led by Dr Sohan Seth
Machine Learning has become a popular topic in the recent years. e.g., for designing recommendation systems used in YouTube or for building computer vision models used in self-driving cars. In this workshop you will learn some fundamental concepts of machine learning, e.g., model training and validation, hyper parameters tuning etc., and explore some of the mostly commonly used algorithms for both supervised and unsupervised learning, for example, random forest and k-means algorithm. The goal of this workshop is to introduce you to machine learning and equip you with practical guidelines to help you use these tools in your research.
Who’s it for?
This is an introductory workshop. You are only expected to be familiar with basic concepts in statistics such as probability distribution, loss function and distance metric. The workshop will be split in short lectures, hands-on session and Q&A. Examples will be given in Python, so it will be good if you have working knowledge of the software (we will try to replicate the examples in R if needed). I will encourage you to bring your own research problems to the Q&A, and discuss machine learning solution with the class.