MW 2:40pm–3:55pm · T 7:00pm–9:30pm
2 distinct times across 3 sections.
2 of 3 sections have room
Prerequisites Prerequisites: (COMS W3134 or COMS W3136 or COMS W3137) or Python programming experience, probability theory, and linear algebra recommended. Some previous or concurrent exposure to AI and machine learning is benefici Some previous or concurrent exposure to AI or Machine Learning is recommended but not required.
Description
Lect: 3. Computational approaches to the analysis, understanding, and generation of natural language text at scale. Emphasis on machine learning techniques for NLP, including deep learning and large language models. Applications may include information extraction, sentiment analysis, question answering, summarization, machine translation, and conversational AI. Discussion of datasets, benchmarking and evaluation, interpretability, and ethical considerations. Due to significant overlap in content, only one of COMS 4705 or Barnard COMS 3705BC may be taken for credit.
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Course experience
n=32, 2004–2026
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Teaching quality
3.5 / 5
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No signal
Workload
4.0 / 5 · Heavy
Difficulty
3.7 / 5 · Hard
Overall sentiment
Mixed
Would take again
Majority say yes
Sections3
- Full
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
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Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
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Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
In your week
Workload and grading
- Grading
- Not published
- Format
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- Credits
- 3 credits
Reported workload
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