Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.
Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.
Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Discusses the significance of data collection and preparation for classification, including labeling challenges and crowdsourcing methods.
Explores feature selection methods, pitfalls, and normalization techniques for optimal model performance.
Explores recommender systems, collaborative filtering, content-based recommendations, similarity metrics, and advanced methods like matrix factorization.
