Welcome

Welcome#

This machine learning course is created with Jupyter notebooks that allow you to interact with all the machine learning concepts and algorithms to understand them better. At the same time, you’ll learn how to control these algorithms and use them in practice.

Lectures#

Lectures can be viewed online as notebooks, as slides (online or PDF), or as videos (hosted on YouTube). They all have the same content. Upon opening the notebooks, you can launch them in Google Colab (or Binder), or run them locally.

	Notebooks	Slides	Video
1	Introduction	HTML - PDF	Youtube
2	Linear Models	HTML - PDF	Youtube
3	Kernelization	HTML - PDF	Youtube
4	Model Selection	HTML - PDF	Youtube¹
5	Ensemble Learning	HTML - PDF	Youtube
6	Data Preprocessing	HTML - PDF	Youtube
7	Bayesian Learning	HTML - PDF	Youtube
8	Neural Networks	HTML - PDF	Youtube
9	Convolutional Neural Networks	HTML - PDF	Youtube
10	Neural Networks for text²	HTML² - PDF²	Youtube²

¹ The order of the slides in the video is slightly different.
² This lecture has been significantly updated since the youtube video. A new recording is pending. TUe students: please see the lecture recording.

Get your hands dirty

Retrieve all materials by cloning the GitHub repo. To run the notebooks locally, see the prerequisites.

Have some feedback?

If you notice any issue, or have suggestions or requests, please go the issue tracker or directly click on the icon on top of the page and then ‘open issue`. We also welcome pull requests :).

Labs#

Download the lab notebooks and solve the questions locally, or launch them in Google Colab or Binder. Please review the relevant tutorials before starting the labs. Solutions will appear towards the end of each lab session.

	Notebooks	Tutorial	Solutions
1	Linear Models for regression Linear Models for classification	Tutorial	Lab 1a Lab 1b (Release date: 7 Feb, 12:00)
2	Kernelization Model Selection	Tutorial	Lab 2a Lab 2b (Release date: 21 Feb, 12:00)
3	Ensembles	/	Lab 3 (Release date: 28 Feb, 12:00)
4	Data engineering	Tutorial	Lab 4 (Release date: 6 Mar, 12:00)
5	Bayesian learning	/	Lab 5 (Release date: 13 Mar, 12:00)
6	Neural Networks	Tutorial	Lab 6 (Release date: 20 Mar, 12:00)
7	Neural Nets for Images Neural Nets for Text	Tutorial	Lab 7a Lab 7b (Release date: 27 Mar, 12:00)

Background materials#

Tutorials#

General introductions into using Python for scientific programming and machine learning, as well as some basic machine learning techniques. Useful for novices to cover any knowledge gaps, while more advanced students can likely skip them.

Recommended resources#

These resources help to further deepen your skills, and are well aligned with this course.