Program Objectives
The Program Objectives for the B.Tech in Computer (AI & ML) program are as follows:
- To provide students with a comprehensive understanding of the principles and theories of AI & ML.
- To equip students with the practical skills necessary to design and implement AI/ML algorithms, models, and systems.
- To introduce industry-relevant topics to prepare them for successful careers in AI and ML engineering.
- To provides opportunities for hands-on experience with real-world data and problems.
- To explore the interdisciplinary nature of AI and ML by incorporating concepts from diverse fields such as computer science, mathematics, psychology, philosophy, and neuroscience.
- To prepare students for advanced research in AI and ML by providing opportunities to participate in research projects with faculty members.

4 Years
Duration

₹ 1,95,000
Fees

PCET
Centralized Placement Cell
Program Highlights
Stringent & disciplined academics.
Unique in-depth learning on emerging technologies.
Strong emphasis on Project, Labs, and Case Study based learning.
Hands-on with industry projects and sessions by industry experts
Industry-Academia Collaboration Framework
Quality placements.
Student participation in global competitions.
Exposure of In-house Incubation Cell nurturing various Start ups.
Preamble
Artificial Intelligence & Machine learning has become important due to recent technology disruptions and are the most transformative technology available today. Today’s world is powered by machines which can think on their own and help us to solve problems.
Given the mounting importance of the AI&ML paradigm, Pimpri Chinchwad University offers a4 years under-graduate B.Tech course in Computer Science & Engineering (Artificial Intelligence and Machine Learning) which aims to develop a strong foundation by using the principles and technologies that consist of many facets of Artificial Intelligence including logic, knowledge representation, probabilistic models, and machine learning. Students will obtain an in-depth knowledge of machine learning and artificial intelligence by implementing real-world problems in a wide variety of application domains such as robotics, computer vision, natural language processing, etc. Students will be experienced in machine learning pipeline, data, models, algorithms and empirics.
Vision and Mission
Vision
To provide value-based technical Education in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning.
Mission
- To develop technically competent and innovative computer science engineers with in-depth knowledge of Artificial Intelligence and Machine Learning.
- To build ethically responsible engineers to serve the needs of industry and society at large.
- To provide a conducive environment and opportunities for holistic development of students.
Course Curriculum
Semester I
Sr. No. |
Category |
Course Name |
Teaching Scheme |
Cr |
Evaluation Scheme | |||||||||
L | T | P | H | IE | MTE | ETE | TW | PR | OR | Total | ||||
1 |
MAJ |
Linear Algebra & Univariate Calculus |
3 |
1 |
– |
4 |
4 |
20 |
20 |
60 |
50 |
– |
– |
150 |
2 | MIN | Engineering Physics | 3 | – | – | 3 | 3 |
20 |
20 |
60 |
– |
– |
– |
100 |
3 |
MAJ |
Basic Electrical & Electronics Engineering |
3 |
– |
– |
3 |
3 |
– |
– |
– |
50 |
– |
– |
50 |
4 | SEC | Computer Programming – I | 1 | – | – | 1 | 1 |
20 |
20 |
60 |
– |
– |
– |
100 |
5 | OE | Open Elective – I | 3 | – | – | 3 | 3 |
– |
– |
– |
50 |
– |
– |
50 |
6 | MIN | Engineering Physics Lab | – | – | 1 | 2 | 1 |
20 |
20 |
60 |
– |
– |
– |
100 |
7 |
MAJ |
Basic Electrical & Electronics Engineering Lab |
– |
– |
1 |
2 |
1 |
– |
– |
– |
30 |
– |
20 |
50 |
8 | SEC | Computer Programming Lab I | – | – | 2 | 4 | 2 |
20 |
20 |
– |
– |
– |
– |
40 |
9 | AEC | HSMC – I | – | – | 1 | 2 | 1 | – | – | – | 20 | 40 | – | 60 |
10 | VAC | Life Skill-I | – | – | 1 | 2 | 1 | – | – | – | 50 | – | – | 50 |
Total | 13 | 01 | 6 | 26 | 20 | 750 |
Semester II
Sr. No. |
Category |
Course Name |
Teaching Scheme |
Cr |
Evaluation Scheme | |||||||||
L | T | P | H | IE | MTE | ETE | TW | PR | OR | Total | ||||
1 | MAJ | Multivariate Calculus | 3 | 1 | – | 4 | 4 |
20 |
20 |
60 |
50 |
– |
– |
150 |
2 | MAJ | Engineering Chemistry | 3 | – | – | 3 | 3 |
20 |
20 |
60 |
– |
– |
– |
100 |
3 | MIN | Engineering Graphics | 2 | – | – | 2 | 2 |
– |
– |
– |
50 |
– |
– |
50 |
4 | SEC | Computer Programming – I | 1 | – | – | 1 | 1 |
20 |
20 |
60 |
– |
– |
– |
100 |
5 |
MAJ |
Engineering Chemistry Laboratory |
– |
– |
1 |
2 |
1 |
– |
– |
– |
50 |
– |
– |
50 |
6 | OE | Open Elective – II | 3 | – | – | 3 | 3 |
20 |
20 |
60 |
– |
– |
– |
100 |
7 |
MIN |
Engineering Graphics Laboratory |
– |
– |
2 |
4 |
2 |
– |
– |
– |
30 |
– |
20 |
50 |
8 | AEC | HSMC – II | – | – | 1 | 2 | 1 |
20 |
20 |
– |
– |
– |
– |
40 |
9 | SEC | Computer Programming Lab I | – | – | 2 | 4 | 2 |
– |
– |
– |
20 |
40 |
– |
60 |
10 | VAC | Life Skill-II | – | – | 1 | 2 | 1 | – | – | – | 50 | – | – | 50 |
Total | 12 | 01 | 7 | 27 | 20 | 750 |
Semester III
Semester – III | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Data Science | 3 | – | – | 3 | 3 |
2 | MIN | Discrete Mathematics | 3 | 1 | – | 4 | 4 |
3 | MAJ | Data Structures | 3 | – | – | 3 | 3 |
4 | OE | Open Elective – III | 3 | – | – | 3 | 3 |
5 | SEC | Proficiency Foundation Course – I | 1 | – | – | 1 | 1 |
6 | MAJ | Data Science Lab | – | – | 1 | 2 | 1 |
7 | MAJ | Data Structures –Lab | – | – | 1 | 2 | 1 |
8 | AEC | HSMC – III | – | – | 1 | 2 | 1 |
9 | SEC | Proficiency Foundation Course – I Lab | – | – | 2 | 4 | 2 |
10 | VAC | Life Skill-III | – | – | 1 | 2 | 1 |
Total | 13 | 01 | 6 | 26 | 20 |
Semester IV
Semester – IV | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Artificial Intelligence | 3 | – | – | 3 | 3 |
2 | MAJ | MAchine Learning | 3 | – | – | 3 | 3 |
3 | MIN | Object Oriented Programming with JAVA | 2 | – | – | 2 | 2 |
4 | MIN | Database Management System | 2 | – | – | 2 | 2 |
5 | MAJ | Artificial Intelligence Lab –Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Machine Learning Lab | – | – | 2 | 4 | 2 |
7 | MIN | Object Oriented Programming-Lab | – | – | 1 | 2 | 1 |
8 | MIN | Database Management System Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – IV | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – II Lab | – | – | 2 | 4 | 2 |
11 | VAC | Life Skill-IV | – | – | 1 | 2 | 1 |
Total | 10 | – | 10 | 30 | 20 |
Semester V
Semester – V | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Computer Vision | 3 | – | – | 3 | 3 |
2 | MAJ | Deep Learning | 3 | – | – | 3 | 3 |
3 | MIN | Design and Analysis of Algorithms | 2 | – | – | 2 | 2 |
4 | MIN | Elective – I | 2 | – | – | 2 | 2 |
5 | MAJ | Computer Vision Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Deep Learning Lab | – | – | 2 | 4 | 2 |
7 | MIN | Design and Analysis of Algorithms Lab | – | – | 1 | 2 | 1 |
8 | MIN | Elective – I Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – V | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – III Lab | – | – | 2 | 4 | 2 |
11 | VAC | Life Skill-V | – | – | 1 | 2 | 1 |
Total | 10 | 00 | 10 | 30 | 20 |
Semester VI
Semester – VI | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Big Data Analytics | 3 | – | – | 3 | 3 |
2 | MAJ | Natural Language Processing | 3 | – | – | 3 | 3 |
3 | MIN | Computer Networks & Internet of Things | 2 | – | – | 2 | 2 |
4 | MIN | Elective – II | 2 | – | – | 2 | 2 |
5 | MAJ | Big Data Analytics Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Natural Language Processing Lab | – | – | 2 | 4 | 2 |
7 | MIN | Computer Networks & Internet of Things Lab | – | – | 1 | 2 | 1 |
8 | MIN | Elective – II Lab | – | – | 1 | 2 | 1 |
9 | AEC | HSMC – VI | – | – | 1 | 2 | 1 |
10 | SEC | Proficiency Foundation Course – IV Lab | – | – | 2 | 4 | 2 |
11 | VAC | Life Skill-VI | – | – | 1 | 2 | 1 |
Total | 10 | 00 | 10 | 30 | 20 |
Semester VII
Semester – VII | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Soft Computing and Optimization Algorithms | 3 | – | – | 3 | 3 |
2 | MAJ | Robotics & Intelligent Systems | 3 | – | – | 3 | 3 |
3 | MIN | Elective – III | 2 | – | – | 2 | 2 |
4 | MIN | Elective – IV | 2 | – | – | 2 | 2 |
5 | MAJ | Soft Computing and Optimization Algorithms Lab | – | – | 2 | 4 | 2 |
6 | MAJ | Robotics & Intelligent Systems Lab | – | – | 2 | 4 | 2 |
7 | MIN | Elective – III Lab | – | – | 1 | 2 | 1 |
8 | MIN | Elective – IV Lab | – | – | 1 | 2 | 1 |
9 | INTR | Internship | – | – | 4 | 8 | 4 |
Total | 10 | – | 10 | 30 | 20 |
Semester VIII
Semester – VIII | Teaching Scheme | ||||||
Sr. No. | Category | Course Name | L | T | P | H | Cr |
1 | MAJ | Time series analysis and Forecasting | 3 | – | – | 3 | 3 |
2 | MIN | Elective – V | 2 | – | – | 2 | 2 |
3 | MAJ | Time series analysis and Forecasting Lab | – | – | 2 | 4 | 2 |
4 | MIN | Elective – V Lab | – | – | 1 | 2 | 1 |
5 | PROJ | Project | – | – | 12 | 24 | 12 |
Total | 5 | 00 | 15 | 35 | 20 |
List of Tentative Electives:
Sr. No. | Course Name |
1 | AI in healthcare |
2 | AI in Cyber security |
3 | AI in Gaming |
4 | Augmented Reality & Virtual Reality |
5 | Edge Computing |
6 | Healthcare Data Analytics |
7 | BioInformatics |
8 | Social Media Analytics |
9 | Bayesian Data Analysis |
10 | Business Intelligence and Analytics |
11 | Cognitive Systems |
12 | Data Modelling and Simulation |
13 | Decision Support systems and Intelligent systems |
14 | Intelligent Database System |
15 | Information Extraction and Retrieval |
16 | Knowledge Representation and Reasoning |
17 | Nature Inspired computing for Data Science |
List of Tentative Open Electives:
Sr. No. | Course Name |
1 | Introduction to Artificial Intelligence |
2 | Introduction to Machine Learning |
3 | Business Intelligence |
4 | Python for Data Science |
5 | Neural Network and fuzzy logic Control |
6 | Programming with Python |
7 | Data Structures using Python |
8 | Data Science for Engineers |
9 | Introduction of Data Science |
10 | Data Analytics using Python |
List of Tentative Life Skill Courses:
Sr. No. | Course Name |
1 | Practicing Meditation |
2 | Sports |
3 | Yoga |
4 |
Performing Arts:
Music, Singing, Poetry, Indian Conventional Dancing, Photography, Short Movie Making, Painting/ Sketching/ Drawing, Theatre Arts, Anchoring, Calligraphy etc. |
5 | Social welfare and Cultural Awareness |
6 |
Caring and service
Hospital Caring, Personal Safety, First Aid, Disaster Management Gardening, Organic farming, Cooking etc. |
Abbreviations: Course Abbreviation; L – Lecture; T – Tutorial; P – Practical; H – Hours; CR – Credits, HSMC – Humanities/ Social Sciences/ Management Courses
Programme
Programme Educational Objectives (PEOs)
- Graduates of the program will demonstrate world-class expertise in AI and ML and emerging technologies which help them to stand in crowd and grow careers in the technological era.
- Graduates of the program will exhibit technical competency with a learning attitude.
- Graduates of the program will practice their professional career with ethical and social responsibilities.
- Graduates of the program will demonstrate research aptitude and innovation throughout their career.
Programme Outcomes (POs)
- Engineering Knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
- Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
- Design/Development of Solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
- Conduct Investigations of Complex Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
- Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
- The Engineer and Society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
- Environment and Sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
- Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
- Individual and Team Work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
- Communication: Communicate effectively on complex engineering activities with the 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 and receive clear instructions.
- Project Management and Finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
- Life-long Learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Programme Specific Outcomes (PSOs)
- Analyze and Apply the knowledge of Artificial Intelligence, Machine Learning and intelligent systems to solve real world problems.
- To develop project development skills using innovative tools and techniques of AI & ML domain to solve social needs.
Career Opportunities
There are many exciting career opportunities in AI & ML engineering such as
Eligibility
Passed 10+2 examination with Physics & Mathematics AND one of the subject from the following:
Chemistry/ Computer Science/ Electronics/ Information Technology/ Biology / Informatics Practices/ Biotechnology/ Technical Vocational subject/ Agriculture/ Engineering Graphics/ Business Studies/ Entrepreneurship.
Obtained at least 45% marks (40marks in case of candidate belonging to reserved category) in the above subjects taken together.
In addition to this, the applicant must have qualified at least one engineering entrance examination like MHT-CET 2023/JEE 2023 /Other State or National Level Engineering Entrance Exam of 2023 / PERA 2023 / CUET 2023 or Entrance Test Conducted by PCU
Candidates are selected based on entrance test score and on merit.