CS59300CVD: Comp. Vision With Deep Learning (Fall 2025)

Course Information

Computer vision is a field that focuses on building machines that can see. In this course, we will cover the fundamentals of major tasks in computer vision, starting from the basics of image formation to modern computer vision methods based on deep learning. By the end of this course, students will have a solid foundation for conducting research in computer vision and the necessary technical background to understand and implement state-of-the-art vision papers.

Pre-requisites:

• CS 37300 Data Mining & Machine Learning
• MA 26500 Linear Algebra
• STAT 41600 Probability

Textbook:

• [FP] Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2nd ed.)
• [RS] Computer Vision: Algorithms and Applications by Richard Szeliski (2nd ed.)
• [DDL] Dive into deep learning by Zhang, Aston, et al.

Grading:

The final grade will be curved and no stricter than the cutoff: A+: 97-100, A: 93-96, A-: 90-92, B+: 87-89, ..., etc.
The percentage is computed following (without any rounding):

• Assignments: 50% (12.5% each assignment)
• Midterm: 25%
• Final Project: 25%

FAQ:

• Lecture slides will be posted on Brightspace. Some materials are from other Professors as referenced in the slides; Do not redistribute.
• The instructor & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
• During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code/solution.
• Tutorial for learning Latex with Overleaf: [Link]

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Course Schedule

The following schedule is tentative and subject to change.

Date	Event	Description	Readings
Aug 26	Lecture 1	Introduction + Applied DL -- Formulation	DDL 3
Aug 28	Lecture 2	Applied DL -- Network & Training	DDL 2
Sep 2	Info.	Assignment 1 Released Select from the following:
Sep 2	Lecture 3	Image Processing - I	RS 2
Sep 4	Lecture 4	Image Processing - II
Sep 9	Lecture 5	Image Filtering - I	FP 4, DDL 7
Sep 11	Lecture 6	Image Filtering - II + CNN	RS 3.4
Sep 16	Lecture 7	Edge / Corner Detection - I	FP 5.1-5.2
Sep 18	Lecture 8	Edge / Corner Detection - II + CNN	FP 5.3
Sep 19	Deadline	Assignment 1 Due at 11:59PM Select from the following:
Sep 23	Info.	Assignment 2 Released Select from the following:
Sep 23	Lecture 9	SIFT - I
Sep 25	Lecture 10	SIFT - II
Sep 30	Lecture 11	Fitting & Alignment - I	FP 10.2-10.4, 22.1
Oct 2	Lecture 12	Fitting & Alignment - II	FP 12.1
Oct 7	Lecture 13	Fitting & Alignment - III
Oct 9	Lecture 14	Cameras, Light, and Shading	FP 1
Oct 10	Deadline	Assignment 2 Due at 11:59PM Select from the following:
Oct 13	Info.	Assignment 3 Released Select from the following:
Oct 14	Break	Fall Break Select from the following:
Oct 16	Lecture 15	Cameras, Light, and Shading	FP 2
Oct 17	Deadline	Project Proposal Due at 11:59PM Select from the following:
Oct 21	Lecture 16	Midterm Review
Oct 23	Deadline	Midterm Select from the following:
Oct 28	Lecture 17	Color + Perspective Projection - I	FP 1
Oct 30	Lecture 18	Perspective Projection - II
Nov 4	Lecture 19	Camera Calibration & Single-View Modeling - I	FP 1
Nov 6	Lecture 20	Camera Calibration & Single-View Modeling - II
Nov 7	Deadline	Assignment 3 Due at 11:59PM Select from the following:
Nov 11	Info.	Assignment 4 Released Select from the following:
Nov 11	Lecture 21	Epipolar Geometry & Structure from Motion - I	FP 7.1
Nov 13	Lecture 22	Epipolar Geometry & Structure from Motion - II
Nov 18	Lecture 23	Two-View Stereo
Nov 20	Lecture 24	Multi-View Stereo
Nov 25	Lecture 25	Light Field Modeling
Nov 27	Break	Thanksgiving Select from the following:
Dec 2	Lecture 26	Image Classification, Segmentation, Detection - I
Dec 4	Lecture 27	Language and Vision + Final Remarks
Dec 5	Deadline	Assignment 4 Due at 11:59PM Select from the following:
Dec 9	Presentation	Project Presentations Select from the following:
Dec 11	Presentation	Project Presentations Select from the following:
Dec 12	Deadline	Final Project Report Due at 11:59PM Select from the following: