Artificial intelligence is a broad field, which refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more. Although artificial intelligence is often thought of as a system in itself. It is a set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem.
Machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions. Machine learning algorithms improve performance over time as they are trained exposed to more data. Machine learning models are the output, or what the program learns from running an algorithm on training data. The more data used, the better the model will get.
AI and ML bring powerful benefits to organizations of all shapes and sizes, with new possibilities constantly emerging. In particular, as the amount of data grows in size and complexity, automated and intelligent systems are becoming vital to helping companies automate tasks, unlock value, and generate actionable insights to achieve better outcomes.
Industries are using AI and ML in various ways to transform their work and do business. Incorporating AI and ML capabilities into their strategies and systems helps organizations rethink how they use their data and available resources, drive productivity and efficiency, enhance data-driven decision-making through predictive analytics, and improve customer and employee experiences.
Here are some of the most common applications of AI and ML:
- Healthcare and life sciences
- Manufacturing
- Ecommerce and retail
- Financial services
- Telecommunications
- Comprehensive Industry Oriented Curriculum: Well-designed curriculum covering the fundamentals and advanced topics in AI and ML (Generative AI, Deep Learning, Natural Language Processing, Computer Vision, etc.)
- Hands-On/ Skills: More emphasis on practical, skills hands-on approach with real-world projects.
- Expert Faculty: Highly qualified and experienced faculty members with expertise in AI and ML, with significant contribution in research and industry sectors.
- State-of-the-Art Labs: Well-equipped AI and ML laboratories with the latest hardware and software resources.
- Interdisciplinary Approach: Collaborations with other departments to promote interdisciplinary learning, such as combining AI/ML with business, healthcare, or engineering, etc.
- Industry Connections: Partnerships with leading tech companies to provide internships, workshops, and guest lectures from industry experts.
- Hackathons and Technical Event: Frequent conduction of technical activities like hackathons, data science competitions, and coding challenges to foster a competitive and collaborative learning environment.
- Networking Opportunities: Through various outreach programs such as Industry and International collaborative events, conferences, and seminars with professionals in AI and ML across the globe.
- Career Services: Dedicated career services to assist students in securing internships and job placements in AI and ML-related fields.
- Research Mentorship for Higher Education: Opportunities for students to engage in AI and ML research projects to cater their scope for pursing higher education.
JOB ROLES
The field of Artificial Intelligence (AI) and Machine Learning (ML) offers a diverse range of job roles across various industries. Here are some common job roles in this field:
-
- Machine Learning Engineer
- Data Scientist
- AI Research Scientist
- AI/ML Research Engineer
- Computer Vision Engineer
- Natural Language Processing (NLP) Engineer
- Data Engineer
- AI Product Manager
- Robotics Engineer
- AI Solutions Architect
These roles often require a combination of skills in and knowledge of artificial intelligence, machine learning frameworks and tools.
| Semester I | ||
| No. | Subject | Code |
| 1.1 | Applied Mathematics – I | AI0101 |
| 1.2 | Computer Programming and Algorithms | AI0102 |
| 1.3 | Computer Organization and Architecture | AI0103 |
| 1.4 | Introduction to Artificial Intelligence | AI0104 |
| 1.5 | Exploratory data analysis | AI0105 |
| 1.6 | Sustainability and Environmental Studies | IEVS100/AI0106 |
| 1.7 | Business Communication Skills | ISDC101 |
| Total Weekly Hours: | 30 | |
| Semester II | ||
| No. | Subject | Code |
| 2.1 | Applied Mathematics – II | AI0201 |
| 2.2 | Data Structures | AI0202 |
| 2.3 | Operating Systems | AI0203 |
| 2.4 | Fundamentals of Machine Learning | AI0204 |
| 2.5 | Python Programming | AI0205 |
| 2.6 | Business Fundamentals | AI0206 |
| 2.7 | Life Coping Skills | ISDC201 |
| 2.8 | Summer Internship | AI0207 |
| Total Weekly Hours: | 31 | |
| Semester-III | ||
| No. | Subject | Code |
| 3.1 | Database Management Systems | AI0301 |
| 3.2 | Object-Oriented Programming | AI0302 |
| 3.3 | Deep Learning | AI0303 |
| 3.4 | Computer Graphics | AI0304 |
| 3.5 | IoT with Artificial Intelligence | AI0305 |
| 3.6 | Domain Foundation-I (Elective)* | |
| Pattern Recognition | AI0306A | |
| Information Security | AI0306B | |
| Design Thinking | AI0306C | |
| Open Elective | AI0306D | |
| 3.7 | Personality Enhancement Skills | IDSC301 |
| Total Weekly Hours: | 31 | |
| Semester IV | ||
| No. | Subject | Code |
| 4.1 | Big Data Analytics | AI0401 |
| 4.2 | Web Application Development | AI0402 |
| 4.3 | Introduction to Natural Language Processing (NLP) | AI0403 |
| 4.4 | Computer Networks | AI0404 |
| 4.5 | Software Development and Operations | AI0405 |
| 4.6 | Domain Foundation-II (Elective)* | |
| Robotics and Automation | AI0406A | |
| AI in Cybersecurity | AI0406B | |
| Games Theory | AI0406C | |
| Open Elective | AI0406D | |
| 4.7 | Career Development Skills | IDSC402 |
| 4.8 | Summer Internship | AI0407 |
| Total Weekly Hours: | 31 | |
| Semester V | ||
| No. | Subject | Code |
| 5.1 | Design and analysis of | AI0501 |
| Algorithm | ||
| 5.2 | Social Media Analytics | AI0502 |
| 5.3 | Reinforcement Learning | AI0503 |
| 5.4 | Machine Vision | AI0504 |
| 5.5 | Artificial Neural Network | AI0505 |
| 5.6 | Domain Advances-I* | |
| Soft Computing | AI0506A | |
| Blockchain | AI0506B | |
| Augmented Reality and Virtual Reality | AI0506C | |
| Open Elective | AI0506D | |
| 5.7 | Professional Competency Skills | IDSC501 |
| Total Weekly Hours: | 31 | |
| Semester VI | ||
| No. | Subject | Code |
| 6.1 | Machine Learning Operations | AI0601 |
| 6.2 | Data Modelling | AI0602 |
| 6.3 | Quantum Computing | AI0603 |
| 6.4 | Cloud Computing | AI0604 |
| 6.5 | Management & | AI0605 |
| Entrepreneurship for IT | ||
| Industry | ||
| 6.6 | Domain Advances-II* | |
| AI in Healthcare | AI0606A | |
| AI in VANET | AI0606B | |
| Demand Forecasting | AI0606C | |
| Open Elective | AI0606D | |
| 6.7 | Behaviour Skills | IDSC601 |
| 6.8 | Internship | AI0607 |
| Total Weekly Hours: | 30 | |
| Semester VII | ||
| No. | Subject | Code |
| 7.1 | Prompt Engineering for Generative AI | AI0701 |
| 7.2 | Explainable Artificial Intelligence | AI0702 |
| 7.3 | Web Semantics and Ontology | AI0703 |
| 7.4 | Research Methodology | AI0704 |
| 7.5 | Project | AI0705 |
| 7.6 | Organizational Skills | IDSC701 |
| Total Weekly Hours: | 30 | |
| Semester VIII | ||
| No. | Subject | Code |
| 8.1 | Internship | AI0801 |
| 8.2 | Seminar | AI0802 |
| Total Weekly Hours: | 18 |
* – Only one domain elective will be opted by the students in each semester of the second and third year.
Passed 10+2 examination with Physics and Mathematics and one of the subjects from the following
Chemistry or Computer Science or Electronics or Information Technology or Biology or Informatics Practices or Biotechnology or Technical Vocational subject or Agriculture or Engineering Graphics or Business Studies or Entrepreneurship and obtained at least 50% marks (at least 40% marks, in case of reserved category of candidates belonging to reserved category as per guidelines of UGC / AICTE / Respective Council) in the above subjects taken together.
OR
Passed min. 3 years Diploma examination with at least 50% marks (40% marks in case of candidates belonging to reserved category) subject to vacancies in the First Year, in case the vacancies at lateral entry are exhausted.
AND
Qualifying score in JEE OR MH-CET OR any other State level Engineering Entrance Exam OR National level Engineering Entrance Exam OR Common University Entrance Test (CUET) OR Symbiosis Engineering Entrance Test (SEET) conducted by SSPU OR Symbiosis International (Deemed) University’s SET Score Card and Candidate also need to appear for counselling session.
Candidates will be selected based on their performance in the selection process as set by SSPU.
NRI Candidate: in the above subjects taken together and must obtain an equivalence certificate from the Association of Indian Universities (AIU).
| FEE STRUCTURE FOR NEW AND EXISTING (FOREIGN AND NRI) STUDENTS FOR THE ACADEMIC YEAR 2025-26 | |||
| 1 | Academic Fees | 2,80,000 | |
| 2 | Caution Money (For new Admission) Refundable | 10,000 | |
| TOTAL | 2,90,000 | ||
| Payment Slabs | Installment 1 | Installment 2 | |
| Academic Fees (New Admission) | 1,50,000 | 1,40,000 | |
| Due Date | (Payable on Admission) | 31.10.2025 | |
| Academic Fees (Existing) | 1,40,000 | 1,40,000 | |
| Due Date | 31.07.2025 | 31.10.2025 | |
| Hostel Charges | |||
| 1 | 2 Seater Non AC Rooms | 1,95,000 | |
| 2 | Hostel Caution Money (Refundable) | 15,000 | |
| Due Dates for “Payments of Instalments” – | ||
| Below are the due dates for the payment of Academic Fees for FY 25-26. | ||
| First Instalment | Second Instalment | |
| New Students *** | As per the Policy available on “Website”. | 31.10.2025 |
| FEE STRUCTURE FOR NEW AND EXISTING (FOREIGN AND NRI) STUDENTS FOR THE ACADEMIC YEAR 2025-26 | |||
| 1 | Academic Fees | 3,35,000 | |
| 2 | Caution Money (For new Admission) Refundable | 10,000 | |
| TOTAL | 3,45,000 | ||
| Payment Slabs | Installment 1 | Installment 2 | |
| Academic Fees (New Admission) | 1,77,500 | 1,67,500 | |
| Due Date | (Payable on Admission) | 31.10.2025 | |
| Academic Fees (Existing) | 1,67,500 | 1,67,500 | |
| Due Date | 31.07.2025 | 31.10.2025 | |
| Hostel Charges | |||
| 1 | 2 Seater Non AC Rooms | 1,95,000 | |
| 2 | Hostel Caution Money (Refundable) | 15,000 | |
| Due Dates for “Payments of Instalments” – | ||
| Below are the due dates for the payment of Academic Fees for FY 25-26. | ||
| First Instalment | Second Instalment | |
| New Students *** | As per the Policy available on “Website”. | 31.10.2025 |
