Machine Learning Lectures
This repository contains an updated version of my lectures and workshops given at:
The workshops are provided as jupyter notebooks, which can be served as reveal.js presentations.
Howto clone this repository
This repository uses git submodules:
git clone –recurse-submodules https://github.com/thomaskeck/thomaskeck.github.io
Howto print the slides
There are two options to convert the html-based presentation into a printable pdf file
- Chrome: In chrome you can append ?print-pdf to the URL of the talk and print the website into a pdf. Check the preview in chrome in order to ensure that the output looks reasonable.
- Decktape: with decktape you can render the html slides and take screenshots, this has the advantage that it works with the animations
I usually do something like this:
First I serve the slides on a local web-server using python
python3 -m http.server
Afterwards I use decktape via docker to create screenshots of each rendered page with a large pause in between the screenshots to ensure that the animations finish.
docker run –shm-size 2G –rm -t –net=host -v
pwd:/slides astefanutti/decktape generic –key=n –screenshots -p 15000 -s ‘1240x874’ –max-slides 150 http://localhost:8000/talks/MachineLearning.html temp.pdf
Finally I remove all uninteresting slides by hand to create a clean version of the pdf.
convert $(ls -v screenshots/*.png) machine_learning.pdf
The original repositories with the previous content and exercises are available here:
An (incomplete) list of interesting books:
- Christopher M. Bishop. Pattern Recognition and Machine Learning
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning.
- J. Han, M. Kamber, J. Pei. Data Mining: Concepts and Techniques
- O. Behnke, K. Kröninger, G. Scott, T. Schörner-Sadenius. Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning (Adaptive Computation and Machine Learning)
- R. S. Sutton, and A. G. Barto. Reinforcement Learning: An Introduction
You can find a list of research publications I enjoyed reading here