Delves into orchestration graphs, transition probabilities, and learning analytics for predicting student states.
Explores postmortem memory analysis using news and social media data, investigating biographic correlates and memory patterns.
Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.
Explores the analysis of postmortem memory of public figures in news and social media, uncovering significant insights into memory formation.
