CSCE 633: Machine Learning (Fall 2019)




Dr. Zhangyang (Atlas) Wang


Office: 328C HRBB (visit by appointment only except office hours)



TA: Tianlong Chen


Office: 407 HRBB



Time and Location

·       Lecture time: 5:45 - 7:00 pm, every Monday and Wednesday

·       Lecture location: HRBB 124

·       Instructor Office Hour: 2:00 3:00 pm every Tuesday

·       TA Office Hour:  9:00 10:00 am every Thursday

·       Class fully seated. NO AUDITION ALLOWED.


Course Description

Machine learning is a sub-field of Artificial Intelligence that gives computers the ability to learn and/or act without being explicitly programmed. Topics include various supervised, unsupervised and reinforcement learning approaches (including deep learning), optimization procedures, and statistical inference.


Course Goal

The students will digest and practice their knowledge and skills by class discussion and exams, and obtain in-depth experience with a particular topic through a final project.


Evaluation Metrics

Grading will be based on four take-home assignments (5% each), one mid-term exam (40%), and one final project (40%) (proposal 10% + presentation 10% + code review 10% + report 10%).  There will be no final exam. 


Final project Collaborations and teamwork are encouraged, but must be coordinated and approved by the instructor. A team can only have 2 members.

The project proposal, report and codes should be all submitted via email. For late submission, each additional late day will incur a 10% penalty.


The grading policy is as follows:













It's important that you work on a real machine learning project, or a real problem in some relevant domain, so that you earn first-hand experience. The instructor is available to discuss and shape the project. The scale of the project should be scheduled as one semester long. This year, we will host a project competition, and the scope details will be announced in class.


By the end of the semester, you should submit your code and data for this project, write a project report of maximum 8 pages (plus additional pages containing only references) using the standard CVPR paper template, and prepare a class presentation. The instructor will be happy to help develop promising project ideas into a formal publication during or after the semester, if you wish so.



·       Students should have taken the following courses or equivalent: Data Structure and Algorithms (CSCE 221), Linear Algebra (MATH 304 or MATH 323), Numerical Methods (MATH 417), and (preferably) Artificial Intelligence. 

·       Coding experiences with Python, Matlab, or C/C++ are assumed.

·       Previous knowledge of machine learning, computer vision signal processing or data mining will be helpful, but not necessary.


Reading Materials

This course does not follow any textbook closely. Among many recommended readings are:

1.     Introduction to Machine Learning, Ethem Alpaydin (2014), MIT Press. [Book home page (3rd edition)] [Book home page (2nd edition)] [Book home page (1st edition)]

2.     Pattern Recognition and Machine Learning, Christopher M. Bishop (2006). [A Bayesian view]

3.     The Elements of Statistical Learning, Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie (2001), Springer. [Warning: not so elementary but quite insightful]

4.     Sparse Coding and its Applications in Computer Vision, Wang et. al. (2015), WorldScientific.

5.     Convex Optimization, Stephen Boyd and Lieven Vandenberghe (2004), Cambridge University Press. [Their CVX toolbox is a great Matlab-based convex optimization tool for beginners]

6.     Distributed optimization and statistical learning via the alternating direction method of multipliers, Stephen Boyd et. al. (2011). [Dedicated reference for ADMM]

7.     Linear Algebra and its Applications, Gilbert Strang (1988). [For those who want to simply keep a concise reference for linear algebra, my best recommendation is The Matrix Cookbook]

8.     Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016), MIT Press.


Lecture Notes (in PDF format) will be uploaded to the course webpage no more than 24 hours AFTER each class.


Attendance and Make-up Policies

Every student should attend the class, unless you have an accepted excuse. Please check student rule 7 for details.


Academic Integrity

Aggie Code of Honor: An Aggie does not lie, cheat or steal, or tolerate those who do. see: Honor Council Rules and Procedures


Americans with Disabilities Act (ADA) Statement

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, currently located in the Disability Services building at the Student Services at White Creek complex on west campus or call 979-845-1637. For additional information, visit



(Further minor changes may occur due to potential changes of the instructor’s schedule, and will be notified separately via email)


Week 1



1.     Introduction (by TA) [Slides]


2.     Basic ML Theory and Concepts (i)

Week 2



3.     Basic ML Theory and Concepts (ii) [Slides]


4.     Linear Classifier [Slides]

Week 3

(Due by Week 3 Sunday: Register Project Teams)


5.     SVM Classifier and Kernel Methods (i)


6.     SVM Classifier and Kernel Methods (ii) [Slides]

Week 4



7.     Other Popular Classifiers, and Clustering (i) (by TA)


No class (Instructor travel)

Week 5



8.     Other Popular Classifiers, and Clustering (ii)


9.     Other Popular Classifiers, and Clustering (iii) [Slides]

Week 6



10.  Dimensionality Reduction and Regression (i)


11.  Dimensionality Reduction and Regression (ii) (by TA) [Slides]

Week 7


12.  Sparsity and Low-Rank (i) (by TA)


13.  Sparsity and Low-Rank (ii) [Slides]

Week 8

(Due by Week 8 Sunday: Submit Project Proposal)


14.  Sparsity and Low-Rank (iii) [Slides]


15.  Multi-Task Learning and Transfer Learning [Slides]

Week 9



16.  Deep Learning: Basic Components (by TA)


No class (Instructor travel)

Week 10



Hands-On Class: Building A Deep Learning Model (by TA)


Midterm Exam

Week 11



No class (Instructor travel)


17.  Deep Learning (ii): Representative Models (1)

Week 12



18.  Deep Learning (ii): Representative Models (2)


19.  Deep learning (iii): Optimization and Implementation (1)

Week 13



20.  Deep learning (iii): Optimization and Implementation (2) [Slides]


21.  Deep Learning (iv): Applications Overview (1)

Week 14



22.  Deep Learning (iv): Applications Overview (2) [Slides]


No class (Thanksgiving)

Week 15



No class (university redefined day)


Final Project Presentations [Slides]