HS2024: 42123/62123 Machine Learning: Theory, Fairness and Privacy

This course will focus on the fundamental theory of machine learning, fairness and privacy. - Probabilistic models - Statistical learning - Concentration inequalities - Learning and generalisation - Fairness, smoothness and conditional independence - Anonymity and differential privacy

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Kursbeschreibung

The course aims to give a solid understanding of the fundamentals of machine learning theory and explanation of the basic algorithms.

Kursprogramm

Theory:
- Probabilistic models and cost minimisation
- Bayesian inference and conjugate priors
- Stochastic gradient descent and neural networks
- Concentration inequalities, and learning theory.
- Learning theory and generalisation
- Fairness and conditional independence
- Differential privacy and randomisation
Algorithms:
- k-nearest neighbour
- Perceptron
- Stochastic Gradient Descent and Backpropagation
- Markov Chain Monte-Carlo
Application Project:
- A medical treatment recommendation system

Zielgruppe

Master students wanting to get deeper into machine learning

Beschreibung

This course will focus on the fundamental theory of machine learning, fairness and privacy.
- Probabilistic models
- Statistical learning
- Concentration inequalities
- Learning and generalisation
- Fairness, smoothness and conditional independence
- Anonymity and differential privacy

Allgemein

Sprache

Deutsch

Verfügbarkeit

Zugriff

Unbegrenzt – wenn online geschaltet

Aufnahmeverfahren

Sie können diesem Kurs direkt beitreten.

Zeitraum für Beitritte

Unbegrenzt

Für Kursadministratoren freigegebene Daten

Daten des Persönlichen Profils

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