I’m a teaching assistant staff for Graduate Level course CAP 5638: Pattern Recognition at CS FSU, collaborated with Dr. Yushun Dong.

Administrivia

• 📢 Instructor: Dr. Yushun Dong (yd24f[at]fsu[dot]edu)
• 💡 Teaching Assistant: Lincan Li (ll24bb[at]fsu[dot]edu)
• 📅 Time: Tuesday & Thursday, 4:50 pm-6:05 pm (ET)
• 🏫 Location: Love Building 301
• 🔍 Instructor Office Hours: Tuesday & Thursday, 6:05 to 7:05 PM at LOV 301.
• 🔍 TA Office Hours: to be determined.
• 🎒 Format: In-person only (unless there is a drastic change in the situation).

Course Overview

🚀 Welcome to the exciting world of pattern recognition, a field dedicated to discovering and interpreting patterns in data! In this course, we’ll dive into the fundamental techniques of pattern recognition, from feature extraction and dimensionality reduction to classification and clustering. You’ll learn how to design models that can recognize and categorize patterns, making sense of complex datasets in various applications such as image and speech recognition, bioinformatics, and computer vision.

📘 This course will draw on materials from the textbook as well as key literature in pattern recognition and machine learning. You will study theoretical concepts, complete assignments, work on a course project, present your findings in class, and take a final exam. A solid understanding of probability theory and linear algebra is essential, along with strong programming skills for implementing algorithms in the course project.

Textbook

Pattern Classification

Authors: Richard O. Duda, Peter E. Hart, David G. Stork

Website: https://www.wiley.com/en-br/Pattern+Classification%2C+2nd+Edition-p-9780471056690

Prerequisite

No hard prerequisite.

Recommended prerequisite: ISC 3222 or ISC 3313 or ISC 4304C or COP 3330 or COP 4530.

If you have not taken any of the prerequisite above, you are recommended to complete one Kaggle competition (a most famous and simple example is here) — this will bring you a sense of how the project and homework of this course would be like and what knowledge we are going to learn. Take this course if you like them (^_^)

Grading

• Assignments (20%): There will be several homework assignments (written and coding-based) spaced out over the course of the semester. All the assignments will be equally weighted. Submission and other instructions will be posted on Canvas.

• Project Proposal & Presentation (30%): There will be a semester-long project where the goal is to solve a challenging real-world pattern recognition problem. Students will work in groups for this term project. Students will need to submit a project proposal outlining the project idea with a hard deadline of 23:59 PM (ET) on 2.17th (20% of final grades). This project proposal is strictly two-page maximum for the main content, with unlimited pages of references and appendices, together with any type of supplementary materials under 50 MB. Students will also be required to present their proposed ideas (10% of final grades) after the submission of the proposal. Several slots will be assigned for each class in a random order among all groups. Each group will be given 10 minutes for presentation and 2 minutes for Q&A (12 minutes in total, subject to changes).

• Final Project Report & Presentation (50%): Students will need to submit a final report (20% of final grades) and the code with a hard deadline of 23:59 PM (ET) on 4.07. This project report is strictly eight-page maximum for the main content, with unlimited pages of references and appendices, together with any type of supplementary materials under 50 MB. Only Python or MATLAB will be allowed for the implementations. Students will also be required to present their projects (30% of final grades) at the end of this semester. Several slots will be assigned for each class in a random order among all groups. Each group will be given 18 minutes for presentation and 2 minutes for Q&A (20 minutes in total, subject to changes).

Please see a detailed introduction of Project Proposal and Final Project Report & Presentation here.

• 🎁 Extra Bonus: (1) Students are highly encouraged to prepare for submissions to major AI/ML/DM conferences based on their projects. Please be sure to make an appointment with the instructor prior to any submission plans to perform a comprehensive evaluation of the research topic. Each submission under the instructor’s recognition will gain 7 points on their final grades; (2) Students are highly encouraged to provide feedback on the development of this course. At the end of this semester, a feedback survey completion rate exceeding 70% leads to an additional 7% for everyone’s actual grade, i.e., your_final_grade = your_actual_grade * 107%‼️