Algorithms for Machine Learning and Inference

This course will discuss the theory and application of algorithms for machine learning and inference, from an AI perspective. In this context, we consider as learning to draw conclusions from given data or experience which results in some model that generalises these data. Inference is to compute the desired answers or actions based on the model.

Algorithms of this kind are commonly used in for example classification tasks (e.g., character recognition, or to predict if a new customer is creditworthy) and in expert systems (e.g., for medical diagnosis). A new and commercially important area of application is data mining, where the algorithms are used to automatically detect interesting information and relations in large commercial or scientific databases.

The course intends to give a good understanding of this crossdisciplinary area, with a sufficient depth to use and evaluate the available methods, and to understand the scientific literature. During the course we may discuss potential problems with machine learning methods, for example, bias in training data and safety of autonomous agents.

The following concepts are covered:

• Bayesian learning: likelihood, prior, posterior.
• Supervised learning: Bayes classifier, Logistic Regression, Deep Learning (Standard and CNN), Support Vector Machines, regression models, K-nn models.
• Unsupervised learning: Clustering algorithms, EM algorithm, Mixture models,
• Kernel methods,
• Temporal machine learning models (for example RNN)