B.Tech in CSE (AI & ML)

Know More About B.Tech in CSE (AI & ML)

Course Offered Duration (in Years) Eligibility
B.TECH IN COMPUTER SCIENCE AND ENGINEERING WITH SPECIALIZATION IN AI & ML 4 Years 12th standard pass with Physics, Maths, Chemistry/ Computer Science with at least 60% marks.
B.TECH IN COMPUTER SCIENCE AND ENGINEERING WITH SPECIALIZATION IN AI & ML (Lateral Entry) 3 Years (i) A 3 Years Diploma from an AICTE approved College or UGC recognised University in India with a minimum of 60% marks
(ii) A personal interview by the competent authority of the University
(iii) Credit transfer possible from other recognised University / College in 3rd / 5th semester with terms & conditions.

Disclaimer: TNU reserves the right for relaxation in admission criteria for the most deserving candidates.

Computer Science and Engineering seems to have become the most popular course in this century for engineering aspirants. Currently B.Tech Computer Science Engineering is one of the top courses in India that attract lakhs of students every year. B.Tech Computer Science and Engineering at The Neotia University (TNU) is designed with specializations in several important and emerging areas such as, Cyber Security, Data Science, Artificial Intelligence (AI) and Machine Learning (ML), Embedded Systems & Industrial Internet of Things (IOT) that will help students to gain knowledge and become industry ready for jobs in top Corporates. The curriculum structure is design by the academicians from Indian Statistical Institute (ISI), Jadavpur University, University of Calcutta and industrial experts from Tata Consultancy Services (TCS), Centre for Development of Advanced Computing (CDAC). The state of the art Computer Labs are well equipped with advance software. Internship and Industrial visit is part of the Curriculum. Students are encouraged to publish journals and articles regularly in National and International platforms.

AI has now penetrated in all the horizons of service sector. There is rarely any field, which is untouched by Artificial Intelligence, and it is the life force behind groundbreaking digital technologies and innovations. This specialisation is design to enable students to build intelligent machines, software, or applications with a cutting-edge combination of machine learning and visualisation technologies.

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Expert Committee

Laboratories

Computer lab with software like Kali Linus, VMWare, Open IAM, Vulnerability scanners, NsLookUP, R, Oracle , Hadoop, Sikuli are provided.

Career Prospects

Engineers are in high demand because they harness the power of AI and ML to provide solutions to applications like diagnosis of diseases, image processing, real time personalization, speech recognition, fraud detection and many more. Students have ample of job opportunities in this field. Some demanding job positions include Machine Learning Engineer, Data Scientist,

Business Intelligence Developer, Research Scientists, AI engineer and Robotics Scientist. Entrepreneurship can also be a good career option for the graduates.

The importance of B.Tech in Computer Science and Engineering with specialization in Cyber Security, Data Science, Artificial Intelligence and Machine Learning, Embedded Systems and Industrial IOT is growing day by day and going to provide huge opportunities in employment, research and entrepreneurship in near future. Graduates can pursue M.Tech at various Universities / Institutes in the above specializations as the demand in these areas shall keep increasing

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INTERNSHIP & ACHIEVEMENTS

  • Arindam Halder completes his Internship program from CRIS – Centre for Railway Information Systems
  • B.Tech CSE Student of TNU completes her Internship program from IIT, Kanpur
  • Students completes their Internship program from WABCO

INDUSTRY PARTNERSHIP

TNU has also collaborated with Batoi Systems Private Limited (BSPL), Bhubaneswar to provide better understanding of Computer Science and Engineering in general and specifically Cyber Security, Internet of Things (IoT), large Data Sciences, etc. other current strengths in CSE to its students. BSPL to provide assistance in drafting the syllabus for B.Tech & M.Tech programme along

with help in establishing laboratory and CSE vertical with stress on modern areas including its applications.

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OUR FACULTY

Course Structure

  • Semester 1
  • Semester 2
  • Semester 3
  • Semester 4
  • Semester 5
  • Semester 6
  • Semester 7
  • Semester 8

Semester 1

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Basic Science Course

Physics   

3

1

2

5

2

Basic Science Course

Mathematics I [Calculus and Linear Algebra]

3

1

0

4

3

Engineering Science Course

Basic Electrical and Electronics Engineering

3

1

2

5

4

Engineering Science Course

Engineering graphics and design

0

1

4

3

5

Mandatory Course

Environmental Science

3

3

6

Humanities and Social Sciences including Management Course

English I

2

0

2

3

 

 

No. of hours

14

4

10

 

28 hours    Total Credits

23

Semester 2

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Professional Elective Course

Introduction to AI

3

1

0

4

2

Basic Science Course

Mathematics II  [Probability and Statistics]

3

1

0

4

3

Engineering Science Course

Programming for Problem Solving

3

1

6

7

4

Engineering Science Course

Workshop / Manufacturing Practices

0

0

4

2

5

Humanities and Social Sciences including Management

English II

2

0

2

3

6

Open elective

Open Elective I

Constitution of India/ Essence of Indian core knowledge

3

0

0

3

 

 

No. of hours

14

3

12

 

29 hours              Total Credits

23

Semester 3

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Basic Science Courses

Statistical Analysis using R

2

1

4

5

2

Professional Core Courses

Data Structure and Algorithms

3

1

4

6

3

Professional Core Courses

Digital Electronics    [up to microprocessor basics]

3

0

4

5

4

Professional Core Courses

Discrete Mathematics

3

1

0

4

5

Professional Core Courses

Numerical Methods

3

0

4

5

 

 

No. of hours

14

3

16

 

33 Hours                                         Total Credits

25

Semester 4

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Professional Core Course

Optimization Techniques

[LPP, Convex Hul, Basis]

3

1

0

4

2

Professional Core Courses

Computer Organisation & Architecture

3

0

4 Microprocessor Lab (8085 & 8086)

5

3

Professional Core Courses

Operating System

3

0

4

5

4

Professional Core Courses

Object Oriented Programming

3

1

4

6

5

Humanities and Social Sciences including Management

Management [Organisational Behaviour / Finance & Accounting]

3

0

0

3

 

 

No. of hours

15

2

12

 

29 Hours                                          Total Credits

23

Semester 5

 

SLNO

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Professional Elective Course

Introduction to Machine Learning

3

0

4

5

2

Professional Core Courses

Database Management System

3

0

4

5

3

Professional Core Courses

Compiler Design and Automata Theory

4

0

0

4

4

Professional Core Courses

Computer Network

3

0

4

5

5

Humanities and Social Sciences including Management Course

Engineering Economics

2

0

0

2

6

 

Internship

3

 

 

No. of hours

15

0

12

 

27hours                                          Total Credits

24

Semester 6

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Professional Elective Courses

Supervised Learning

3

0

4

5

2

Professional Core Course

Design and Analysis of Algorithm

3

0

4

5

3

Professional Elective Courses

Big Data Analytics with Hadoop / Spark

3

0

2

4

4

Professional Elective Courses

Unsupervised Learning

 

3

0

4

5

5

Project

Project I

0

0

6

3

 

 

No. of hours

12

0

20

 

32 Hours                                          Total Credits

22

Semester 7

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Professional Elective Course

Natural Language Processing

4

0

4

6

2

Professional Elective Courses

Data Pre-Processing and Visualization

3

0

4

5

3

Open Elective Courses.

Open Elective IV [Communicative English]

3

0

0

3

4

Professional Core Course

Software Engineering

3

0

0

3

5

Project

Project II

0

0

12

6

6

 

Internship

3

 

 

No. of hours

13

0

20

 

33 Hours                                          Total Credits

26

Semester 8

Sl No

Type of Course

Course title

Hours per week

Credits

Lecture

Tutorial

Practical

1

Project

Project III

0

0

30

15

2

Project

Seminar

3

3

 

Grand Viva

3

3

 

No. of Hours

0

0

30

 

                 30 Hours

21

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