CSE 455/555 Introduction to Pattern Recognition
In this updated Pattern Recognition course, we’ll learn not only by lectures and problem sets but by diving directly into the original research papers that shaped the field. Each week, you’ll read several papers—ranging from classic template-matching methods and statistical classifiers to modern deep-learning approaches—then discuss and implement key ideas.
Our in-class sessions will be a mix of presentations, guided code walkthroughs, and Q&A. You’ll write brief critical analyses for each paper, code up core algorithms in Python (using NumPy, scikit-learn or PyTorch), and compare performance on benchmark datasets. By the end of the semester, you’ll not only master pattern-recognition techniques but also develop the critical reading and implementation skills needed to engage with cutting-edge research.
Prerequisite: Pre-Requisites: CSE 250 or EAS 230 or EAS 240 or CSE 115 or EAS 999TRCP and EAS 305 or STA 301 and STA 301 or MTH 411; Computer Science, Computer Engineering, or Bioinformatics majors only. Students must complete a mandatory advisement session with their faculty advisor.
Resources: A curated PDF reading list will be provided; expect weekly code exercises and written critiques.
Instructor Information
Course Instructor: Jue Guo
- Research Area: Optimization for machine learning, Adversarial Learning, Continual Learning and Graph Learning
- Interested in participating in our research? Reach to me by email.
Course Outline and Logistics
Check out the course material under lecture notes.
Course Hours: MoWe 7:00 PM – 8:20 PM
Format: Remote
Term Dates: Jun 22, 2026 – Jul 31, 2026
Course Format
This is a paper-reading seminar. Each session covers one or two papers — one foundational, one modern — tracing an arc from classical statistical pattern recognition to current research frontiers.
- Pre-class quiz. Each session is gated by a short online quiz (~10 minutes, mix of multiple-choice and short-answer) released 48 hours before class and due 30 minutes before it starts. Questions check basic comprehension: notation, headline claims, the experimental setup, and one "did you actually read it?" detail from the paper.
- In class. The instructor opens with a 15-min framing, then leads discussion focused on the items the quiz revealed students struggled with, followed by a deep dive into the hardest concept or derivation.
- No exams, no formal write-ups. Comprehension is verified continuously through the quizzes; the rest of your time goes into the final project.
Schedule
Each week pairs one canonical paper with one that builds on or challenges it.
Grading
| Component | Weight & Details |
|---|---|
| Pre-class quizzes | 50% — 12 quizzes, drop the lowest two |
| In-class participation | 15% — discussion quality & engagement |
| Final project | 35% — proposal (Wk 3), draft (Wk 5), final report & demo (Wk 6) |
Final Project
Pick one of three tracks:
- Reproduce. Re-implement a paper from the syllabus on a smaller dataset and verify a key claim.
- Extend. Propose and test a small modification to a syllabus method — new dataset, ablation, or hybrid with another paper's idea.
- Survey. Write a 6-page critical survey of a sub-topic (max 6 papers cited), comparing methods and identifying open questions.
Artifacts: 2-page proposal (end of Wk 3) → 4-page draft for peer review (end of Wk 5) → 6-page final report + 5-min lightning talk (Wk 6).
Note on Logistics
- A week-ahead notice for mid-term, based on the pace of the course.
- The logistic is subject to change based on the overall pace and the performance of the class.
Grading
The following is the outline of the grading:
Grading Rubric
This course is absolute grading, meaning no curve, as there is a certain standard we need to uphold for students to have a good knowledge of algorithm.
| Percentage | Letter Grade | Percentage | Letter Grade |
|---|---|---|---|
| 95-100 | A | 70-74 | C+ |
| 90-94 | A- | 65-69 | C |
| 85-89 | B+ | 60-64 | C- |
| 80-84 | B | 55-59 | D |
| 75-79 | B- | 0-54 | F |
AI Research Reading List
Neural Network Foundations
- Back-Propagation (Rumelhart, Hinton & Williams, 1986) – PDF · notes
- Dropout (Srivastava et al., 2014) – PDF · notes
- ReLU (Nair & Hinton, 2010) – PDF · notes
- Adam (Kingma & Ba, 2014) – arXiv:1412.6980 · notes
- Batch Normalization (Ioffe & Szegedy, 2015) – arXiv:1502.03167 · notes
- RNN Encoder–Decoder (Cho et al., 2014) – PDF · notes
Computer Vision
- AlexNet (Krizhevsky, Sutskever & Hinton, 2012) – NeurIPS 2012 PDF
- VGG (Simonyan & Zisserman, 2014) – arXiv:1409.1556
- ResNet (He et al., 2015) – arXiv:1512.03385
- Inception Net (Szegedy et al., 2015) – arXiv:1409.4842
- U-Net (Ronneberger et al., 2015) – arXiv:1505.04597
- Faster R-CNN (Ren et al., 2015) – arXiv:1506.01497
- YOLO (Redmon et al., 2016) – arXiv:1506.02640
- Mask R-CNN (He et al., 2017) – arXiv:1703.06870
- EfficientNet (Tan & Le, 2019) – arXiv:1905.11946
Natural Language Processing
- BERT (Devlin et al., 2018) – arXiv:1810.04805
- GPT-2 (Radford et al., 2019) – PDF
- RoBERTa (Liu et al., 2019) – arXiv:1907.11692
- T5 (Raffel et al., 2020) – arXiv:1910.10683
- GPT-3 (Brown et al., 2020) – arXiv:2005.14165
Graph Neural Networks
- Graph Attention Networks (Veličković et al., 2018) – arXiv:1710.10903
- Deep Graph Infomax (Veličković et al., 2019) – arXiv:1809.10341
Adversarial Machine Learning
- Adversarial Examples in the Physical World (Kurakin, Goodfellow & Bengio, 2016) – arXiv:1607.02533
- Fast Is Better Than Free (Wong, Rice & Kolter, 2020) – arXiv:2001.03994
Continual Learning
- Overcoming Catastrophic Forgetting (Kirkpatrick et al., 2017) – arXiv:1612.00796
- Learning without Forgetting (Li & Hoiem, 2018) – arXiv:1606.09282