Skip to content

Latest commit

 

History

History
68 lines (53 loc) · 9.6 KB

intro.md

File metadata and controls

68 lines (53 loc) · 9.6 KB
Raw

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.

Retrieve all materials by cloning the <i class="fab fa-github"></i> [GitHub repo](https://ml-course.github.io/master). To run the notebooks locally, see the [prerequisites](https://ml-course.github.io/master/labs/Lab%200%20-%20Prerequisites.html).

:class: tip
If you notice any issue, or have suggestions or requests, please go the
<i class="fab fa-github"></i> [issue tracker](https://github.com/ml-course/master/issues/) or directly click on the  <i class="fab fa-github"></i> 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.

  1. Python basics
  2. Python for data analysis
  3. Machine learning in Python
  4. Recap: Decision trees
  5. Recap: Nearest Neighbors

Recommended resources

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

  1. Scientific Python Lectures (by J.R. Johansson)
  2. Mathematics for Machine Learning (by M.P. Deisenroth et al.)
  3. fast.ai online course on practical deep learning
  4. Google Machine Learning crash course