Advanced Machine Learning for Data Science (Spring 2025)

Abstract

In this course, we teach both advanced machine learning (ML) approaches and hands-on skills for applying these approaches to data science problems.

Description

We teach the current state of the art of Machine Learning (ML) and applying ML to interesting data science problems. The module builds on top of the BSc course on Introductions to Machine Learning and focuses on Deep Learning and Probabilistic Machine Learning. On this basis, we cover both, theoretical and computational ML learning concepts and key mechanisms, as well as technical details for programming ML approaches hands-on in leading ML frameworks.

In particular, this course covers:

• A brief recap of fundamental learning types, preprocessing & data representations, and linear models,
• focussed recap of deterministic and probabilistic decision and information theory,
• supervised learning, including deep neural networks, discriminative learning,
• unsupervised learning, including dimensionality reduction, structure learning, and autoencoders,
• sequence learning including recurrent neural networks, and attention,
• regularization, optimization, augmentation, transfer learning,
• representation learning, including generative models,
• geometric deep learning, including graph neural networks,
• hybrid learning, adaptive learning, meta-learning.

Application areas will include data mining, bioinformatics, natural language processing, computer vision, and robot control.

In the accompanying tutorials, we will exercise to:

• reproduce key approaches in ML frameworks such as PyTorch and Tensorflow,
• monitor and analyse training and representation of ML approaches,
• collaboratively develop & visualise ML approaches,
• develop a complete ML solution on a task of choice in a small group.

Formal prerequisites

Having courses on Applied Statistics, Linear Algebra, and Optimisation completed is required. A good background in basic Machine Learning and object-oriented and/or interpreted programming languages such as Python is recommended.

Intended learning outcomes

After the course, the student should be able to:

• define and describe the state of the art approaches in machine learning (ML),
• characterise and analyse key mechanisms in major ML approaches in-depth,
• implement novel ML mechanisms in leading ML frameworks and develop applications for large-scale data science problems.

Ordinary exam