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 2024-25
1 Academic Fees 2,77,000
2 Caution Money (For new Admission) Refundable 10,000
TOTAL 2,87,000
Payment Slabs  Installment 1  Installment 2
Academic Fees (New Admission) 1,48,500 1,38,500
Due Date  (Payable on Admission)  31.10.2024
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 24-25.
First Instalment Second Instalment
New Students *** As per the Policy available on “Website”. 31.10.2024
FEE STRUCTURE FOR NEW AND EXISTING (FOREIGN AND NRI) STUDENTS FOR THE ACADEMIC YEAR 2024-25
1 Academic Fees 3,32,000
2 Caution Money (For new Admission) Refundable 10,000
TOTAL 3,42,000
Payment Slabs  Installment 1  Installment 2
Academic Fees (New Admission) 1,76,000 1,66,000
Due Date  (Payable on Admission)  31.10.2024
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 24-25.
First Instalment Second Instalment
New Students *** As per the Policy available on “Website”. 31.10.2024