Probabilistic Retrieval: Practical Relevance Feedback

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Explores epidemics in network data, covering SIR model, basic reproductive ratio, percolation, directed networks, and maximum likelihood estimation.

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Covers state space models and their expressivity compared to transformers, focusing on attention mechanisms and computational efficiency.

Explores various continuous probability distributions and expectation of random variables.

Introduces Boolean and Vector Space models for information retrieval, covering syntax, similarity computation, term frequency, and query weights.

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Explores term-offset indices in inverted files and relevance feedback solutions.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

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