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
Exercisestgz
Final exam