Machine Learning

2016/17 summer

Lectures

Introduction Duda 1.1-1.2

Decision trees, machine learning dilemmas Alpaydin 4.7-4.8

Linear machines, SVM Duda 5.1-5.5.2, Alpaydin 13.1-13.3, 13.5-13.6

Regression, neural networks Alpaydin 4.6, 9.2.2, 13.10; Duda 6-6.3

Bayes decision theory, parameter estimation Duda 2.1, 2.2, 2.4-2.6, 3.1-3.5

Naive Bayes, non parametric classifiers Duda 4.1-4.4 (excluding 4.3.5), 4.6

Clustering Alpaydin 7.3, 7.7, 7.8

Generative modeling

Recommender systems and Learning to rank

Reinforcement learning Alpaydin 18.1-18.5.1

Exam

On the oral exam, you got a topic from this list and after preparation time we talk about it.

Machine Learning project (exercise)

Project requirements

Project plan template

An example presentation

Timeline:

13/03/2017 team registration (e-mail)

20/03/2017 task selection (e-mail) - link

03/04/2017 project plan (e-mail) - 2 pages

14/05/2017 deliverables (e-mail) - codes/scripts, data, results

16?/05/2017 presentation