💻 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):
- General problem statement and introduction
- Ensembles of trees: forests and gradient boosting
- Neural networks: convolutions aren't convoluted
- Model selection and evaluation: predict future performance
- PCA and t-SNE: lower dimensional embeddings and visualisation
- Bayesian optimisation for hyper-parameter tuning (~)
- Meet a GAN: cops and robbers for neural networks (~)
- 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.