BCA (AI & ML)

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The BCA in Artificial Intelligence and Machine Learning (AI & ML) programme at the School of Science and Computer Studies (SSCS), CMR University, Bengaluru, is a specialised undergraduate course designed to equip students with advanced technical skills in AI and ML technologies. The programme emphasises hands-on learning through real-time projects, assignments, and industry-relevant case studies, enabling students to build intelligent systems and data-driven solutions.

The curriculum covers key areas such as machine learning models, deep learning, natural language processing, and computer vision, ensuring students are prepared for industry requirements. With a strong foundation in programming languages, data science principles, and algorithm development, students gain expertise in tools like Python and TensorFlow.

Graduates can pursue diverse roles such as AI Engineer, Data Scientist, and Business Intelligence Analyst, catering to the increasing demand for AI and ML professionals across industries. The programme also focuses on industry-relevant skills like data analytics, predictive modeling, and machine learning frameworks, ensuring students are well-equipped to address real-world challenges.

Duration

3 years

Programme Type

Full-time

Programme Fees

Rs. 62,500/- per semester

Eligibility Criteria

Candidates should have passed PUC / 12th Grade or equivalent examination in any stream from a recognised board. Candidates should also have studied Mathematics as a compulsory subject up to the 10th Grade.

Scope and Objective

The BCA in Artificial Intelligence and Machine Learning (AI & ML) programme at CMR University, Bengaluru, offers promising career prospects in emerging technology domains. The course is designed to equip students with skills in data analytics, predictive modeling, and AI-driven solutions, preparing them for roles in industries such as healthcare, finance, and e-commerce.

Course Objectives:

Develop a strong foundation in programming languages, data science, and algorithm development.
Equip students with practical knowledge of machine learning models, deep learning, and natural language processing.
Foster problem-solving skills through hands-on projects and industry-relevant case studies.
Prepare students to apply AI and ML techniques for developing intelligent systems and data-driven solutions.
Enhance research capabilities in advanced AI technologies and innovative applications.
Train students in industry-standard tools such as Python, TensorFlow, and Keras.
Develop skills for designing predictive models and improving business intelligence strategies.
Enable students to pursue roles like AI Engineer, Data Scientist, and Business Analyst across diverse industries.

Programme Structure

Semester I

Problem Solving Techniques Using C and Lab
Database Management Systems and Lab
Mathematical Foundation for Computer Science
Language
Common Core Courses
Community Service Programme – I

Semester II

Data Structures using C and Lab
Operating System & Linux Foundation and Lab
Statistics
Interdisciplinary Elective I
Language
Common Core Courses
Community Service Programme – II

Semester III

Artificial Intelligence and Machine Learning fundamentals with Lab
Object Oriented Programming Using Java and Lab
Software Engineering
Interdisciplinary Elective II
Excel for Data Analysis
Common Core Courses
Community Service Programme – III

Semester IV

Deep Learning and Lab [with TensorFlow and PyTorch]
Web Application Development and Lab
AI in Computer Vision
MOOC
Interdisciplinary Elective III
Common Core Courses
Career Preparedness Program-III

Programme Assessment

Choice-Based Credit System (CBCS): The university follows CBCS, allowing students to choose courses and earn credits based on their performance.
Grades and GPA: Students are awarded grades for each course in a semester. SGPA is used to measure academic performance for each semester, while CGPA reflects performance across all semesters.
Prescribed Curriculum: Each programme has a prescribed Scheme of Teaching and Evaluation, including required courses, labs, and MOOCs from reputed institutions.
Auditing Courses: Students can audit courses to gain additional exposure without the pressure of earning a grade—useful for placements.
Evaluation System: Evaluation is continuous and includes Continuous Internal Evaluation (CIE) and Semester End Examination (SEE), each contributing 50% to the final score.
Assessment Methods: Faculty members may use assignments, quizzes, seminars, case studies, presentations, and projects to assess student performance.
Semester End Examination: SEE is held for all registered courses unless the course is entirely evaluated through CIE.
Makeup Examinations: Students who fail the SEE in one or more courses can take makeup exams to improve their grades.

Programme Outcome

Analysis and Development of Solutions: Ability to solve real-world problems through data visualisation using tools like R, Tableau, and Python.
Data-Driven Decision-Making: Prepares students to design solutions for business problems through data-driven decisions using NoSQL databases.