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VIB ML course 2024

Based on previous courses by Prof. Sven Degroeve.

This repository contains the Jupyter notebooks for the VIB Machine Learning & Deep Learning Workshop.

Getting started

Schedule

Day 1


9:30 Introduction to Machine Learning (Robbin Bouwmeester)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 1_introduction.pptx


10:00 Data fitting (Robbin Bouwmeester)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 2_regression.pptx

Some discussion about gradient descent.

Hands on: Histone_marks_lr.ipynb section 1


10:45 Break


11:00 Logistic regression (Robbin Bouwmeester)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 3_logistic_regression.pptx

Introduction to learning platform Kaggle + peptide retention time competition

Hands on: Histone_marks_lr.ipynb sections 2, 3 and 4


12:15 Lunch


13:15 Model complexity (Robbin Bouwmeester)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 4_regularization.pptx

Hands on: Histone_marks_lr.ipynb section 5


14:00 Bias & Variance (Robbin Bouwmeester)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 5_ensemble_learning.pptx

Hands on: Histone_marks_dt.ipynb


15:00 Kaggle Competition (Ralf Gabriels & Robbin Bouwmeester)

In this section it is up to you to fit and optimze a regression model, evaluate it, and make predictions on the test set. At this point there should be enough time to help each of you individually.

Day 2


09:00 What is deep learning? (Ralf Gabriels)

Lecture by Prof. Sven Degroeve: YouTube

Lecture slides: 6_deep_neural_networks.pptx


10:00 Break


10:15 CNNs and RNNs (Ralf Gabriels)

Hands on (CNN): Melanoma_CNN.ipynb

Additional material (RNN): Pytorch lightning RNN

Lecture slides:

7_computer_vision.pptx

8_sequence_modeling.pptx


11:00 Competition (Ralf Gabriels & Robbin Bouwmeester)


12:15 Lunch


13:15 Deep Generative Models (Robbin Bouwmeester)

Lecture slides: 9_deep_generative_models.pptx


14:30 Break


14:45 Discussions, Q&A, and competition (Ralf Gabriels & Robbin Bouwmeester)

https://playground.tensorflow.org/


Further learning