Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.
Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.
Covers Probabilistic Information Retrieval, including Query Likelihood Model, Language Modeling, and smoothing techniques for non-occurring terms.
Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.
Explores the significance of System Modeling Languages like OPM, SysML, and Modelica in modern Systems Engineering.
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.
Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.
Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.
