Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
