Institute of Science and Technology · Computer Science and Engineering (%30 English) · Master
ECTS: 7.5 T+P+L: 3+0+0 Departmental Elective
Coordinator: Dr. Hakan ERDUN
Instructors: Dr. Hakan ERDUN
Course Objective
This course is designed for graduate students to gain practical knowledge and experience. Course will cover from basics to state-of-the-art machine learning techniques. The end of the day they will able to apply machine learning algorithms to classify and predict your data.
Course Content
• Concept Learning • Version Spaces • Decision Trees, Information Gain • Over fitting and Validation • Incorporating Continuous Values • Artificial Neural Networks, McCulloch-Pitts Neurons • Neural Network-Perceptron • Bayesian Learning • Regression • Evolutionary Algorithms
Course Learning Outcomes
- Concept learning skills by machines
- Real life problem solving skills
Core Area Distribution
(48) Computing%70 (52) Engineering and Engineering Trades%30