SEDS 537
Machine Learning
To provide graduate students the foundations of machine learning. This course will begin with an introduction to the machine learning with examples in different application areas. Bayesian decision theory. Supervised learning techniques. Model selection. Dimensionality reduction. Clustering. Support vector machines. Graphical models. Introduction to neural networks. Reinforcement learning.
Reference book(s):
• Ethem Alpaydın, Introduction to Machine Learning (2nd Edition). MIT Press, 2010.
• Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
• Richard Duda, Peter Hart, David Stork, Pattern Classification, 2nd Ed. John Wiley & Sons, 2001.
Course Objectives: To advance students on the current trends in machine learning.
| Week | Topics |
| 1 | Introduction, Definitions of Machine Learning |
| 2 | Bayesian Decision Theory |
| 3 | Supervised Learning Fundamentals |
| 4 | Model Selection Procedures, Tuning Model Complexity |
| 5 | Multivariate Classification, Multivariate Regression |
| 6 | Dimensionality Reduction and Principal Component Analysis |
| 7-8 | Unsupervised Learning |
| 9 | Linear Discriminant Functions |
| 10 | Supervised Learning: Non-parametric approaches |
| 11 | Decision Trees |
| 12-13 | Artificial Neural Networks |
| 14 | Reinforcement Learning |
Grading:
Final Exam %40
Midterm Exam %30
Practice Assignments %30
Course Learning Outcomes:
CO1 To be able to describe the fundamental concepts in machine learning
CO2 To be able to analyze the machine learning problems and identify appropriate solution techniques
CO3 To be able to apply proper algorithms using proper tools to solve the encountered machine learning problems
CO4 To be able to interpret the output of the implementations from algorithmic point of view
Contribution of Program Learning Outcomes:
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | |
| CO1 | 1 | 1 | |||||
| CO2 | 1 | 1 | |||||
| CO3 | 1 | 1 | 1 | ||||
| CO4 | 1 | 1 | 1 |
Justification of the course:
It is an elective course of the Software Engineering and Data Science Master of Science Program and it is one of the important data science electives. The course introduces the fundamentals of machine learning along with giving the opportunity to apply proper algorithms and tools to solve the encountered machine learning problems. Main topics are supervised and unsupervised learning techniques, model selection, dimensionality reduction, clustering, neural networks and reinforcement learning. Together with practical exercises students will be able to analyze the machine learning problems and identify appropriate solution techniques.
Overlapping with or complementing topics in courses:
Since this is a fundamental course on machine learning, it serves as foundation for further courses like SEDS 538 Big Data Analytics and SEDS 539 Deep Learning, but there is not a significant amount of overlap with those courses.