Tutorials and followtorials for the course "Machine Learning: A Hands-On Approach" at University of Bayreuth by Christopher Kuenneth
Run the notebooks on Google Colab using the buttons in the notebooks or in VScode or Jupyter.
I am using PDM for managing the Python dependencies.
- Find out how good you are at Python Click
- Deploy the 13-billion CodeLlama Instruct model on an Colab instance. Click
- AT HOME: Work with Pandas: balance data, plot, store Click
- Outlier detection with sklearn Click
- Compute fingerprints for text and materials Click
- AT HOME: Operations 🤕 on multi-dimensional arrays (tensors) Click
- Step-by-step linear regression Click (moved to lecture 6)
- Step-by-step linear regression Click
- My first NN Click
- HOMEWORK (play with and modify some parts of step-by-step linear regression) Click
- AutoML with AutoGluon to predict the tendency to crystalize for polymer Click
- HOMEWORK: prepare for your project 🚀 Click
- Visualize neural networks Click
- Regression: template for training a property ML predictor from chemical structures Click
- Computer vision: template for training a property predictor from figures Click
- Train your pytorch NN Click
- HOMEWORK: Read https://jalammar.github.io/illustrated-transformer !
- Please read this great article on "A Visual Exploration of Gaussian Processes"
- Folloturial: BO for design of experiments Click
See README in folder.