M. Tech. in Machine Learning and Intelligent Systems

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Overview

The programme introduces the principles and working of machine learning and intelligent systems. It enhances the students’ knowledge on design, development and testing of various machine learning algorithms and systems using industry standard tools. It is structured in such a way that the students will be able to build intelligent systems and evaluate machine learning algorithms for domain specific applications.

Objective

The objectives of the programme are to train and educate the students on the following:

  • State-of-the-art machine learning algorithms and intelligent systems
  • Principles and concepts of pattern recognition, computational intelligence, probabilistic graphical models, neural networks and deep learning algorithms
  • Development of algorithms for machine learning tasks
  • Implementation of machine learning algorithms using industry standard tools
  • Construction and adaptation of intelligent systems using machine learning algorithms for domain specific applications
  • Industry practice, process and standards giving general perspectives as well as opportunities for a career in development of machine learning algorithms

Highlights

  • Elective Specialisation

    Elective courses are so designed as to offer a specialisation such as AI for Healthcare or AI for Robotics. A student may register for up to two elective courses online based on his or her interests in AI and Machine Learning

  • International Competitions

    Students can also earn 3 or 4 credits by participating in international competitions like technical presentations, conferences and publications in journals and winning awards. Students who achieve this can be exempted from one of the elective courses in the program

  • Group Project

    The main objective of a group project is to build a product prototype as a part of a team and to experience and solve problems that arise in integration of components. A group shall have up to 5 students and the project may be interdisciplinary. Students are encouraged to innovate and file patents

  • Industry Internship or Project

    A student can opt for an internship in an industry, a business or research organisation. Alternately, students can undertake a mini-project which requires self-directed study that can be pursued with an affiliated faculty member. The internship needs to be approved by the HoD and Dean and students have to submit a report and make a presentation to a panel constituted by the HoD for assessment

  • Dissertation and Publication

    Each student gets an opportunity to perform research work in a chosen AI topic with well-defined objectives, carrying out literature survey and addressing a few gaps. Students are encouraged to publish their results. It is Ramaiah University’s experience that a dissertation helps students in both an industrial career as well as a research career

  • Application based learning program
  • Research opportunities
  • Internship, training and placement support with various worldwide universities and industries
  • Faculty with PhD degrees in machine learning will advise students on group projects and dissertation work.
  • Includes expert guest lectures from various institutions and industries.
  • Includes workshops, industry visits, group discussions and debates.
  • Ensures small batches to encourage in-depth learning

  • We offer accommodation in student hostels on our campus, and even offer third-party off-campus hotels where necessary to ensure students can focus on what matters the most. 
  • Our on-campus facilities include WiFi any time of the day, 24/7.
  • From a host of eateries and laundry facilities, we ensure that students have access to all facilities.
  • We encourage students to participate in co-curricular activities on campus which include sports and cultural events, both at a university level and a faculty level.
  • We help students focus on independent and sponsored research with projects and a dissertation. 

Structure

Study Domains

  • CC

    Core Courses

    A rich set of core courses in Programmeming for Data Science, Data Mining, Data processing and Advanced Data Processing with case studies using tools based on Map Reduce Paradigm

  • EC

    Elective Courses

    Includes electives in Artificial Intelligence, Text Mining, Big Data and Healthcare, Big Data and SDN and Natural Language Processing

  • PSI

    Project, Seminar, and Internships

    Active participation in output-based group projects, seminars, and internships in industry. Students will work in a team and exhibit the product developed and also develop a thesis by investigating into a problem in Data Science and Engineering

Course Progression

Course Credits
Mathematics for Machine Learning

Theory

4
Programmeming for Machine Learning

Theory Practicals

4
Data Mining

Theory Tutorials

4
Artificial Intelligence

Theory Practicals

4
Computational Intelligence

Theory Practicals

4
Research Methodology & IPR

Theory

2
Professional Communication

Theory

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Artificial Neural Networks

Theory Tutorials

5
Pattern Recognition
5
Deep Learning

Theory Practicals

4
Probabilistic Graph Models

Theory Tutorials

4
AI for Healthcare

Theory Tutorials

4
Value Education

Theory

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Internship
4
Group Project
8
Dissertation and Publication Phase 1

Key

  • Theory
  • Tutorials
  • Practicals

Course Credits
Dissertation and Publication Phase 2
24

Key

  • Theory
  • Tutorials
  • Practicals

Details

Teaching and Assessment

Courses are self-contained to a great extent and students are encouraged to interact with faculty members so as to enhance their knowledge. Assignments set in courses provide real-life scenarios to be addressed and analysed. Teaching and evaluation methodology is to bring out the potential in a student and also encourages them to improve their communication skills by presenting the work carried out in assignments and projects

Key Skill Development

  • Ability to apply concepts in AI and Machine Learning to various social problems.
  • Ability to analyze the performance of various pattern recognition and intelligent algorithms.
  • Ability to develop machine learning algorithms using statistical learning, probabilistic graphical models, neural networks and deep learning.
  • Ability to apply machine learning algorithms to specific applications in computer vision, speech processing, text mining and biomedical image processing.
  • Ability to evaluate machine learning algorithms based on performance metrics. 
  • Ability to implement machine learning algorithms for various domain-specific applications.
  • Ability to use standard simulation software tools to build machine learning algorithms and intelligent systems.
  • Ability to perform tests to evaluate pattern recognition and machine learning algorithms.
  • Ability to construct and adapt intelligent systems for various domain specific applications using machine learning algorithms.
  • Ability to manage information, develop technical reports and make presentations.
  • Ability to build, manage and lead teams to successfully complete projects and communicate across teams and organisations to achieve professional objectives. 
  • Ability to work under various constraints to meet project targets.
  • Ability to adapt to the chosen profession by continuously upgrading knowledge and understanding through lifelong learning. 

Admissions

Eligibility

Candidates must have a

  • BE or B. Tech or equivalent degree in:
    • Electronics and Communication Engineering;
    • Computer Science and Engineering; 
    • Electrical and Electronics Engineering; 
    • Automobile/​Automotive Engineering;
    • Mechanical Engineering
    • Aerospace/​Aeronautics Engineering;
    • Civil Engineering;
    • Bio-Medical Engineering;
    • Mechatronics;
    • Information Science;
    • Telecommunication Engineering
    • Instrumentataion Engineering; or
    • Medical Electronics

Candidates who belong to SC/ST categories are given a different qualifying mark as per government notifications.

Application Process

University Quota

Candidates seeking admission are required to contact the admissions department for more details.

Through Government of Karnataka Quota 

  1. PGCET, Counselling and Selection (PGCET Code T945)
  2. Download the admission form 
  3. Submit completed form and documents at the University Admissions Office
  4. Pay fees online or through DD at the University admission Office. See full instructions.

Fees & Scholarships

Government Seats:

  • As per Government of Karnataka for Indian nationals

University Seats:

  • Rs 2,13,550 per annum for Indian nationals

Eligibility

  • Candidates should have the equivalent qualification approved by Association of Indian Universities.
  • Should have proof of proficiency in English with a minimum TOEFL score of 8

Application Process

University Quota

Candidates seeking admission are required to contact the admissions department for more details.

Through Government of Karnataka Quota

  1. PGCET, Counselling and Selection (PGCET Code T945)
  2. Download the admission form
  3. Submit completed form and documents at the University Admissions Office
  4. Pay fees online or through DD at the University admission Office. See full instructions.

Fees & Scholarships

NRI/ Foreign Students:

  • USD 6000 + Other fee Rs.38,550 per annum

Start your journey with RUAS

Contact
Person
Mr. S L Narasimhan
Email
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Phone
+91 80 4536 6616