YAM 328 - Introduction to Machine Learning

Faculty of Engineering and Natural Sciences · Software Engineering (English) · Undergraduate

ECTS: 6 T+P+L: 3+0+0 Compulsory
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Course Objective

In machine learning, it is aimed to model methods and these methods on data mathematically.

Course Content

It includes applied concepts related to learning types in machine learning. Supervised learning, Bayesian decision theory, size reduction, clustering, distribution-free methods, decision trees, linear classification, multi-layered perceptrons, support vector machine algorithms will be covered with applications in theory and practice.

Course Learning Outcomes

  1. Learn the basics of machine learning.
  2. Understand the mathematical underpinnings of machine learning methods.
  3. Apply machine learning methods to real-world problems.
  4. Will be able to use feature extraction, selection, dimension reduction approaches.
  5. Identify the right model for the problem. Multilayer perception (Training a Perceptron,
  6. Will be able to evaluate and compare the success and performance of the applied methods.
  7. Calculate the optimal machine learning method for a given data set and interpret the results obtained.
  8. They will be able to process the raw data with data preprocessing steps and make it ready for application to machine learning methods.
  9. Know the difference between supervised and unsupervised learning.
  10. Know the algorithmic flow of machine learning methods.

Core Area Distribution

(46) Mathematics and Statistics%20 (48) Computing%50 (52) Engineering and Engineering Trades%30