Introduction to Machine learning

Machine Learning methods are at the core of many recent advances in “intelligent computing”. Current applications include machine perception (vision, speech recognition), control (process control, robotics), data mining, time-series prediction (e.g. in finance), natural language processing, text mining and text classification, bio-informatics, neural modeling, computational models of biological processes, and many other areas. This is an introductory undergraduate course on machine learning and its applications in different areas. The course will briefly cover techniques for visualizing and analyzing multi-dimensional data along with algorithms for projection, dimensionality reduction, regression, clustering, classification and neural networks, and deep learning. A main course objective is to present various approaches to classifier design so that students can make judicious choices when confronted with real pattern recognition problems. However, it is important to emphasize that the design of a complete pattern recognition system for a specific application domain requires domain knowledge, which is beyond the scope of this course. Students will use available tools and libraries to implement some algorithms using MATLAB.