B.E. | Artificial Intelligence and Data Science

Course Overview

1.What is Artificial Intelligence and Data Science specialization in Engineering?
Artificial Intelligence and Data Science is a new branch of study which deals with scientific methodologies, processes, and techniques drawn from different domains like statistics, cognitive science, and computing and information science to extract knowledge from structured data and unstructured data. This knowledge is applied in making various intelligent decisions in business applications. Artificial Intelligence and data science focuses on collecting, categorizing, strategizing, analyzing and interpretation of data. It is a specialised branch that deals with the development of data driven solutions, data visualization tools and techniques to analyse big data. It also incorporates the concepts of machine learning and deep learning model building for solving various computational and real world problems.

2. Who should study Artificial Intelligence and Data science study?
AI and Data science is the current trend ruling the business world and it is highly paid career now. Artificial Intelligence and data science is a suitable course for those who would like to develop various intelligent business solutions. Big data solutions has changed the way how business models to be built and run. This study contributes much in manufacturing, e-commerce, banking, finance, transport and healthcare industry.

3. What will I study in this course?
In this course, you will learn how to design, create and implement AI and DS based software solutions to solve actual business problems. This course helps to explore concepts such as AI, Data analytics, Data visualization, Machine Learning, Deep Learning, semantic web and social network analytics, Block chain Technologies, and Data Security and Privacy.

4.What are the career opportunities after the completion of this course?/What will I do once I graduate?
AI and DS graduates will be able to design, and develop intelligent business applications to solve various industrial problems. They use the latest tools and open source technologies to recommend the required solutions. They can figure out how to evaluate the ethical, legitimate, proficient and social standards of engineering knowledge and practices. These graduates can also exhibit their domain knowledge in data handling, knowledge extraction, mobile and distributed application development, intelligence web/ecommerce development, database administration, computer hardware, networking, education and training and decision support systems using AI and Data Science tools and techniques.

Programme Duration

Programme Duration
4 years (8 semesters)

Programme Type
Full-time

Eligibility Criteria

The candidate should have passed the 2nd PUC/12th/Equivalent Exam with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics along with Chemistry/Biotechnology/Biology/Electronics/Computers (40% for Karnataka reserved category candidates).

Candidate must also qualify in one of the following entrance exams: CET/ COMED-K/JEE/AIEEE

Course Structure

The electronics and communication engineering syllabus is as follows:

I & II Semester

  • Calculus and Linear Algebra
  • Engineering Physics
  • Basic Electrical Engineering
  • Elements of Civil Engineering and Mechanics
  • Engineering Graphics
  • Engineering Physics Laboratory
  • Basic Electrical Engineering Laboratory
  • Technical English-I
  • Engineering Chemistry
  • C Programming For Problem Solving
  • Basic Electronics
  • Elements of Mechanical Engineering
  • Engineering Chemistry Laboratory
  • C Programming Laboratory
  • Advanced Calculus and Numerical Methods
  • Technical English-II

III Semester

  • Transform Calculus, Fourier Series and Numerical Techniques
  • Data Structures and Applications
  • Analog and Digital Electronics
  • Computer Organization
  • Software Engineering
  • Discrete Mathematical Structures
  • Analog and Digital Electronics Laboratory
  • Data Structures Laboratory
  • Kannada/Constitution of India, Professional Ethics and Cyber Law

IV Semester

  • Complex Analysis, Probability and Statistical Methods
  • Design and Analysis of Algorithms
  • Operating Systems
  • Microcontroller and Embedded Systems
  • Object Oriented Concepts
  • Data Communication
  • Design and Analysis of Algorithms Laboratory
  • Microcontroller and Embedded Systems Laboratory
  • Kannada/Constitution of India, Professional Ethics and Cyber Law

V Semester

  • Management and Entrepreneurship for IT Industry
  • Python Programming
  • Database Management System
  • Automata Languages and Artificial Intelligence
  • Principles of Artificial Intelligence
  • Mathematics for Data Science
  • Artificial Intelligence Laboratory
  • DBMS Laboratory with miniproject
  • Environmental Studies

VI Semester

  • Machine Learning
  • Data Science and its applications
  • Java for Mobile applications
  • Professional Elective -1
  • Open Elective -A
  • Machine Learning Laboratory
  • Data Science Laboratory
  • Mobile Application Development Laboratory
  • Internship

VII Semester

  • Advanced Artificial Intelligence
  • Data Visualization
  • Professional Elective 2
  • Professional Elective 3
  • Open Elective – B
  • Visualization & DS Mini Project Laboratory
  • Internship
  • Project work Phase I

VIII Semester

  • Data Security and Privacy
  • Professional Elective-4
  • Project Work Phase II
  • Technical Seminar
  • Internship

ELECTIVE

Students can choose from the following electives:

PROFESSIONAL ELECTIVE-1

  • Natural Language Processing
  • Software project and management
  • Web Programming
  • Analysis on Big data

OPEN ELECTIVE-A

  • Mobile Application Development
  • Introduction to Data structures and Algorithms
  • Programming in Java
  • Introduction to Operating Systems

PROFESSIONAL ELECTIVE-2

  • Internet of Things
  • Blockchain Technology
  • Advanced Data Analytics
  • Cloud Computing and Virtualization

PROFESSIONAL ELECTIVE-3

  • Fuzzy Logic & its Applications
  • Image processing
  • Semantic Web and Social Network
  • Business Intelligence

OPEN ELECTIVE-B

  • Introduction to Big Data Analytics
  • Python Application Programming
  • Introduction to Artificial Intelligence
  • Introduction to Dotnet Framework for Application Development

PROFESSIONAL ELECTIVE-4

  • System Modelling and Simulation
  • Soft and Evolutionary Computing
  • Robotic Process Automation Design and Development
  • Deep Learning

Evaluation Criteria

TESTS

  • The Continuous Internal Evaluation (CIE) is prescribed for maximum of 40 marks. Marks prescribed for test shall be 30 and for assignment is 10. The CIE marks for test in a theory Course shall be based on three tests and generally conducted at the end of fifth, tenth and fourteenth week of each semester. Each test shall be conducted for a maximum of 30 marks and the final marks shall be the average of three tests. However, to support slow learners, improvement tests will be carried out to help them gain the average. The remaining 10 marks shall be awarded based on the evaluation of Assignments/ Unit Tests/ written quizzes that support to cover some of the Course/programme outcomes. Final CIE marks awarded shall be the sum of test marks and assignment marks making a maximum of 40 marks.
  • In the case of Practical, the CIE marks shall be based on the laboratory journals/records (30 marks for continuous evaluation based on conduct of experiment, viva and report writing and one practical test (10 marks) to be conducted at the end of the semester.
  • The IA marks in the case of Mini Project (in 5th Semester), Projects and Seminars in the final year shall be based on the evaluation at the end of 8th semester.

ASSIGNMENTS

  • Assignments are given to students after completion of each unit of the syllabus and comprehensively cover all of the important aspects of each topic in a particular unit.
  • Completing the prescribed assignments will greatly help students prepare for the internal assessments and the final exams. All the assignments will be evaluated and based on the performance of the students marks will be awarded for each course.
  • The Student Assistant for the course will neatly script solutions to assignments, and after due checking and correction by faculty, these solutions will be scanned and made available on the faculty webpage for all students to access and download.

Information & Downloads