ML-course-VIB-2021

This repository contains the Jupyter notebooks for the VIB course on Machine Learning: "A tour of Machine Learning: classification".

You can fork this to your own repository to obtain a working copy.

Each notebook contains a button to run the code in Google Colaboratory.

The lectures can be watched on Youtube.

You will enjoy competing against each other to fit the best performing model in the Kaggle competition.

Schedule

DAY 1

9:30 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 Sanity 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 Virtual lunch time

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 Competition time

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.

DAY 2

9:30 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

11:00 Improve your Kaggle AUC score

12:15 Virtual lunch time

13:15 What is deep learning?

https://www.youtube.com/watch?v=x2FHuttvApE&list=PLv5LrvIzDSWZXAyIJmXgQ-ezCFELN8b5e&index=6

Hands on: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/classification.ipynb

14:30 Sanity Break

14:45 Discussions, Q&A

https://playground.tensorflow.org/

Further learning

Coursera ML course: https://www.coursera.org/learn/machine-learning

Kaggle learning: https://www.kaggle.com/learn/overview and https://www.kaggle.com/sashr07/kaggle-titanic-tutorial