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WeekTopicContent
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W1Introduction
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W2Machine learning 1Feature extractor, Linear regression, Linear classification
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W3Machine learning 2Stochastic gradient descent, Non-linear features, Neural networks
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W4Machine learning 3Generalization, Overfitting, Underfitting
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W5Representing texts with vectors 1Sparse representations, Bag-of-words, TF-IDF
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W6Representing texts with vectors 2Dense static representations, Word embeddings,
Skip-gram learning, Word2Vec
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W7Representing texts with vectors 3Dense contextual representations, Self-attention,
Masked language modeling, BERT
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W8Midterm
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W9Text classification 1Pretraining, Fine-tuning,
Classification learning and evaluation
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W10Text classification 2Semi-supervised learning: Self-training
Multi-task learning: Multi-gate mixture of experts
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W11Text classification 3Domain adaptation: Data reweighting, Adversarial learning
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W12Search 1Search task and evaluation
Lexical retrieval: Inverted index, BM25
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W13Search 2Dense retrieval: ANN index, Learning to rank
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W14Search 3Bi-encoder, Cross-encoder, Multi-stage ranking pipeline,
Late interaction, Infusing user interests
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W15Search 4LLM-enhanced IR: LLM as a ranker,
LLM as a context enhancer, LLM as a user simulator
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W16Final exam
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