Miaomiao Zhang
ECE / CS 4501 / 6501: Machine Learning in Image Analysis
Description: This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed to build their own models for effective problem solving. Topics of image denoising/reconstruction, deformable image registration, numerical analysis, probabilistic modeling, data dimensionality reduction, and convolutional neural networks for image segmentation/classification will be covered. The main focus might change from semester to semester. The graduate students (ECE/CS 6501) will be given additional programming tasks and more advanced theoretical questions.
Mathematical background in linear algebra, multivariate calculus, probability and statistics, and programming skills are required in this class.
Class meetings: MW 3:30-4:45pm
Class location: Chemical Engineering Bldg 005 (lectures will also be streamed and recorded online)
Instructor: Miaomiao Zhang (mz8rr -at- virginia.edu)
Teaching Assistant: Jian Wang (jw4hv -at- virginia.edu); Peiyun Zhao (pz8wv -at- virginia.edu)
Office hours: Fridays 11am-12pm (instructor); TTh 3:00-5:00pm (TA)
Lecture materials including slides and notes will be posted on UVA Collab
Grading
* Projects (3 course projects and 1 final project) (20% * 3 + 10% final project presentation + 10% final project report + 20% final project implementation)
No exams are required in this course.
All programming will be in Matlab or Python.
All reports must be written in LaTeX and submitted as a PDF.
Homeworks are due by midnight (11:59:59 PM) on the due date. Late penalty will be applied: 10% off (1 day), 20% off (2 days), 30% off (3 days). Late submissions are not accepted after three late days.
Schedule
| Date | Topics | Projects |
| Week 1 | Aug 25 | Course Introduction |
| Week 2 | ||
| Aug 30 | Introduction to Image Analysis and Basic Variational Methods | |
| Sep 1 | Image Denoising I: Convolution, Fourier Transformation | PS1 Release |
| Week 3 | ||
| Sep 6 | Image Denoising II: Total Variation | |
| Sep 8 | Basics of Image Registration | |
| Week 4 | ||
| Sep 13 | Deformable Image Registration | |
| Sep 15 | Roundtable Paper Reading and Discussion (papers will be uploaded online) | PS1 Due |
| Week 5 | ||
| Sep 20 | Population studies: Fréchet mean, image atlas estimation | PS2 Release |
| Sep 22 | Data Dimensionality Reduction: Principal Component Analysis (PCA) | |
| Week 6 | ||
| Sep 27 | Nonlinear PCA methods, kernel trick | |
| Sep 29 | Regression Methods I | |
| Week 7 | Oct 4 | Image Regression Methods |
| Oct 6 | Roundtable Paper Reading and Discussion (papers will be uploaded online) | PS2 Due |
| Week 8 | ||
| Oct 11 | Reading days (no class) | |
| Oct 13 | Maximum likelihood (MLE), Maximum a Posteriori (MAP) | PS3 Release |
| Week 9 | ||
| Oct 18 | Bayesian Methods | |
| Oct 20 | Sampling Methods: Markov Chain Monte Carlo | |
| Week 10 | ||
| Oct 25 | Image Data Augmentation / Synthesis | |
| Oct 27 | Image Classification / Segmentation I | PS3 Due |
| Week 11 | ||
| Nov 1 | Image Classification / Segmentation II | Final Project Release |
| Nov 3 | Roundtable Paper Reading and Discussion (papers will be uploaded online) | |
| Week 12 | ||
| Nov 8 | Neural Network | |
| Nov 10 | Back propagation | |
| Week 13 | ||
| Nov 15 | (Variational) AutoEncoders | |
| Nov 17 | Deep Neural Network | |
| Week 14 | ||
| Nov 22 | Multi-task Learning and its applications | |
| Nov 24 | Thanksgiving break (no class) | |
| Week 15 | ||
| Nov 29 | Final project presentation I | |
| Dec 1 | Final project presentation II | |
| Week 16 | Dec 6 | No class - please work on your final projects |
| Dec 13 | Final project Due |
COVID Mask Policy
Every student who attends the class in-person MUST wear masks regardless of vaccination status untill the university policy changes. Anyone who is non-compliant on masking will be treated as a violation of University Policy SEC-045.
Disclaimer
The instructor reserves the right to make changes to the course schedule, syllabus, and project deadlines. Changes will be announced early in advance.
Honor Code (Adapted from Honor Syllabus Example Statement of UVa)
I expect students in this class to fully comply with all of the provisions of the University’s Honor Code. By enrolling in this course, you are requested to abide by and uphold the Honor System of the University of Virginia and policies specific to this course by default. All suspected violations will be forwarded to the Honor Committee, and you may, at my discretion, receive an immediate zero on that assignment regardless of any action taken by the Honor Committee. Please let me know if you have any questions regarding the course honor policy. If you believe you may have committed an Honor Offense, you may wish to file a Conscientious Retraction by calling the Honor Offices at (434) 924-7602. For your retraction to be considered valid, it must, among other things, be filed with the Honor Committee before you are aware that the act in question has come under suspicion by anyone. More information can be found at https:honor.virginia.edu/.
Students With Disabilities or Learning Needs
It is my goal to create a learning experience that is as accessible as possible. If you anticipate any issues related to the format, materials, or requirements of this course, please meet with me outside of class so we can explore potential options. Students with disabilities may also wish to work with the Student Disability Access Center to discuss a range of options to removing barriers in this course, including official accommodations. Please visit their website for information on this process and to apply for services online: sdac.studenthealth.virginia.edu. If you have already been approved for accommodations through SDAC, please send me your accommodation letter and meet with me so we can develop an implementation plan together.
Recording of Classroom Activities
Every lecture will be recorded to accommodate students who are not able to join in-person. These recordings can only be used for the purpose of individual or group study with students enrolled in this class during this semester. You may not distribute them in whole or in part through any other platform or to any person outside of this class, nor may you make your own recordings of this class, unless the instructor and all students who are present have been informed that recording will occur. For more details, please refer to UVA Provost Policy-008.