Learner Modelling: Chapter 7Delves into learner modelling, emphasizing the significance of understanding students' behaviors to enhance learning activities.
Knowledge Inference for GraphsExplores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Introduction to Data ScienceIntroduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Topic ModelsIntroduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Variational Auto-Encoders and NVIBExplores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.
Inference of Reaction KineticsFocuses on the inference of reaction kinetics in combustion, covering rules inference, sensitivity analysis, and Bayesian inference.