Course:
Course Program:
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: BM 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)
| Introduction | ||
| Probability review | ||
| Supervised learning | ||
| Bayesian decision theory, parametric methods | ||
| Parametric methods | ||
| Multivariate data, dimensionality reduction | ||
| Dimensionality reduction, clustering | ||
| Clustering, nonparametric methods | ||
| Neural Networks | ||
| Decision Trees | ||
| Support Vector Machines | ||
| Spring break | ||
| Reinforcement Learning | ||
| Design and Analysis of ML methods | ||
| Final exam |