Machine Learning for Computer Vision Applications MLCVA 19

In this course we intend to introduce the basic concepts of computer vision and machine learning from a mathematically well motivated perspective. This course covers some of the most popular supervised learning algorithms including linear regression, decision trees, k-nearest neighbor, Bayesian learning, support vector machines, neural networks with an introduction to Deep Learning and unsupervised clustering algorithms. We also discuss about, how machine learning algorithms are applied to various computer vision problems. This course also includes hands-on problem solving with programming in Python and some tutorial sessions. Eminent resource persons will be handling the sessions.

Introduction to Computer Vision
Image Processing Techniques
Machine Learning Fundamentals
Regression and Classification
Bayesian Learning & Instance based Learning
Support Vector Machines
Perceptron & Neural Networks
Unsupervised Learning Techniques
Convolutional Neural Networks
Machine Learning Applications in Signal & Image Processing

Last date for Receipt of Applications : 30-11-2019
Intimation of Selection (By E-mail) : 02-12-2019
Confirmation of participation by the applicants : 04-12-2019

FT Research Scholar / PG Students - Rs.1200/-
Faculty / PT Research Scholar - Rs.1500/-
Industry Persons - Rs.2500/-

In-campus accommodation will be provided to the participants
on request.

Accommodation Fee : Rs.100/- per day.