How DID YOU GET INTO machine learning?
Dr. Bah is currently the German Research Chair of Mathematics with specialization in Data Science at the African Institute for Mathematical Sciences (AIMS) South Africa. Prior to this he held a postdoc positions at EPFL, Switzerland, and then at UT Austin, USA. His PhD degree is in Applied and Computational Mathematics from the University of Edinburgh, UK, his MSc degree in Mathematical Modeling and Scientific Computing from the University of Oxford, UK, and his BSc degree (summa cum laude) in Mathematics and Physics from the University of The Gambia. His research has been mainly on compressed sensing interested in sampling strategies for high-dimensional data with lower intrinsic dimensions. This was not mainstream data science but he now reorienting his research to mainstream Data Science focusing on methods, algorithms, HPC and applications of Data Science.
HOW Do You Think We Can strengthen African Machine Learning?
As a group we should continue to organize trainings like boot camps, workshops, etc to provide state-of-the-art skills that are typically not thought in standard university courses. We should support existing Machine Learning and Data Science courses/programs where we can teach or propose and supervise research projects for MSc students for instance. Secondly, we should come up with strategies to connect what going on in South Africa with the rest of Africa.
What advice would you give to those getting started in machine/deep learning?
You are entering a very exited field, which has a lot of potential. Study introductory statistics and probability and some basic numerical linear algebra. This will help you have insights into how algorithms work and to better understand outcomes/results of machine/deep learning algorithms. Exploit existing online resources to better prepare yourself for this field and attend workshops to keep up with the state-of-the-art innovations/problems in the area. Setup a GitHub and attempt challenges from competitions like Kaggle.