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Final overview
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Classroom Contents
Natural Language Processing - Spring 2024
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- 1 Intro to NLP; Course logistics
- 2 Machine learning basics; Perceptron
- 3 Logistic regression; Tokenization
- 4 Vector semantics; Embeddings
- 5 Neural network basics
- 6 Language modeling
- 7 Neural machine translation
- 8 Attention; Transformer (Encoder)
- 9 Transformer (Decoder); Pretraining & Finetuning
- 10 Finetuning DeBERTa in 🤗 (demo); Instruction FT
- 11 Finetuning a seq2seq model in 🤗 (demo); RLHF
- 12 “Taskification” and Valid Benchmarks
- 13 Midterm overview
- 14 POS Tagging; Named Entity Recognition; Hidden Markov Model
- 15 Hidden Markov Model: Parameter Estimation & Efficient Inference
- 16 Constituency parsing
- 17 Dependency parsing
- 18 Semantic parsing; SRL
- 19 Coreference resolution
- 20 Probing
- 21 Question Answering
- 22 Multilingual and multimodal LLMs
- 23 Topics in Responsible AI
- 24 Final overview
- 25 Summarization
