- Course Enquiry
- Course Description
- Course Structure
|Program Name||M.Tech in Machine Learning|
|Program Specific Enquiries||0863-2344731|
|Admission Enrollment and General Enquiries|| 0863-2344777, 1800-425-2529
|HoD Contact Number||9000239485|
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.
- 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.
|Project Work Phase - I|
|Project Work Phase - II|