University of Information Technology

Machine Learning with Data Visualization

Course Description

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.

Intended Learning Outcomes

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.

Text and References Books


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


  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)

Assessment system

Evaluation Marks Percentage
Attendance 10 Marks 10%
Tutorial 10 Marks 10%
Assignments/Presentation 10 Marks 10%
Final Examination 70 Marks 70%