YAP470

School of Engineering \ Artificial Intelligence Engineering

3

6

Compulsory

Turkish

BİL 113, BİL 245, BİL 345

Artificial Intelligence Engineering Undergraduate Program

This course provides a broad introduction to machine learning. Topics include: supervised learning, unsupervised learning, learning theory and reinforcement learning. The course will also discuss some recent applications of machine learning to different fields.

G. James, D. Witten, T. Hastie, R. Tibshirani. Introduction to Statistical Learning, Springer, 2014.
T. Mitchell. Machine Learning. McGraw Hill, 1997.

1. To learn what machine learning deals with.
2. Be familiar with some basic machine learning methods.
3. To learn model selection and regularization.

1. Learning the concept and terminology of machine learning
2. To know some basic machine learning methods.
3. To learn model selection and regularization.
4. To learn the selection and adaptation of appropriate machine learning models to different types of problems
5. To learn supervised, unsupervised, reinforcement learning topics
6. To be able to train, test and analyze using different machine learning models on a dataset

Week 1: Introduction, basics.
Week 2: Concept Learning.
Week 3: Linear regression, classification
Week 4: Resampling methods
Week 5: Linear model selection and regularization
Week 6: Decision trees, support vector machines
Week 7: Probabilistic models
Week 8: Neural networks
Week 9: Unsupervised learning, ensemble models.
Week 10: Learning Theory
Week 11: Reinforcementl learning
Week 12: Machine learning applications

• Midterm Exams 20 %
• Final 25 %
• Homework 25 %
• Project 30 %