rs 5000/- 2. Scholarship 10% in Tution fee OR merit scholarship as above whichiever is higher | NA | ||
Note:
- 1. Merit scholarship is linked with admission date (irrespective of date of result declaration of previous class)
- 2. Student can avail only one type of merit scholarship scholarship (whichever is higher).
- 3. Merit Schiolarship Continuation eligibility: Above merit scholarship is only for 1st Year. For continuation of the same in subsequent year, student has to maintain CGPA >= 6.5
- 4. EAB Scholarship is given in addion to the merit scholarship.
- 5. To avail scholarship, student must deposit applicable semester fee before the cut-off date for scholarship. If student fail to do so, his/her eligibiity for availing scholarship will be cancelled.
- 6. Also, to avail merit scholarship, student has to submit supporting documents in original along with 1 set of photocopies.
Why Join B. Tech in
Artificial Intelligence & Machine Learning?
The scope of B.Tech AIML is extensive and dynamic, offering diverse career opportunities in technology, entrepreneurship, research, and cross-disciplinary applications. With the increasing demand for digital solutions across industries, AIML professionals are well-positioned for success and innovation in the ever-evolving field of technology.
- High Demand and Career Opportunities: AI and ML technologies are in high demand across various industries, including healthcare, finance, automotive, retail, and more. Graduates with expertise in AI and ML are sought after by employers for roles such as machine learning engineer, data scientist, AI researcher, and AI software developer.
- Cutting-edge Technology: AI and ML are at the forefront of technological innovation. Joining a B. Tech program in CSE with a focus on AI and ML allows students to work with cutting-edge technologies, algorithms, and tools used to solve complex problems and develop intelligent systems.
- Interdisciplinary Learning: AI and ML draw from various disciplines, including computer science, mathematics, statistics, and engineering. Students in a B. Tech program with a specialization in AI and ML gain interdisciplinary knowledge and skills that are applicable across a wide range of domains and industries.
- Research Opportunities: B. Tech programs in CSE with a focus on AI and ML often provide opportunities for research and innovation. Students can work on research projects, collaborate with faculty members, and contribute to advancements in AI and ML technologies through publications, conferences, and industry partnerships.
- Practical Experience: The program typically includes hands-on projects, laboratory sessions, and internships where students apply AI and ML algorithms to real-world problems. This practical experience enhances students' problem-solving skills, programming abilities, and understanding of AI and ML concepts in practical settings.
What makes Department
of B. Tech in
Artificial Intelligence & Machine Learning unique?
Several factors can make a Department offering a Bachelor of Technology (B. Tech) program in Computer Science and Engineering (CSE) with a specialization in Artificial Intelligence (AI) and Machine Learning (ML) unique:
Placements
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Package: 6 LPA
Package: 9 LPA
How to Apply
Steps to Follow
Frequently Asked Questions
B. Tech in Artificial Intelligence & Machine Learning at Career Point University
Candidates must have completed 10+2 or equivalent examination with Physics, Chemistry, and Mathematics as core subjects. They should have secured a minimum aggregate of 50% marks in these subjects.
Yes, admission is typically based on performance in the Career Point University Entrance Exam (CPUEE) or other national-level entrance exams like JEE Main.
The application process involves:
- Filling out the online application form available on the university’s official website.
- Submitting academic transcripts and entrance exam scores.
- Paying the application fee as specified by the university.
Yes, Career Point University offers scholarships based on academic performance, entrance exam scores, and other criteria. Detailed information can be found on the university’s scholarship page.
While a general Computer Science degree covers a broad range of computing topics, a B. Tech in AI & ML focuses specifically on artificial intelligence and machine learning, providing in-depth knowledge and specialized skills in these areas.
Students are encouraged to participate in workshops, seminars, and conferences. Career Point University also has student clubs and organizations dedicated to AI & ML where students can collaborate and learn about the latest trends and advancements.
Students have access to state-of-the-art laboratories, high-performance computing resources, extensive libraries, and licensed software tools for AI & ML development.
Graduates can pursue careers as AI/Machine Learning Engineers, Data Scientists, Research Scientists, Software Developers, and Consultants. They can work in sectors like technology, healthcare, finance, automotive, and retail.Graduates can pursue careers as AI/Machine Learning Engineers, Data Scientists, Research Scientists, Software Developers, and Consultants. They can work in sectors like technology, healthcare, finance, automotive, and retail.
Yes, graduates can pursue advanced degrees such as M.Tech, M.S., or Ph.D. in AI, ML, data science, or related fields.
Companies like Google, Microsoft, IBM, Amazon, Facebook, Tesla, healthcare institutions, financial firms, and startups focused on AI technologies actively hire AI & ML graduates.