Pedram Daee

Pedram Daee · Wed, 23.09.2015

Machine Learning Summer School, Sydney, 15-25 February 2015

The Machine Learning Summer School (MLSS) series was started in 2002 and since then there have been several schools in each year. All schools cover general introductions to different subfields of machine learning and a more in depth introduction to one or two specific fields. The targeted audience are students, in particular PhD, and professionals (faculty, researchers, and postdocs).

As a first year PhD student in Aalto University studying machine learning, in particular probabilistic modeling and information retrieval, I decided to participate in the next MLSS. This year’s MLSS took place in the University of Sydney from 15 to 25 February 2015. The main focus of the school was on probabilistic inference, large scale learning, Bayesian non-parametrics and applications to recommender systems, which was in line with my current research. The school consisted of sixteen lectures (approximately 2.5h each) and six lab tutorial sessions (approximately 1.5h each) in a very tight schedule.

The experience was helpful to my research from several point of views. First, some of the lectures were directly about the topics that I was studying at the moment. Second, it was a great opportunity for me to meet and become familiar with current research direction of my colleagues and future peer reviewers. Finally and most importantly, the lectures covered from fundamentals to state-of-the-art of most of the current hot topics in the field. I believe that just knowing the boundaries of different subfields of machine learning, can help a new researcher to accurately spot his current position in the field. Having this big picture in mind can substantially help to make well devised new research steps.

A huge thank to HICT and Aalto University for providing the funding for my trip and supporting this great experience in beautiful Sydney.