💻 Material for a course on applied machine-learning for scientists taught at EPFL in spring 2018.
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.
- Intro and Model Performance
- Tree based models
- Neural Networks
- Feature engineering
- Embeddings and pretrained networks
- Interpretability
You can run the notebooks from this repository from your browser without installing anything. To do so click the badge below.
This is good if you want to try out something but mybinder.org makes it a bit difficult to save your work so I recommend you still install things locally.
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 v0.19
- keras
- matplotlib v2.x.x
- jupyter
All work submitted for credit has to run with these dependencies only.
Instructions on installing on Windows, mac and linux.
There are many good books, lecture courses and videos out there.