CSCE 471 - Natural Language Processing (Spring 2027)

Course Information

Lectures
  • Time: Monday/Wednesday/Friday 11:30am – 12:20pm
  • Location: HRBB 105
Instructor
  • Kuan-Hao Huang
  • Email: khhuang [at] tamu [dot] edu
  • Office: PETR 219
  • Office Hour: Wednesday 2pm – 3pm
Readings (Optional)

Grading

Grade
  • Assignments (25%)
    • Assignment 0 (1%) [2/4]
    • Assignment 1 (8%) [2/11]
    • Assignment 2 (8%) [3/11]
    • Assignment 3 (8%) [4/22]
  • Quizzes (45%)
    • Quiz 1 (9%) [2/1]
    • Quiz 2 (9%) [2/15]
    • Quiz 3 (9%) [3/1]
    • Quiz 4 (9%) [3/29]
    • Quiz 5 (9%) [4/19]
  • Course Project (26%)
    • Project Proposal (3%) [3/9]
    • Project Presentation (10%) [4/26, 4/28, 4/30, 5/3]
    • Project Report (13%) [5/6]
  • Participation (4%)
Late Policy
  • Assignment 1-3, Project Proposal, Project Report
    • 1 day late: 10% penalty
    • 2 days late: 20% penalty
    • 3 days late: 30% penalty
    • 4 days late: 50% penalty
    • 5 or more days late: 100% penalty
  • Assignment 0, Project Presentation
    • No late submissions allowed

Schedule

Week Date Topic Content Readings Note
W1 1/18 Martin Luther King, Jr. Day (No Class)
1/20 L1 Course Overview
1/22 L2 Text Classification Text Classification: Formulation
Bag-of-Words Features
Logistic Regression 		Text Classification
TF-IDF
Logistic Regression
W2 1/25 L3 Text Classification Neural Networks Neural Networks
TensorFlow Playground
1/27 L4 Word Representations Counting-Based Word Vectors
Learning-Based Word Vectors: The Model 		LSA
Word2Vec
1/29 L5 Word Representations Learning-Based Word Vectors: Training and Variants
Learning-Based Word Vectors: Evaluation 		GloVe
fastText
Bias in Word Embeddings
W3 2/1 L6 Word Representations Tokenization Byte-Pair Encoding Quiz 1 (L2-L5)
2/3 L7 Language Modeling N-Gram Language Models
Decoding Strategies 		Language Modeling
Decoding Methods
2/4 Assignment 0 Due
2/5 L8 Language Modeling
W4 2/8 L9 Neural Sequence Models Convolutional Neural Network TextCNN
2/10 L10 Neural Sequence Models Recurrent Neural Network
Long Short-Term Memory 		RNN
LSTM
2/11 Assignment 1 Due
2/12 L11 Neural Sequence Models Sequential Labeling BiLSTM-CRF
W5 2/15 L12 Neural Sequence Models Sequence-to-Sequence Models
RNN with Attention 		Seq2Seq
Attention 		Quiz 2 (L6-L11)
2/17 L13 Transformers
2/19 L14 Transformers
W6 2/22 L15 Transformers
2/24 L16 Transformers
2/26 L17 Transformers
W7 3/1 L18 Contextualized Representations Quiz 3 (L12-L17)
3/3 L19 Pre-Training
3/5 L20 Large Language Models
W8 3/8 L21 Large Language Models
3/9 Project Proposal Due
3/10 L22 Model Efficiency
3/11 Assignment 2 Due
3/12 L23 Model Efficiency
W9 3/15 Spring Break (No Class)
3/17 Spring Break (No Class)
3/19 Spring Break (No Class)
W10 3/22 L24 Evaluation
3/24 L25 Post-Training
3/26 Reading Day (No Class)
W11 3/29 L26 Post-Training Quiz 4 (L18-L24)
3/31 L27 Large Reasoning Models
4/2 L28 Large Reasoning Models
W12 4/5 L29 Text Similarity
4/7 L30 Retrieval-Augmented Generation
4/9 L31 Agents
W13 4/12 L32 Multilingual Models
4/14 L33 Vision-Language Models
4/16 L34 Vision-Language Models
W14 4/19 L35 Hallucinations Quiz 5 (L25-L34)
4/21
4/22 Assignment 3 Due
4/23
W15 4/26 Project Presentations (Remote)
4/28 Project Presentations (Remote)
4/30 Project Presentations (Remote)
W16 5/3 Project Presentations (Remote)
5/5 Reading Day (No Class)
5/6 Project Report Due