CMPSCI 574/674:
Intelligent Visual Computing: A Neural Network Approach


Time: Fridays 11:15AM - 1:15PM
Location: CS142
Instuctor: Evangelos Kalogerakis
Office hours: Mondays 1:30-3pm (CS 250)
TA Office hours: Wesdnessdays 4-5 pm (CS 207)

Lecture notes, programming resources, and assignments will be posted on the UMass Moodle web site.

Course Description

Intelligent visual computing is an emerging new field that seeks to combine modern trends in computer graphics, augmented reality, virtual reality, and computer vision with machine learning to intelligently process and synthesize 2D/3D visual content and animations. The course will start by covering the basic principles and representations for modeling images, shapes, deformations, and animations. It will then continue with the fundamentals of machine learning and deep networks, and their application to 2D/3D visual content analysis and synthesis. In particular, the course will cover deep learning methods for 3D shape reconstruction, classification, segmentation, correspondences, synthesis, motion analysis and animation of 3D models.

Students will complete 5 programming assignments in Matlab/Octave and work on a course project related to visual computing with machine learning.

There are no prerequisites for CMPSCI graduate students, although familiarity with probability, statistics, linear algebra, undergraduate computer graphics and machine learning is essential!

For others and undergrads, the course has the following prerequisites:
- Introduction to Algorithms (311 or equivalent) with grade B or better
- Artificial intelligence (383 or equivalent) with grade B or better
- Introduction to Computer Graphics (373, 473 or equivalent) with grade B or better

This course counts as a CS Elective toward the CMPSCI major (BA/BS) and can satisfy a second or third AI core requirement in the CMPSCI PhD and MSc program. It offers 3 credits.

Week-by-week outline (tentative)

Week 1: Image and Shape Representations, Basics of Classification & Regression [+assignment 1]
Week 2: Neural Networks, Convolutional Networks [+assignment 2]
Week 3: Generatative Networks
Week 4: 3D Deep Learning, Multi-view methods, Applications to 3D Shape Segmentation and Correspondences [+assignment 3]
Week 5: 3D Deep Learning, Volumetric methods
Week 6: 3D Deep Learning, Point-based methods [+assignment 4]
Week 7: Surface reconstruction, Alignment
Week 8: Recurrent Neural Networks, Applications to 3D Animation [+assignment 5]
Week 9: Shape Deformations
Week 10: Probabilistic Graphical Models for 3D Shape and Scene Modeling
Week 11: Graph Neural Networks
Week 12-13: Computer graphics paper presentations / project presentations


Complete five assignments
- Research paper presentation
- Research paper reaction reports
- Class participation and discussions.

- Complete five assignments
- A term project. This should be an implementation of a machine learning algorithm related to visual computing. The result of the project should be a prototype with some preliminary results, or could lead to a submission of a research paper in the future.
- Research paper reaction reports.
- Class participation and discussions.

Marking scheme

90% assignments, reaction reports
10% paper presentation

75% assignments, reaction reports
25% project

Scores to letter conversions:
95%-100% A
90%-95% A-
85%-90% B+
80%-85% B
75%-80% B-
70%-75% C+
65%-70% C
60%-65% C-
55%-60% D+
50%-55% D

Note: A course grade below C is not a passing grade for graduate students.

Accommodation Statement

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students.  If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course.  If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.

Academic Honesty Statement

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst.  Academic dishonesty is prohibited in all programs of the University.  Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty.  Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty.  Instructors should take reasonable steps to address academic misconduct.  Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible.  Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair.  Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent (


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