Based on previous courses by Prof. Sven Degroeve.
This repository contains the Jupyter notebooks for the VIB Machine Learning & Deep Learning Workshop.
- Fork this repository to obtain an editable copy.
- Start by clicking on the
Open in Colabbutton in each notebook. - After editing a notebook, it can be saved to GitHub in your own repository.
9:00 Introduction to Machine Learning
https://www.youtube.com/watch?v=N9p81OwKI18&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=1&t=1s
10:00 Data fitting
https://www.youtube.com/watch?v=MhXYAAYj69Q&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=2
Some discussion about gradient descent.
Hands on: Hitsone_marks_lr.ipynb section 1
10:45 Break
11:00 Logistic regression
https://www.youtube.com/watch?v=JaoCcC1UIa4&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=3
Introduction to learning platform Kaggle + Histone mark contest
Hands on: Hitsone_marks_lr.ipynb sections 2, 3 and 4
12:15 Lunch
13:15 Model complexity
https://www.youtube.com/watch?v=7JH3kNdai-4&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=4
Hands on: Hitsone_marks_lr.ipynb section 5
14:00 Bias & Variance
https://www.youtube.com/watch?v=5Nvoy7VEuJA&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=5
https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
Hands on: Hitsone_marks_dt.ipynb
15:00 Kaggle Competition
In this section it is up to you to fit and optimze a classification model, evaluate it, and make predictions on the test set. At this point there should be enough time to help each of you individually.
09:00 What is deep learning?
https://www.youtube.com/watch?v=x2FHuttvApE&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=6
10:00 Break
10:15 CNNs and RNNs
Hands on: pytorch.ipynb
11:00 Competition
12:15 Lunch
13:15 Deep Generative Models
Hands on: pytorch.ipynb
14:30 Break
14:45 Discussions, Q&A