NLP 243: Deep Learning for NLP
University of California, Santa Cruz
Introduction to machine learning models and algorithms for natural language processing (NLP) including deep learning approaches. Targeted at professional master's degree students, course focuses on applications and current use of these methods in industry. Topics include: an introduction to standard neural network learning methods such as feed-forward neural networks; recurrent neural networks; convolutional neural networks; and encoder-decoder models with applications to natural language processing problems such as utterance classification and sequence tagging. (Formerly Machine Learning for Natural Language Processing.)
Average GPA: 3.55
Grade distribution records: 151 students across 6 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 15 | 9.9% |
| A | 38 | 25.2% |
| A- | 47 | 31.1% |
| B+ | 18 | 11.9% |
| B | 15 | 9.9% |
| B- | 8 | 5.3% |
| C+ | 1 | 0.7% |
| F | 3 | 2.0% |
| S | 3 | 2.0% |
Based on 151 student grade records across 6 terms and 4 professors.
Instructors
- Amita Misra 55 students, Average GPA 3.45
- Dilek Hakkani-Tur 42 students, Average GPA 3.61
- Judith Clymo 32 students, Average GPA 3.47
- Ian Lane 22 students, Average GPA 3.77