Information Retrieval Basics: Boolean and Vector Space Models

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Text Retrieval: Document Ranking

Covers text retrieval tasks with document ranking and re-ranking, using a large corpus for evaluation.

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Vector Space Semantics (and Information Retrieval)

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Indexing and Distributed Retrieval

Explores indexing techniques, inverted files, map-reduce algorithms, and top-k document retrieval methods in text retrieval systems.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Programming Basics: Python Fundamentals

Covers the basics of programming with Python, emphasizing practical exercises and self-learning resources.

Python Programming Basics

Covers the basics of Python programming, focusing on flow control structures, interactive mode, script mode, and variables.

Latent Semantic Indexing

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Introduction to Information Retrieval

Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.

Projects Presentation & Logistics

Covers the presentation of 4 projects in the course and related logistics.