Sources:

An Introduction to Statistical Learning (with python)

https://www.statlearning.com/

A Course in Machine Learning (by Hal Daumé III)

http://ciml.info/

An Introduction to Machine Learning (by Miroslav Kubat)

Slides

Introduction  slides

Supervised Learning slides

Model selection slides

Unsupervised Learning slides


Review materials:

Gradient

MLE & MAP estimation

Homeworks


HW1 (deadline: 10th November)

HW2 (deadline: 30th November)

HW3 (deadline: 22th December)

HW4 (deadline: 15th January)


Final Project deadline: 6th February

(This semester will not include oral presentations. Please consolidate your report, presentation slides, and the chosen paper into a single RAR file. Submit the file to cv.kntu2023@gmail.com, using the subject line "ML Final Project )

Note: Reports that are simple translations will not be graded.

Final Project presentation:  6th February