- This course (aka unit from now on) was originally designed for various elite class Bachelor students or research students in some top Asia Pacific universities, including Southeast University, University of Chinese Academy of Sciences, Nanjing University of Science and Technology, Vellor Institute of Technology, SRM Institute of Science & Technology etc. (since 2012).
- Materials in this course include resources collected from various open-source online repositories. You are free to use, change and distribute this package.
- If you found any issue/bug for this site, please submit an issue at tulip-lab/pattern-classification:
- Pull requests are welcome:
- Prerequist unit 👉 :
- Subsequent unit 👉 :
- Point of Contact 👉 : Prof. Gang Li
Prepared by TULIP Lab
This course (aka unit) delves into the foundational aspects of automated pattern recognition and its associated methods. The primary focus is on the fundamental theories and frameworks of statistical pattern recognition, with practical applications in computer vision, social science data analysis, and other relevant domains.
The initial portion of the course concentrates on generative methods rooted in Bayes decision theory, encompassing techniques for parameter estimation and density estimation. Following that, the course shifts attention towards discriminative methods such as support vector machines, as well as non-parametric techniques, notably nearest-neighbor classification.
Pattern classification plays a pivotal role in various applications, such as information retrieval, data mining, multimedia analysis and recognition, computational linguistics, information forensics, biometrics, and bioinformatics. Additionally, this subject introduces additional topics derived from real-world research projects, providing students with practical insights and exposure to contemporary advancements in the field.
Students will have access to a comprehensive range of subject materials, comprising slides handouts, practicals, assessment documents, and relevant readings. It is recommended that students commence their engagement with each session by thoroughly reviewing the pertinent slides handouts and readings to obtain a comprehensive understanding of the content.
Additionally, students are encouraged to supplement their knowledge by conducting independent research, utilizing online resources or referring to textbooks that cover relevant information related to the topics under study.
You are recommended to use Cloud platform to run the materials, such as:
- Google Colab: which will be used in the practical classes.
The sister repository of this unit can be found at:
This unit needs a total of 48 class hours, including 32 hours teaching, and 16 hours student presentation/discussion. The unit plan is as below:
| 🔬 Session |
🏷️ Category |
📒 Topic |
🎯 ULOs |
👨🏫 Activity |
|---|---|---|---|---|
| 0️⃣ | Preliminary | 📖 Induction | ULO1 |  |
| 1️⃣ | Preliminary | 📖 Math Foundations | ULO1 | |
| 2️⃣ | Core | 📖 Bayesian Decision Theory | ULO1 ULO2 | |
| 3️⃣ | Core | 📖 Parameter Estimation | ULO1 ULO2 | |
| 4️⃣ | Core | 📖 Parametric Model | ULO1 ULO2 | |
| 5️⃣ | Core | 📖 Non-Parametric Model | ULO1 ULO2 | |
| 6️⃣ | Advanced | 📖 Stochastic Methods | ULO1 ULO2 | |
| Student Work | 📖 Advanced Topics in Pattern Recognition | ULO3 | |
|
| 7️⃣ | Core | 📖 Discriminant Functions | ULO1 ULO2 | |
| 8️⃣ | Core | 📖 Model Evaluation | ULO1 ULO2 ULO3 | |
| 9️⃣ | Advanced | 📖 Deep Learning | ULO1 ULO2 ULO3 | |
| 🔟 | Advanced | 📖 Data Privacy | ULO1 ULO2 | |
| Student Work | 📖 Advanced Topics in Pattern Recognition | ULO3 | |
|
| 🏆 | Advanced | 📖 [Invited Talk and Discussions] | ULO1 ULO2 | |
The assessment of the unit is mainly aimed at assessing the students' achievement of the unit learning outcomes (ULOs, a.k.a. objectives), and checking the students' mastery of those algorithms/models and theory covered in the unit.
The detailed assessment specification and marking rubrics can be found at: S00D-Assessment. The relationship between each assessment task and the ULOs are shown as follows:
| 🔬 Task |
👨🏫 Category |
🎯 ULO1 |
🎯 ULO2 |
🎯 ULO3 |
Percentage |
|---|---|---|---|---|---|
| 1️⃣ | Presentation | 50% | 25% | 25% | 25% |
| 2️⃣ | Project | 30% | 70% | 50% | |
| 2️⃣ | Report Presentation |
20% | 40% | 40% | 25% |
-
SEU 2024 - The final assessment files submission due date is 🗓️
Saturday, 23/11/2024, group of No More Than THREE (1, 2 or 3) members for both tasks. -
SEU 2023 - The final assessment files submission due date is 🗓️
Saturday, 25/11/2023, group of ONE member only (individual work) for both tasks.
It is expected that you will submit each assessment component on time. You will not be allowed to start everything at the last moment, because we will provide you with feedback that you will be expected to use in future assessments.
㊙️
If you find that you are having trouble meeting your deadlines, contact the Unit Chair.
This course uses several key references or textbooks, together with relevant publications from TULIP Lab:
- Pattern Classification, 2nd Edition by Richard O. Duda, Peter E. Hart, and David G. Stork
- Research Publications, various resources and readings
Thanks goes to these wonderful people 🌷
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