B.Tech in Artificial Intelligence and Machine Learning

B.Tech in Artificial Intelligence and Machine Learning

B.Tech in Artificial Intelligence and Machine Learning

Duration:4 years, Eight semesters (Admissions open for the batch 2020-21).

Eligibility: 

Pass in 10 + 2 / 12th Standard with 45% marks (40% in case of candidate belonging to SC/ST category).

Lateral Entry: 

The candidates, who have successfully completed 3 year diploma in Engineering, are eligible to apply for lateral entry into 2nd year of B.Tech. Courses .Candidates will be admitted to second year of the programme only after appearing the Srinivas University selection process for engineering programme.

About B.Tech in Artificial Intelligence and Machine Learning:

The B.Tech in Artificial Intelligence and Machine Learning is specialisation program designed to enable students to build intelligent machines, software, or applications with a cutting-edge combination of machine learning, analytics and visualisation technologies. The course will include study of algorithms, signal processing, robotics and mathematical foundations, AI methods based in different fields, including neural networks, data mining, in order to present an integrated treatment of machine learning problems and solutions. The course also provides abundant opportunities to students to work on self-designed mini-projects, develop communication skills, explore internship opportunities in industry and take part in national and international conferences and circuit/Software design contests. The department is committed to promote research, industrial interaction and multi-dimensional development of the students with theoretical as well as practical exposure.

Special Features of the Program:

  • State of art facilities with modern multimedia lecture & seminar halls.
  • Well-equipped laboratories with modern instruments with latest technology.
  • Continuously upgraded laboratories for hands on training.
  • Good study materials will be provided for every subject.
  • Industry oriented syllabus with special focus on hands on training.
  • Project/Mini project in each semester.
  • Innovations in examination system with opportunity for personal seeing of evaluated papers.
  • Make-up exams in every semester to avoid year loss.
  • Placement support and research oriented projects for every student.
  • Focus on Soft Skill Development & Training on competitive exams.
  • Regular Technical Seminars by experts.
  • Interaction with Industries, R & D organizations.
  • Regular Industrial Visits.
  • Separate Hostel & Transport facility for Boys & Girls.

Career Opportunities:

Graduation in Artificial Intelligence and Machine Learning provides career roles as

  • Data Scientist
  • Data Engineer
  • Business Analyst
  • Data Analyst
  • IoT/AI/ML Engineer
  • Business Intelligence Engineer
  • Research Scientist
  • Further Opportunity to pursue M.Tech.
  • Opportunity to appear for GATE/Engineering Services and other competitive Exams.

Course Structure:

SEMESTER 1 SEMESTER 2
S. No. Subject Credit/ Marks S. No Subject Credit/ Marks
1 Engineering Physics of Materials 4/100 1 Engineering Chemistry of Materials 4/100
2 Computer Software Concept & Programming 4/100 2 Information Communication & Computation Technology 4/100
3 Elements of Electrical & Electronics 4/100 3 Elements of Mechanical and Civil Engineering 4/100
4 Quantitative Techniques in Engineering –I/II 4/100 4 Quantitative Techniques in Engineering –I/ II 4/100
5 Lab on Engineering Physics of Materials 1.5/100 5 Lab on Engineering Chemistry of Materials 1.5/100
6 Electrical & Electronics Lab 2/100 6 Computer Aided Engineering Drawing Lab 2/100
7 Lab on Computer Programming 1.5/100 7 Lab on Spreadsheet Programming 1.5/100
8 Technical English (ESEP – Xlanz) 2/50 M 8 Professional English ( ESEP – Xlanz) 2/50 M
9 Principles of Environmental Studies 2/50 M 9 Constitution & Professional Ethics 2/50 M
10 Kannada/ Co-curricular Activities/Sports (ESEP) - 10 Kannada/ Co-curricular Activities/Sports (ESEP) -
  Total Credit 25/800   Total Credit 25/800
SEMESTER 3 SEMESTER 4
S. No. Subject Credit/ Marks S. No Subject Credit/ Marks
1 Numerical Techniques and Integral Transforms 4/100 1 Probability theory and Statistical Methods 4/100
2 Data structures and Applications 4/100 2 Design and Analysis of Algorithms 4/100
3 Analog & Digital Electronic Circuits 4/100 3 Object Oriented Concepts 4/100
4 Microprocessors & Embedded Systems 4/100 4 Sensors and Sensor Applications 4/100
5 Analog & Digital Electronics Lab 1.5/100 5 Design and Analysis of Algorithms Lab 1.5/100
6 Data structures lab 2/100 6 Database Management Lab 2/100
7 Microprocessors Lab 2/100 7 Computer Graphics and Visualization Laboratory 2/100
8 ESEP – Python Programming 1.5/50 M 8 ESEP- Object Oriented programming Lab 1.5/50 M
9 ESEP-Xlanz 2/50 M 9 ESEP-Xlanz 2/50 M
10 Co-curricular Activities/ Sports - 10 Co-curricular Activities/ Sports -
  Total Credit 25/800   Total Credit 25/800
SEMESTER 5 SEMESTER 6
S. No. Subject Credit/ Marks S. No Subject Credit/ Marks
1 Automata Languages and Artificial Intelligence 4/100 1 Advanced AI and ML techniques 4/100
2 Computer Networks 4/100 2 Web applications using Machine Learning Techniques 4/100
3 Core-Elective -1 4/100 3 Core-Elective -2 4/100
4 Machine Learning with Python 4/100 4 Digital Image Processing 4/100
5 AI and Machine Learning with Python Laboratory 2/100 5 Image Processing Lab 2/100
6 Computer Network Lab 1.5/100 6 Web applications Lab 1.5 /100
7 Microcontrollers & Embedded Systems Lab 2/100 7 Advanced AI and ML Lab 2/100
8 ESEP – IPR in EC 1.5/50 8 ESEP – Patent Analysis 1.5/50
9 ESEP-Xlanz 2/50 9 ESEP-Xlanz 2/50
10 Co-curricular Activities/ Sports - 10 Co-curricular Activities/ Sports -
Core Electives –1 Core Electives –2
S. No. Subject Credit/ Marks S. No Subject Credit/ Marks
1 Operating Systems 4/100 1 Designing Embedded Systems 4/100
2 Natural Language Processing 4/100 2 IOT Technology & Applications 4/100
3 Advanced Computer Graphics with Virtual Reality 4/100 3 Data Warehousing and Data Mining 4/100
  Total Credit 25/800   Total Credit 25/800
SEMESTER 7 SEMESTER 8
S. No. Subject Credit/ Marks S. No Subject Credit/ Marks
1 Robotic Process Automation 4/100 1 Project with applied patent 21/700
2 Internet of Things with Machine Learning Seminar 2/50
3 Virtual Reality 4/100 4 Patent filing 2/50
4 Core Elective - 3 4/100 4 ESEP–ABC Skill Trainer(optional)
5 Mobile Application development Laboratory 1.5/100 5
6 Internet of Things Laboratory Lab 2/100 6
7 Lab on Core Elective 1.5/100 7
8 ESEP –Project Phase -1 2/50 8
9 ESEP-Xlanz 2/50 9
10 Co-curricular Activities/ Sports - 10 -
  Total Credit 25/800   Total Credit 25/800
Electives: SEMESTER 7
Core Elective –3
Sl NO Subject Credit/ Marks
1 Block chain 4/100
2 Cloud computing 4/100
3 Multimedia Processing 4/100

Note :
(1) University Registration Fee, Eligibility Fee for other State students, Examination Fee, Uniform Fee, Hostel Fee, Industry visit fee, Internship fee, Transportation fee, Sports & games fee, Extra-curricular activity fee, etc., are extra. The course fees mentioned for subsequent years may vary annually up to 6%.
(2) The University Marketing Team will help the admitted students to avail of Education Loans, Scholarships, etc.
(3) The University Placement & Training Team will assist the admitted students to get (a) Earn While Learn, (b) Industry Internship, and (c) Final Job Placement.