Emre Ugur
Ph.D.
CMPE462 Machine Learning
2018-2019 Spring
Instructor: Emre Ugur (contact)
Description: Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition and data mining
Textbook: Introduction to Machine Learning, Ethem Alpaydin, 3e, The MIT Press, 2014.
Lectures: Monday 09:00-11:00, Tuesday 09:00-10:00
Classroom: Computer Engineering Dept, A2
Mailing-list: Send email if not automatically registered.
Note: Only offered to CMPE undergraduate students.
Grading: (Tentative)
- Midterm: 20%
- Project: 30%
- Final: 30%
- Homeworks: 20%
Schedule (Tentative)
| Week 1 | Introduction | Slides | Week 1 | Probability review | Slides | Week 2 | Supervised learning | Slides | Week 3 | Bayesian decision theory | Slides | Week 3 | Parametric methods | Slides | Week 4 | Parametric methods cont'd | Slides | Week 5 | Multivariate data, dimensionality reduction | Slides | Week 5 | Dimensionality reduction | Slides | Week 6 | Dimensionality reduction | Slides | Week 6 | Clustering | Slides | Week 7 | Midterm | Week 7 | non-parametric methods | Slides | Week 8-9 | Neural Networks | Slides, Delta Rule | Week 10 | Decision Trees | Slides | Week 11 | Support Vector Machines | Slides | Spring break | Week 12 | Reinforcement Learning | Slides | Week 13 | Analysis of ML methods | Slides | Week 13 | GAN | Slides | Exercises | tgz | Final exam |