Machine learning is the science of developing statistical methods that quantify relationships within data. This branch of mathematics/computer science has seen an explosive growth over the past decade as our ability to store and process digital data has dramatically increased. Prediction, classification, regression, and identification are the aims of learning from data. All of these problems are routinely performed in data analytic’s.

To obtain an overview of the literature in learning-based methods and applications.

To obtain an understanding of a variety of machine learning techniques for classification, regression, and prediction.

To obtain the ability to implement and experiment with a wide range of machine learning algorithms in Python with examples.

To apply: Unsupervised and Supervised learning and clustering concepts, Dimensional reduction, Kernels and kernel-based classifiers such as SVM, and Deep Learning algorithms.

To understand and implement learning-based methods for classification of images, signals and features.

Prerequisites: Participants are expected to have a working knowledge of the UNIX/Linux environment or should have taken Cluster computing course from HPE DSI dept.

Dates: 9th June 2020 - 14th July 2020

Time: Tue Thurs 1:00 PM to 2:30 PM

Instructor: Dr. Pablo Guillen-Rondon

Location: AERB 200, 202

Class Capacity: 44