Machine Learning with Data Visualization

This course provides an understanding of the role of machine learning in computer science and artificial intelligence. Topics include:

1. Supervised learning (classification techniques, regression, parametric/non-parametric algorithms, support vector machines, multi-layer perception).
2. Unsupervised learning (clustering).
3. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The emphasis is for the students to learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

The aims of this course are:

• To describe a range of machine learning algorithms along with their strengths and weaknesses.
• To define the basic theory underlying machine learning.
• To apply machine learning algorithms to solve problems of moderate complexity.
• To compare machine learning problems corresponding to different applications.

Upon the successful completion of this course, students should be able to:

• describe a variety of learning algorithms to data.
• explain the evaluation of learning algorithms and model selection.
• demonstrate the modeling aspects of various machine learning approaches.
• evaluate machine learning approaches in terms of inductive bias.

Textbooks:

1. Introduction to Machine Learning By Ethem Aplaydin(Third Edition,2014)

References:

1. Machine Learning A Probabilistic Perspective By Kelvin P. Murphy(2012)
2. A Course in Machine Learning by Hal Daume III (2015)
3. Machine Learning by Tom Mitchell (Mar 1, 1997)