💻 Material for a course on applied machine-learning for scientists taught at EPFL in spring 2017.

The course consists of six two hour lectures, followed by one hour to discuss the week's homework assignment. Followed by a final project on real world data.

Current bucket list of topics to cover ((~) denotes: short introduction to):

1. General problem statement and introduction
2. Ensembles of trees: forests and gradient boosting
3. Neural networks: convolutions aren't convoluted
4. Model selection and evaluation: predict future performance
5. PCA and t-SNE: lower dimensional embeddings and visualisation
6. Bayesian optimisation for hyper-parameter tuning (~)
7. Meet a GAN: cops and robbers for neural networks (~)
8. Probabilistic datastructures: a bonus lecture

Take a look at possible course projects.

All the code will be written in python. We will make use of the scientific python stack:

• python v3.6
• numpy v1.12.1
• scikit-learn v0.18.1
• keras
• matplotlib v2.0.0
• jupyter v5.0.0

All work submitted for credit has to run with these dependencies only.

Instructions on installing on windows, mac and linux.

Heavily inspired by ESL, ISL, Introduction to machine-learning with python, and lecture notes by Gilles Louppe.

All original work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.