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Program Name M.Tech in Machine Learning
Level Post Graduation
Program Specific Enquiries 0863-2344731
Admission Enrollment and General Enquiries 0863-2344777, 1800-425-2529
HoD Contact Number 9000239485
Learning Objectives of the Course:

This course provides a broad introduction to machine learning. Topics include: supervised learning (discriminative/generative learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); learning theory; ensemble learning. We will also cover sequential learning problems and algorithms. Lectures will discuss general issues in these topics and well-established algorithms, both from a computational aspect and a statistical aspect.

  • Formulate machine learning problems related to different applications.
  • Discussions on machine learning algorithms along with their pros and cons.
  • Understand the theory underlying machine learning.
  • Able to apply machine learning algorithms to solve problems of moderate complexity.
  • Able to read current research papers and understand the issues raised by current research.

Outcomes of the Course:

  • The students will be able to describe why a particular model is appropriate in a given situations, formulate the model and use it appropriately.
  • The student will be able to analytically demonstrate how different models and different algorithms are related to one another.
  • Students will be able to implement a set of practical methods, given example algorithms in MATLAB/other tools, and be able to program solutions to some given real world machine learning problems, using the toolbox of practical methods presented in the lectures.
  • Given a particular situation, students will be able be able to justify why a given model is appropriate for the situation or why it is not appropriate. Students will be able to developing an appropriate algorithm from a given model, and demonstrate the use of that method.
  • Students will be able to design and compare machine learning methods, and discuss how different methods relate to one another and will be able to develop new and appropriate machine learning methods appropriate for particular problems. Given a complex problem, students will be able to:
    • (a) identify sub-problems that are amenable to solution using Machine Learning techniques
    • (b) provide solutions to those sub-problems, and evaluation of the solutions.


I Year

I Semester

CS621 Data Structures and Algorithms
CS641 Fundamentals of Image Processing
CS659 Machine Learning
CS661 Advanced Data Mining Techniques
  Electives-I
CS626 Cloud Computing
CS663 Predictive Analytics
CS665 Web Intelligence
  Electives-II
CS667 Optimization Techniques
CS649 Advanced Computer Graphics
CS602 Modern Compiler Design
                   Labs :
CS623 Data Structures and Algorithms Lab
CS669 Machine Learning Lab
                  

I Year

II Semester

CS636 Natural language Processors
CS638 Artificial Intelligence
CS658 Computer Vision
CS660 Fuzzy Set Theory and Evolutionary Algorithms
  Electives-III
CS606 Big Data Analytics
CS662 Bio Informatics
CS642 Biometrics
  Electives-IV
CS646 Neural Networks
CS630 Information Retrieval Systems
CS664 Search Engines
                   Labs :
CS666 Machine Intelligence Lab
CS650 Natural Language Processing lab
                  

II Year

III Semester

  Seminar
  Project Work Phase - I
                  

II Year

IV Semester

  Project Work Phase - II

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