LEARNING OUTCOMES
After the course, the student understands how Bayesian networks are constructed with conditional independence assumptions and how they are applied in modeling of joint probability distributions. The students can explain the structure and usage of common probabilistic models in machine learning, such as sparse Bayesian linear models, Gaussian mixture models and factor analysis models. The students can apply Bayes theorem for computing probability statements and understand the fundamental role of Bayes theorem in probabilistic inference. The students can derive approximate inference algorithms for complex models, where exact probabilistic inference may not be applied. Furthermore, they can translate probabilistic models, inference, and learning algorithms into practical computer implementations.
Credits: 5
Schedule: 10.01.2025 - 11.04.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pekka Marttinen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
The course covers concepts in probabilistic machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, latent linear models, and algorithms for exact and approximate inference, with an emphasis on variational inference. The course emphasizes understanding fundamental principles that allow students to understand and apply probabilistic modeling in practice.
Assessment Methods and Criteria
valid for whole curriculum period:
Exercises and an exam (details provided on the first lecture).
DETAILS
Study Material
valid for whole curriculum period:
Kevin P. Murphy, Machine Learning - A Probabilistic Perspective. The MIT Press, 2012
David Barber, Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
Christopher M. Bishop, Pattern recognition and machine learning. Springer, 2006.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring III - IV
2025-2026 Spring III - IV