Classes

Python for Machine Learning

October 07, 2020

Class, Scripting in Python course, OsloMET, Oslo, Norway

This class offers an introduction to machine learning using python. After a brief review of the main concepts in machine learning, the class discusses the main python environment to run and train machine learning models. Examples of basic applications of machine learning models (linear regression, logistic regression, k-means, classification trees, neural networks) are then illustrated using sckikit-learn. Finally, references for further learning are provided.

Fair ML and Causal ML

May 12, 2020

Class, IN3050 Introduction to Artificial Intelligence and Machine Learning, University of Oslo, Oslo, Norway

These slides are a contribution to a class on future developments of artificial intelligence and machine learning. The first set of slides introduces the problem of fair decision-making in machine learning systems, and illustrates some sample solutions underlining ideas and flaws. The second set of slides discusses the problem of assessing causal relations in data, and points to solutions in the direction of causal graphical models.

Unsupervised Learning

April 14, 2020

Class, IN3050 Introduction to Artificial Intelligence and Machine Learning, University of Oslo, Oslo, Norway

This class offers an introduction to the topic of unsupervised learning. It first contrasts the problem of unsupervised learning to the problem of supervised learning. It presents some conceptual tools (representations, structure) to formalize and reason about the unsupervised learning problem. These concepts are then used to discuss several prototypical unsupervised learning tasks, from clustering to generative modelling. Finally three paradigmatic unsupervised learning algorithms (PCA, k-means, and autoencoders) are analyzed and evaluated.