B. Tech Computer Science and Engineering (Artificial Intelligence & Machine Learning)

Duration
4 Years Full-Time Program
Eligibility

Eligibility Norms for Admission to B. Tech Programs: 

Candidates seeking admission to B.Tech. Program should have passed the Pre-University / Higher Secondary /or any equivalent 10+2 examination from an approved State / Central govt. board or from an international board like University of Cambridge "A" level certificate or certificate from International Baccalaureate (IB), Geneva, etc., with a minimum of 45% marks in Physics and Mathematics as compulsory subjects along with either Chemistry / Biotechnology / Biology / Electronics /Computer as an additional subject, and with 55% aggregate for SOCSE & 50% aggregate for SOE of total marks in the Qualifying Examination. Candidates should have appeared in any national / state level / Presidency University entrance examination viz. CET, Comed – K, JEE and such others.   

Eligibility Norms for Admission to B. Tech Programs (Lateral)  

A candidate who has passed any diploma examination or equivalent examination and obtained a minimum of 60% aggregate is eligible for admission to B. Tech Programs (Lateral Entry). Candidate should have appeared in any state level entrance examination. 

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Artificial intelligence (AI) is a branch of computer science focused on making computers behave in human ways, such as rational thinking, perception and action. Machine Learning (ML) is a branch of artificial intelligence and one of the ways AI can be achieved. While AI initiatives make it possible for a machine to mimic human behavior based on what it has been taught, ML models help machines to teach themselves. This engineering program leads to a specialization in Artificial Intelligence and Machine Learning by combining important domains such as Mathematics, Computer Programming, Machine Learning, Statistics, and Software Design in order to mimic the complexity of human thought processes. Graduates of this program have opportunities to be employed by multinational IT companies and large Indian technology companies, as well as pursue higher studies.

B. Tech Computer Science and Engineering (Artificial Intelligence & Machine Learning)

Programme Overview

Artificial intelligence (AI) is a branch of computer science focused on making computers behave in human ways, such as rational thinking, perception, and action. Machine learning (ML) is a branch of artificial intelligence and one of the ways AI can be achieved. While AI initiatives make it possible for a machine to mimic human behavior based on what it has been taught, ML models help machines to teach themselves. This engineering program leads to a specialization in Artificial Intelligence and Machine Learning by combining important domains such as Mathematics, Computer Programming, Machine Learning, Statistics, and Software Design in order to mimic the complexity of human thought processes. Graduates of this program have opportunities to be employed by multinational IT companies and large Indian technology companies, as well as pursue higher studies. 

Course Curriculum

  • 01Calculus and Differential Equations - MAT2301
  • 02English for Technical Communication - ENG1900
  • 03Optoelectronics and Quantum Physics - PHY2501
  • 04Optoelectronics and Quantum Physics Lab - PHY2504
  • 05Engineering Graphics - MEC1006
  • 06Digital Design - ECE2022
  • 07Digital Design Lab - ECE2052
  • 08Introduction to Design Thinking - DES1146
  • 09Computational Thinking using Python - CSE1500
  • 10Industry Readiness Program – I - PPS1025
  • 01Calculus and Differential Equations - MAT2301
  • 02English for Technical Communication - ENG1900
  • 03Chemistry of Smart Materials - CHE2501
  • 04Chemistry of Smart Materials Lab - CHE2502
  • 05Foundations of Integrated Engineering - CIV1200
  • 06Basics of Electrical and Electronics Engineering - EEE1200
  • 07Basics of Electrical and Electronics Engineering Lab - EEE1250
  • 08Indian Constitution - LAW7601
  • 09Computational Thinking using Python - CSE1500
  • 10Industry Readiness Program – I - PPS1025
  • 01Probability and Statistics - MAT2402
  • 02Advanced English - ENG2501
  • 03Chemistry of Smart Materials - CHE2501
  • 04Chemistry of Smart Materials Lab - CHE2502
  • 05Foundations of Integrated Engineering - CIV1200
  • 06Basics of Electrical and Electronics Engineering - EEE1200
  • 07Basics of Electrical and Electronics Engineering Lab - EEE1250
  • 08Indian Constitution - LAW7601
  • 09Problem Solving using C - CSE2000
  • 10Problem Solving using C Lab - CSE2001
  • 11Industry Readiness Program – II - PPS1026
  • 12Design Workshop - ECE1511
  • 13Environmental Studies - CHE7601
  • 01Probability and Statistics - MAT2402
  • 02Advanced English - ENG2501
  • 03Optoelectronics and Quantum Physics - PHY2501
  • 04Optoelectronics and Quantum Physics Lab - PHY2504
  • 05Engineering Graphics - MEC1006
  • 06Digital Design - ECE2022
  • 07Digital Design Lab - ECE2052
  • 08Introduction to Design Thinking - DES1146
  • 09Problem Solving using C - CSE2000
  • 10Problem Solving using C Lab - CSE2001
  • 11Industry Readiness Program – II - PPS1026
  • 12Design Workshop - ECE1511
  • 13Environmental Studies - CHE7601
  • 01Linear Algebra and Vector Calculus - MAT2303
  • 02Data Communication and Computer Networks - CSE2251
  • 03Data Communication and Computer Networks Lab - CSE2252
  • 04Data Structures - CSE2253
  • 05Data Structures Lab - CSE2254
  • 06Object Oriented Programming Using Java - CSE2255
  • 07Object Oriented Programming Using Java Lab - CSE2256
  • 08Computer Organization and Architecture - CSE2257
  • 09Essentials of AI - CSE2264
  • 10Essentials of AI Lab - CSE2265
  • 11Introduction to Aptitude - APT4002
  • 12Universal Human Values and Ethics - CIV7601
  • 01Discrete Mathematics - MAT2404
  • 02Database Management Systems - CSE2260
  • 03Database Management Systems Lab - CSE2261
  • 04Machine Learning - CAI2500
  • 05Machine Learning Lab - CAI2501
  • 06Ethics of AI - CAI2511
  • 07Analysis of Algorithms - CSE2262
  • 08Analysis of Algorithms Lab - CSE2263
  • 09Aptitude Training - Intermediate - APT4004
  • 10Essentials of Finance - FIN1002
  • 01Theory of Computation - CSE2266
  • 02Deep Learning - CAI2502
  • 03Deep Learning Lab - CAI2503
  • 04Neural Networks and Fuzzy Logic - CAI2512
  • 05Web Technologies - CSE2258
  • 06Web Technologies Lab - CSE2259
  • 07Operating Systems - CSE2269
  • 08Operating Systems Lab - CSE2270
  • 09Professional Elective – I - CSEXXXX
  • 10Internship - CSE7000
  • 11Logical and Critical Thinking - APT4006
  • 01Reinforcement Learning - CAI2507
  • 02Reinforcement Learning Lab - CAI2508
  • 03Natural Language Processing - CAI2504
  • 04Natural Language Processing Lab - CAI2505
  • 05Software Design and Development - CSE2271
  • 06Competitive Programming and Problem Solving - CSE2274
  • 07Professional Elective – II - CSEXXXX
  • 08Professional Elective – III - CSEXXXX
  • 09Open Elective – I - XXXXXXX
  • 10Aptitude for Employability - APT4005
  • 01Professional Elective – IV - CSEXXXX
  • 02Professional Elective – V - CSEXXXX
  • 03Professional Elective – VI - CSEXXXX
  • 04Open Elective – II - XXXXXXX
  • 05Preparedness for Interview - PPS3018
  • 06Mini Project - CSE7100
  • 01Capstone Project - CSE7300

Programme Educational Objectives

After four years of successful completion of the program, the graduates shall be:

PEO 01: Demonstrate as a Computer Engineering Professional.

PEO 02: Engage in lifelong learning through research and professional development.

PEO 03: Serve as a leader in the profession through consultancy, extension activities and/or entrepreneurship.

Programme Outcomes (POs)

On successful completion of the Program, the students shall be able to:

PO 1: Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

PO 2: Problem analysis: Identify, formulate, research literature, and analyse complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.

PO 3: 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.

PO 4: 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.

PO 5: Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.

PO 6: The engineer and society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues, and the consequent responsibilities relevant to professional engineering practice.

PO 7: Environment and sustainability: Understand the impact of professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO 8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of engineering practice.

PO 9: Individual and teamwork: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings. 

PO 10: Communication: Communicate effectively on complex engineering activities with the engineering community and with the 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. 

PO 11: Project management and finance: Demonstrate knowledge 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.

PO 12: 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

On successful completion of the Program, the students shall be able to:

PSO 01: [Problem Analysis]: Identify, formulate, research literature, and analyze complex engineering problems related to AI & ML principles and practices, Programming and Computing technologies reaching substantiated conclusions using first principles of mathematics, natural sciences and engineering sciences.

PSO 02: [Design/development of Solutions]: Design solutions for complex engineering problems related to AI & ML principles and practices, Programming and Computing technologies and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, cultural, societal and environmental considerations.

PSO 03: [Modern Tool usage]: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities related to AI & ML principles and practices, Programming AI & ML Computing & analytics with an understanding of the limitations.

Student handbook

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USP

  • Industry-Driven Curriculum covering deep learning, neural networks, NLP, computer vision, and reinforcement learning.
  • State-of-the-Art AI & ML Labs equipped with high-performance GPUs, TensorFlow, PyTorch, and cloud AI platforms.
  • Expert Faculty & Industry Mentorship providing hands-on experience with real-world AI applications.
  • Hands-On Learning through live projects, AI hackathons, and model deployment.
  • Global Internship & Study Abroad Opportunities for international exposure and career growth.
  • Strong Industry Collaborations with top tech companies for research, internships, and certifications.
  • AI & ML Certifications including TensorFlow, AWS AI, and Microsoft Azure AI to boost employability.
  • Excellent Placement Records with opportunities in AI research, data science, automation, and AI-driven industries.

Career Opportunities

  • AI/ML Engineers

    Develop intelligent systems and machine learning models.

  • Data Scientists

    Extract insights from complex datasets for decision-making.

  • Robotics Engineers

    Design and build advanced robotic systems.

  • NLP Specialists

    Enhance human-computer interaction through language processing.

  • Big Data Analysts

    Analyze vast datasets to uncover trends and solutions.

Your Next Move Awaits

Begin an extraordinary journey with Presidency University.

FAQs

B.Tech. AI & ML programme combines domains such as mathematics, computer programming, machine learning, statistics, and software design to mimic the complexity of human thought processes.

The average salary range of B.Tech. AI & ML graduate is in between ₹5 LPA and ₹10 LPA.