Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.
Explores heavy-tailed distributions, the Hill estimator, convergence to Gaussian, and distribution comparison.
Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.
Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.
Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.
