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

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

School of Engineering and Technology (SET)

  • Program Code

    00001

  • Level

    Graduate

  • Duration

    4 Years

About the Programme

B. Tech in Computer Science (Artificial Learning and Machine Learning) is an undergraduate programme with advanced learning solutions imparting knowledge of advanced innovations like machine learning, often called deep learning and artificial intelligence. 

This specialisation is designed to enable students to build intelligent machines, software, or applications with a cutting-edge combination of machine learning, analytics and visualisation technologies. The main goal of artificial intelligence (AI) and machine learning is to program computers to use example data or experience to solve a given problem. Many successful applications based on machine learning exist already, including systems that analyze past sales data to predict customer behaviour (financial management), recognize faces or spoken speech, optimize robot behaviour so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. 

This programme discusses AI methods based in different fields, including neural networks, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions.

This course is for individuals who...

This programme is for students who want to acquire the ability to design intelligent solutions to problems in a variety of domains and business applications and fields such as natural language processing, text mining, robotics, reasoning and problem-solving

Students who are looking for...

a career with exceptional prospective fields and challenging roles in a futuristic industry. The financial security, the freedom to relocate, lifelong prospects of learning makes this programme an ideal career for computer science enthusiasts.

Course Fee
Fee Per Semester Fee Per Year
For National Students 92 500 1 79 000
For International Students 1950 3800
Course Structure

S.No.

Course Code

Course Title

L

T

P

Credits

1

CSP101

Introduction to Artificial Intelligence and Machine Learning

0

0

2

1

2

CSE113/CSP113

Programming for Problem Solving

3

0

2

4

3

SC4

Maths I(Bucket based)

3

1

0

4

4

SC8

Engineering Physics

2

1

2

4

5

SC12

Computer Aided Design & Drafting

0

0

3

1.5

6

SC11

Principles of Electrical and Electronics Engineering

2

1

2

4

7

SC16

Soft Skill - 1

0

0

4

2

 

Credits in Term 1

 

 

 

20.5

II. Syllabus for the Courses:

Introduction to Computer Science and Engineering: This course focuses application areas of Computer Science and Engineering for students admitted in undergraduate program. The purpose of B. Tech. in Computer Science & Engineering is to be given through this course to students.

Programming for problem solving: Programming for problem solving gives the Understanding of C programming and implement code from flowchart or algorithm

S.No.

Course Code

Course Title

L

T

P

Credits

1

CSE114/CSP114

Application based Programming in Python

3

0

2

4

2

SC5

Maths II(Bucket based)

3

1

0

4

3

SC9

Advanced Physics (Bucket based)

2

1

2

4

4

SC10

Engineering Chemistry

3

0

2

4

5

SC13

Mechanical Workshop

0

0

3

1.5

6

SC17

Soft Skill - 2

1

0

2

2

7

CSP103

(Design/creativity) Multimedia Application Lab

0

0

2

1

 

Credits in Term 2

 

 

 

20.5

Application Based Programming in Python : Python is a language with a simple syntax and a powerful set of libraries. It is widely used in many scientific areas for data exploration. This course is an introduction to the Python programming language for students without prior programming experience. We cover data types control flow object-oriented programming.

Multimedia and Animation Lab: This course is an overview of the modern technologies used for the Web development.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC18

CTS-1 Building Essential Language and life skills

0

0

4

2

2

SC14

Introduction to Biology for Engineers

2

0

0

2

3

MTH201

Discrete Structures

3

1

0

4

4

CSE247

Computer Organization and Architecture

3

0

0

3

5

CSE242/CSP242

Data Structures Using C

3

0

2

4

6

CSE243/CSP243

OOPS Using Java

3

0

2

4

7

PC3

Project Based Learning (PBL) -1

0

0

2

1

8

  PC4

Industrial Internship

0

0

2

1

 

CSA201

Soft computing

3

0

0

3

 

Credits in Term 3

 

 

 

24

Object Oriented Programming Using JAVA: Basic  Object Oriented Programming  (OOP) concepts including objects   classes methods parameter passing information hiding inheritance and polymorphism are introduced and their implementations using Java  are discussed.

Data Structures: This course  starts with an introduction to data structures with its classification efficiency of different algorithms array and pointer based implementations and Recursive applications. As the course progresses the study of Linear and Non-Linear data structures are studied in details. The course talks primarily about Linked list stacks queue Tree structure Graphs etc. This Course also deals with the concept of searching sorting and hashing methods.

Computer Organization and Architecture: This course discusses the basic structure of a digital computer and used for understanding the organization of various units such as control unit Arithmetic and Logical unit and Memory unit and I/O unit in a digital computer.

Soft Computing: This course introduces soft computing theories techniques and tools. Those are frequently required for understanding and developing the exploratory data analysis techniques and knowledge discovery and intelligent systems.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC19

CTS-2 Communicate to conquer

0

0

4

2

2

CSE244/CSp244

Principles of Operating System

3

0

2

4

3

CSE245/CSP245

Computer Networks

3

0

2

4

4

CSE246/CSP246

Data Base Management System

3

0

2

4

5

SC23

Management Course (from basket)

3

0

0

3

6

PC14

Project Based Learning (PBL) -2

0

0

2

1

7

SC7

Environmental Science

2

0

0

2

8

CSP201

Advance Java Lab

0

0

2

1

 

CSA202

Pattern Recognition

3

0

0

3

 

Credits in Term 4

 

 

 

24

Computer Network: To familiarize with the basic taxonomy and terminology of computer networking area.

Data Base Management systems: This course introduces database design and creation using a DBMS product. Emphasis is on normalization data integrity data modeling and creation of simple tables queries reports and forms. Upon completion students should be able to design and implement normalized database structures by creating simple database tables queries reports and forms.

Principles of Operating system: This course introduces the design principles of operating systems resource management identifying challenges and applying respective algorithms.     

  Advance Java Programming Lab: This course is designed to meet the needs of those who want to be professional Java developers. Students should be familiar with Java programming techniques and should be comfortable with concepts such as Classes Objects Inheritance Interfaces I/O Streams Threading and Networking.

Pattern Recognition: This course introduces neural computational paradigm for critical & implementable understanding of feature engineering.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC21

CTS-4 Ace the Interview

0

0

4

2

2

CSE344/CSP344

Compiler Design

3

0

2

4

3

CSE346/CSP346

Artificial Intelligence

3

0

2

4

4

CSP308

Technical Skill Enhancement Course-2(Statistical Analysis Lab)

0

0

2

1

5

PE2

Program Elective-2

3

0

0

3

6

PE3

Program Elective-3

3

0

0

3

7

PC16

Project Based Learning (PBL) -4

0

0

2

1

8

OE2

Open Elective - 2

3

0

0

3

 

CSA302

Neural Networks

3

0

0

3

 

Credits in Term 7

 

 

 

24

Compiler Design: To provide students with an overview of the issues that arise in Compiler construction as well as to throw light upon the significant theoretical developments and tools that are deep rooted into computer science.

Artificial Intelligence: This course introduces basic aspects of Artificial intelligence comparing the AI and conventional solutions to real world problems utilizing and analyze AI techniques for identifying optimal solutions to search strategies.

  Android Application Development: The course will introduce concepts of the Android platform Android application components Activities and their lifecycle UI design. It will also help students to build applications according to their problem statements.

  Introduction to Cloud Computing: This course introduces advanced aspects of Cloud Computing encompassing the principles to analyze the cloud identify the problems and choose the relevant models and algorithms to apply.

Software Project Management: This course introduces concepts of software project management in which Project Planning Project Evaluation Software Effort estimation Monitoring and control and Managing contracts tools and techniques are included.

Web Designing: This course is an overview of the modern Web technologies used for the Web development. The purpose of this course is to give students the basic understanding of how things work in the Web world from the technology point of view.

Natural Language Processing: This course introduces concepts of Basic  concepts of Natural Language and Grammar.

Information Retrieval: This course introduces neural computational paradigm for critical & implementable understanding for pattern based problem areas.

Neural Networks: This course introduces neural computational paradigm for critical & implementable understanding for pattern based problem areas.

S.No.

Course Code

Course Title

L

T

P

Credits

1

PE4

Program Elective-4

3

0

0

3

2

CSE458/CSP458

Web Technologies

3

0

2

4

3

SC26

Major Project- 1

0

0

6

3

4

SC22

Comprehensive Examination

0

0

0

0

5

SC28

Professional Ethics and Values

0

0

0

0

6

SC25

Industrial Internship

0

0

2

1

7

OE3

Open Elective - 3

2

0

0

2

8

 

CTS-5 Campus to corporate

0

0

2

1

 

CSA401

Introduction to Deep Leaning

3

0

0

3

 

Credits in Term 7

 

 

 

17

Web Technology:  The purpose of this course is to give students the basic understanding of how different computers and devices to communicate and share resources as well as to give the basic overview of the different technologies.

Digital Image Processing: This course is to study the image fundamentals and mathematical transforms necessary for image processing.

Wireless Network : Overview of wireless network architectures including cellular networks local area networks multi-hop wireless networks such as ad hoc networks mesh networks and sensor networks capacity of wireless networks medium access control routing protocols and transport protocols for wireless networks mechanisms to improve performance and security in wireless networks energy-efficient protocols for sensor networks.

  Mobile Computing: This course will give you an understanding of mobile computer systems particularly in the context of wireless network systems such as 2G/3G/4G mobile telephony data networks and other wireless networks and infrastructure.

Software Testing: This course will examine fundamental software testing and related program analysis techniques. The important phases of testing will be reviewed emphasizing the significance of each phase when testing different types of software. The course will also include concepts such as test generation test coverage regression testing mutation testing program analysis (e.g. program-flow and data-flow analysis) and test prioritization.

Digital Image Processing: This course will give you an understanding the image fundamentals and mathematical transforms necessary for image processing.

Introduction to Deep Leaning: This course introduces neural computational paradigm for critical & implementable understanding for automated learning based problem areas.

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC27

Major Project - 2

0

0

18

9

2

PE6

Program Elective-5

3

0

0

3

3

PE5

Program Elective-6

3

0

0

3

4

OE4

Open Elective - 4

3

0

0

3

5

  SC

Universal Human Value & Ethics

2

0

0

2

 

CSA402

Robotics and Intelligent Systems

3

0

2

4

 

Credits in Term 8

 

 

 

20

PE-1

PE-2

PE-3

PE-4

PE-5

PE-6

                                                                                                                                                                  Introduction to Mathematical & Statistical Techniques in Computer Science                                                CSE348                             

Introduction to Cloud Computing  CSE351

Information Retrevial CSA304

Digital Image Processing CSA403

Fuzzy Logic CSA404

Intelligent Agents
CSA405

Introduction to Graph Theory and its Applications CSE349

Natural Language Processing CSA303

 

 

 

Introduction to Internet of Things CSI201

Program Elective List

 

Total Credits: __180___

Minimum Credits essential for the Programme:__180___

 

 

Distributed System Concepts & Design: This course covers issues and solutions related to the design and the implementation of distributed algorithms for different issues of distributed system.

  Introduction to Internet of Things: This course introduces Concepts for internet of things and how we can embed it into our daily lives for the development of life style. It will also help students to build applications according to their problem statements.

Fuzzy Logic: This course introduces Basic  concepts of Set Theory with functions and relational model.

Intelligent Agents:

Robotics and Intelligent Systems

S.No.

Course Code

Course Title

L

T

P

Credits

1

SC20

CTS-3 Impress 2 Impact

0

0

4

2

2

CSE341/CSP341

Design and Analysis of Algorithm

3

0

2

4

3

CSE342

Theory of Computation

3

1

0

4

4

CSE343

Software Engineering and Testing Methodologies

3

0

0

3

5

PE1

Program Elective-1

3

0

0

3

6

CSP302

Technical Skill Enhancement Course-1(LINUX Programming Lab)

0

0

2

1

7

PC15

Project Based Learning (PBL) -3

0

0

2

1

8

OE1

Open Elective - 1

3

0

0

3

9

  PC5

Industrial Internship-II

0

0

2

1

 

CSA301

Introduction to Machine Learning

3

0

2

4

 

Credits in Term 5

 

 

 

26

Theory of computation: The course introduces some fundamental concepts in automata theory and formal languages including grammar finite automaton regular expression formal language pushdown automaton and Turing machine. Not only do they form basic models of computation they are also the foundation of many branches of computer science e.g. compilers software engineering concurrent systems etc. The properties of these models will be studied and various rigorous techniques for analyzing and comparing them will be discussed by using both formalism and examples.

Software Engineering and Testing Methodologies: The objective of this course is to provide fundamental knowledge of software engineering and make student aware of best software engineering practices and contemporary software engineering tools.

  Linux Programming Lab: The course is designed to make the students research/industry ready as by using the open source applications along with any of the Linux flavor operating systems.

Design and Analysis of Algorithms: This course introduces concepts related to the design and analysis of algorithms. Specifically it discusses recurrence relations and illustrates their role in asymptotic and probabilistic analysis of algorithms. It covers in detail greedy strategies divide and conquer techniques dynamic programming and max flow - min cut theory for designing algorithms and illustrates them using a number of well-known problems and applications.

Introduction to Graph Theory and its Application: This course is to teach students the basic graph theory concepts and their applications in computer science.

    Mathematical and Statistical Techniques: In this subject the fundamental concepts and principles of Mathematical & Statistical Techniques together with the challenging issues in Computer Science software development will be introduced.  Discussion on various topics related to mathematics and Computer Science will also be conducted.  

Introduction to Machine Learning: This course introduces computational learning paradigm for critical & implementable understanding for supervised and unsupervised learning based problem areas.

Eligibility Criteria
For National Students
  • Matriculation with-60% marks Sr. Secondary (10+2) 70% marks (Aggregate) & minimum 60% marks in PCM Computer Science/PCB for Biotechnology without gap between 10th and 12th.

Minimum 50% marks in Maths

+

  • SUAT (Online Test) Followed by Personal Interview

Or

  • JEE All India Rank upto 3 Lakh

Or

  • UPSEE All India Rank upto 50 000
For International Students The eligibility criterion for all programs for international applicants is minimum 50% in the qualifying examination and having studied the pre-requisite subjects for admission in to the desired program.
Career path you can choose after the course
  • Machine Learning Engineer 
  • Data Scientist 
  • Artificial Intelligence Engineer 
  • Data Analyst 
  • Machine Learning Architect 
  • Amazon
  • CapGemini
  •   Mphasis 
  • NTTData 
  • Quickheal 
  • SYNTEL 
  • TCS 
  • Vivo Mobile 
  • Wipro

Take the next step towards a career in engineering.

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