Advanced Statistical Inference
π Course Brief
This course focuses on the principles of learning from data and quantification of uncertainty in the context of machine learning, by complementing and enriching the βMachine Learning and Intelligence Systemsβ course. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical learning models/algorithms can be seen as special cases of inference problems for more general probabilistic models.
How: Lectures and Lab sessions (Python)
Policy: Attendance is highly recommended
π― Learning Objectives
- To identify the key elements composing a given probabilistic model
- To recognize the suitability of different probabilistic models given a machine-learning problem
- To use the appropriate techniques to derive probabilistic machine-learning algorithms
- To develop codeto set up analyses of data using probabilistic machine-learning algorithms
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