This two-day course aims at giving the fundamental, essential concepts of machine learning. Through simplified key concepts from statistical, probabilistic and computational principles, the course aims at giving heuristics on how and when to choose a particular machine learning approach to a problem. This will aid in interpreting and explaining, to an extent, a models behaviour. The course focuses on supervised and unsupervised approaches, and model selection.

The course is organized on site at CSC. A Zoom option will be provided for those whom register to course but cannot make it on site. Hands-on exercises will be done using the Python language in CSC Notebooks environment (https://notebooks.csc.fi/).

Learning outcomes: To obtain ideas on what to look out for when a given problem can be solved using supervised or unsupervised learning tools, and focus on interpreting and explaining models.

This course is for students, researchers or in industry that are new and wants to get into applying machine learning methods in their applications. Also those whom have been using machine learning might also benefit from this course.

Prerequisites: Basics of the Python language is assumed but not mandatory. Additionally basic notions of statistics and probability will be beneficial, however basic notions will be explained as methods and approaches are introduced.

Schedule

both days from 09:00 to 16:00

Day 1
Course introduction ( Primer on principles of statistical inference, probability, and algorithms )
Supervised Learning ( Support Vector Machines, Neural Networks )
Numerical Methods & Optimization

Day 2
Ensemble methods
Model selection
Unsupervised learning (parametric and non-parametric)