Bayesian Models

Machine learning has become one of the hottest areas in computer science and technology. Both industry and academia have gone gaga. Big tech companies send 100’s each to the top research conferences and the conference numbers are increasing in size so they are now beyond capacity. But, how do you learn about machine learning in the first place? Assuming you have a strong STEM undergraduate degree and are research savvy, this page points to some appropriate resources for research. These are intended for starting PhD students.

If you are more interested in learning the basics as a potential user, then you will need to find different resources such as the blogs up on https://medium.com/search?q=machinelearning&ref=opensearch or at the MOOCS such as Coursera.

University Classes

Places like Stanford and CMU have very good advanced masters-level classes ideal for starting PhD students. Slides and oftentimes lectures are online for the public. e.g., deep networks for NLP http://web.stanford.edu/class/cs224n/

See also Lex Fridman’s seminars up at https://deeplearning.mit.edu/ . Very good overview of capabilities and directions for a general overview.

Good Venues

Excellent tutorials are available recently at the major conferences, oftentimes with vidoes and/or slides on the website, although sometimes you have to hunt through the author’s webpages. The top conferences include AI&Stats, IJCAI, ICML, ACL … be warned, some tutorials are a bit specialised or advanced.

• https://nips.cc/Conferences/2018/Schedule?type=Tutorial
• https://icml.cc/Conferences/2019/Schedule
  • tutorials are in pink up the top; e.g. Active Learning, Attention, Meta Learning

Machine Learning Summer School (MLSS)

This series is managed by venerable machine learning researchers and only has a few per year internationally. Their list of venues is at http://mlss.cc/ . You have to go to each and navigate disparate and sometimes wacky layouts to locate slides and/or videos.

• 2019 in London is high quality
  • https://sites.google.com/view/mlss-2019/lectures-and-tutorials
  • https://github.com/mlss-2019/slides
  • see Shakir Mohamed’s VAE.pdf, more a checklist than a tutorial
  • see Nowozin’s GAN tutorial
• 2019 Stellenbosch (Africa)
  • a few videos https://www.youtube.com/channel/UC722CmQVgcLtxt_jXr3RyWg
  • and slides https://drive.google.com/drive/folders/1mHqPevddlosT7TnqumDBq4e-fIw8sot_
• 2018 Madrid
  • Slides and videos here: http://mlss.ii.uam.es/mlss2018/speakers.html
  • NOTE: some are earlier versions of above, so take the later one!

AutoML

The Freiburg-Hannover group has a great sequence of tutorials on AutoML and learning to learn:

• https://www.automl.org/events/tutorials/
• https://nips.cc/Conferences/2018/Schedule?showEvent=10979 the NeurIPS 2018 version with video

VideoLectures.net

An initiative of the Jozef Stefan Institute, Ljubljana, records many great tutorials, but coverage not as good recently. Go seaching for your favorite subjects:

• NLP and Deep Learning 1: Human Language & Word Vectors, by Chris Manning, 2015
• e.g., on the subject of “Machine Learning”

Others

• https://learningnets.github.io/KDD19_Tutorial/ KDD tute by a star-studded list , many slides there
• health informatics http://www.kmd.ovgu.de/KMD+Events/Data+Science+for+Health.html
• https://sites.google.com/view/deepdial/ conversational AI … the new wa