深度學習於電腦視覺 Deep Learning for Computer Vision
深度學習於電腦視覺 Deep Learning for Computer Vision
The syllabuses on both this page and the NTU online course information are synchronized.
Course Information
Item | Content |
Course title | Deep Learning for Computer Vision |
Semester | 111-1 |
Designated for |
GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING Intelligent Medicine Program |
Instructor | YU-CHIANG WANG |
Curriculum No. | CommE 5052 |
Curriculum Id No. | 942 U0660 |
Class | |
Credit | 3 |
Full/Half Yr. | Half |
Required/Elective | Elective |
Time | Tuesday 2,3,4(9:10~12:10) |
Place | 博理112 |
Remarks |
Course Syllabus
Item | Content |
Course Description | |
Course Objective | |
Course Requirement | |
Student Workload (expected study time outside of class per week) | |
References | |
Designated Reading |
Progress
Week | Date | Topic |
Week 1 | 09/06 | Course logistics & registration; Machine Learning 101 |
Week 2 | 09/13 | Introduction to Convolutional Neural Networks (I) |
Week 3 | 09/20 | Introduction to Convolutional Neural Networks (II) Tutorials on Python, Github, etc. (by TAs) |
Week 4 | 09/27 | Object Detection & Segmentation; Generative Model |
Week 5 | 10/04 | Generative Adversarial Networks, and Diffusion Model |
Week 6 | 10/11 | Transfer Learning for Visual Classification & Synthesis |
Week 7 | 10/18 | Guest Lecture (TBD) |
Week 8 | 10/25 | Recurrent Neural Networks |
Week 9 | 11/01 | Transformer; Vision & Language (I) |
Week 10 | 11/08 | Vision & Language (II); Few-Shot Learning (I) |
Week 11 | 11/15 | N/A |
Week 12 | 11/22 | 3D Vision |
Week 13 | 11/29 | Announcement of Final Project |
Week 14 | 12/06 | Self-Supervised Learning & Guest Lecture |
Week 15 | 12/13 | Federated Learning, Domain Generalization and More Advanced Topics |
Week 17 | 12/29 Thur | Presentation for Final Projects |
Grading
NO | Item | Pc | Explanations for the conditions |
1 | HW 1 | 15% | |
2 | HW 2 | 15% | |
3 | HW 3 | 18% | |
4 | HW 4 | 18% | |
5 | Final Project | 34% |
Adjustment methods for students
Adjustment method | |
Teaching methods | |
Assignment submission methods | |
Exam methods | |
Others |
Office Hour
NO | Day | Start time | End time |
Remarks | None |