Machine Learning (CMPE462)

This course provides an overview of artificial learning systems, covering both supervised and unsupervised learning approaches. You will explore statistical models, decision trees, clustering techniques, and feature extraction. The curriculum also delves into artificial neural networks and reinforcement learning, with applications in pattern recognition and data mining. The language of instruction is English.

Curriculum

The course covers fundamental concepts in Machine Learning, including supervised and unsupervised learning methods, statistical models, decision trees, clustering, feature extraction, neural networks, and reinforcement learning.

1. Year 1
   • Machine LearningCMPE462

Frequently asked questions

What is the language of instruction for the Machine Learning course?

The language of instruction for this course is English.

What topics are covered in the Machine Learning course?

The course covers supervised and unsupervised learning, statistical models, decision trees, clustering, feature extraction, artificial neural networks, and reinforcement learning, with applications in pattern recognition and data mining.

Who teaches the Machine Learning course?

The course is taught by experienced faculty members, with current instruction led by Associate Professor İnci Baytaş.

What are the specific entry requirements for this course?

This course is part of the Computer Engineering program, and specific entry requirements for that degree program apply. No specific prerequisites are listed for the course itself beyond being part of the degree.

What is the course credit value?

The course has 3 Course Credits and 6 ECTS Credits.