Linear Regression and Beyond (2019/20)

African Institute for Mathematical Sciences Rwanda and Ghana

This short online course is part of the African Masters in Machine Intelligence (AMMI) at the African Institute for Mathematical Sciences (AIMS) in Rwanda and Ghana.

Syllabus

1. Linear Regression (please use Acrobat Reader for animations)
2. Gaussian Processes (please use Acrobat Reader for animations)
3. Bayesian Optimization
4. Writing Guidelines

Annotated Slides

1. Linear Regression (annotated)
2. Gaussian Processes, Part 1 (annotated)
3. Gaussian Processes, Part 2 (annotated)
4. Bayesian Optimization (annotated)

Tutorials

• Linear Regression
• Gaussian Processes (Lab 1 from GPSS 2019)
• Bayesian Optimization

Teaching Assistants

• Abdul-Ganiy Babatunde Usman (AIMS Rwanda)
• Aisha Alaagib (AIMS Ghana)
• Amr Khalifa (AIMS Rwanda)
• Jeremiah Fadugba (AIMS Ghana)
• Kobby Panford-Quainoo (AIMS Rwanda)
• Kossi Amouzouvi (AIMS Ghana)
• Mustafa Alghali (AIMS Rwanda)

Resources

• M. P. Deisenroth, A. A. Faisal, C. S. Ong: Mathematics for Machine Learning, Cambridge University Press, 2020
• C. E. Rasmussen, C. K. I. Williams: Gaussian Processes for Machine Learning, MIT Press, 2006
• M. P. Deisenroth, Y. Luo, M. van der Wilk: A Practical Guide to Gaussian Processes
• B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. de Freitas: Taking the Human Out of the Loop: A Review of Bayesian Optimization, Transactions of the IEEE, vol. 104(1), pp. 148-175, 2016

Essential Preparation

• Basic mathematical concepts: Chapters 2-5 of this book
• Vector calculus