B.Sc. (AI & ML)
The Bachelor of Science in Artificial Intelligence and Machine Learning (B.Sc (AI & ML))
Pattern
NEP 2020
Programme Overview
The Bachelor of Science in Artificial Intelligence and Machine Learning (B.Sc (AI & ML)) and B.Sc (AI & ML) Honors and Research; program is designed to provide advanced education and training in the field of AI and ML. Driven by the combination of increased access to data, computational power, and improved algorithms, Artificial Intelligence (AI) technologies are entering the mainstream of technological innovation. These technologies include search, machine learning, natural language processing, robotics and computer vision.
This course will also introduce the field of Machine Learning, in particular focusing on the core concepts of supervised, unsupervised learning and reinforcement learning. In supervised learning we will discuss algorithms which are trained on input data labeled with a desired output, for instance an image of a face and the name of the person whose face it is, and learn a function mapping from the input to the output. Unsupervised learning aims to discover latent structure in input data where no output labels are available. Students will learn the algorithms which underpin many popular Machine Learning techniques, as well as developing an understanding of the theoretical relationships between
these algorithms.
Eligibility
Passed Standard XII (10+2) or equivalent examination with Physics and Mathematics as compulsory subjects along with one of the Chemistry/ Biotechnology/ Biology/ Technical Vocational subject/ Computer Science/ Information Technology/ Informatics Practices/ Agriculture/ Engineering Graphics/ Business Studies from any recognized Board with a minimum of 50% marks or equivalent grade (45% marks or equivalent grade for Scheduled Caste/ Scheduled Tribes).
Programme Structure
B.Sc. Artificial Intelligence and Machine Learning – Course Structure
The B.Sc. Artificial Intelligence and Machine Learning (AI & ML) programme follows the curriculum prescribed by Savitribai Phule Pune University (SPPU) as per the National Education Policy (NEP) guidelines. The programme is designed to provide students with a strong foundation in computer science, programming, mathematics, statistics, artificial intelligence, and machine learning.
The course structure is organized across six semesters (Graduation), covering three years of study. It includes core courses, practical sessions, major electives, minor courses, skill-based courses, open electives, projects, and other components as prescribed by the university.
The curriculum progressively develops students’ theoretical knowledge and practical skills in areas such as Python programming, data structures, software engineering, databases, artificial intelligence, machine learning, data analytics, deep learning, natural language processing, and related technologies.
The programme also emphasizes hands-on learning through laboratory work, mini-projects, internships or on-the-job training, and research projects, wherever applicable under the prescribed course structure. In the final year (4th year after graduation) , students may follow the applicable Honors Degree or Honors with Research Degree pathway, as specified in the university syllabus.
The detailed semester-wise course structure, including course codes, titles, credits, teaching schemes, and examination patterns, is available in the official syllabus PDF attached below.
Programme: B.Sc. Artificial Intelligence and Machine Learning
University: Savitribai Phule Pune University (SPPU)
Curriculum: NEP Guidelines
Duration: Four Years (Honours)
Course Structure: Semester I to Semester VIII
Career Prospects
The B.Sc. Artificial Intelligence and Machine Learning programme opens up career opportunities in AI, machine learning, data analytics, Python programming, and software development. Graduates can pursue roles in IT companies, technology startups, and research organizations or opt for higher education and specialized certifications to advance their careers.
Programme Outcome
Programme Outcomes:
On the successful completion of the program, the following are the expected outcomes.
PO1: Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.
PO2: Demonstrate awareness and a fundamental understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.
PO3: Identify problems where artificial intelligence techniques are applicable and demonstrate ability to share in discussions of AI, its current scope and limitations, and societal implications.
PO4: Demonstrate proficiency in applying scientific method to models of machine learning.
PO5: Develop an appreciation for what is involved in Learning models from data by understanding a wide variety of learning algorithms and by understanding of the strengths and weaknesses of many popular machine learning approaches
PO6: To apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the ML models.
PO7: Consider the pros and cons when choosing ML / AI methods for different applications
PO8: Appreciate the underlying mathematical relationships within and across Machine Learning an
AI
PO8: Conduct investigations of complex problems by using research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
PO9: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings
PO10: Communicate effectively on complex engineering activities with then engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give clear instructions.