HS2023: 42032/62032 Machine Learning and Data Mining

This course gives an introduction to the theory of machine learning. Application is in the form of a course project.
During the course, you will be able to:

- Formulate machine learning problems in terms of opimisation or probabilistic inference.
- Apply off-the-shelf algorithms to problems with scikit-learn
- Develop custom models and algorithms using python libraries such as pyTorch

Grading:
- Exam: 40%
- Project: 40%
- Assignments: 20%

1. Learning as optimisation
1.1 Objective functions
1.2 k-Nearest neighbours
1.3 Generalisation
1.4 Linear neural networks
1.5 Stochastic gradient descent
1.6 Multi-layer neural networks
1.7 Backpropagation
2. Learning as probabilistic inference
2.1 Probabilistic models
2.2 Discriminative models
2.3 Generative models
2.4 Expectation Maximisation
2.5 Monte-Carlo Inference
2.6 Variational inference
3. Sequence modelling
3.1. Auto-regresive models
3.1.1 MLP Auto-Regressors and Transformers
3.1.2 (Variable Order) Markov Models
3.2. Recursive models:
3.2.1. Recursive Neural Networks
3.2.2. Hidden Markov Models
4. Decision making
4.1. General statistical decision problems
4.2. Reinforcement learning

Deutsch

This work has all rights reserved by the owner.

Christos Dimitrakakis

christos.dimitrakakis@unine.ch

Fridays 13:00-14:00, B116, by appointment.

Unbegrenzt – wenn online geschaltet

Sie können diesem Kurs direkt beitreten.

Unbegrenzt

Anmeldename
Vorname
Nachname
E-Mail