CSE353 - Machine Learning
Fall 2021, SUNY Korea
Fall 2021, SUNY Korea
This is a course designed to introduce core concepts in machine learning.
Unfortunately, we probably won't have enough time to cover all major topics in detail. Therefore, this course will serve as a semi-intensive overview of the ML discipline in a wide and shallow manner.
By the end of this semester, students are expected to know the workings of vanilla ML algorithms: what are the basic mathematical models, what kind of problems can they solve, etc, as well as some of the (more recent) core deep learning concepts.
Experience in the following is most recommended:
Linear algebra
Probability/statistics
Optimization
Reading published research articles
There is no single or required textbook for this course. Instead, I will draw materials from the following list of books.
Richard O. Duda, Peter E. Hart, David G. Stork. "Pattern Classification". Wiley-Interscience, 2000. ISBN: 0471056693. (Good beginner's book)
Christopher M. Bishop, "Pattern Recognition and Machine Learning". Springer, 2011. ISBN: 0387310738. (Good intermediary material)
Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective". The MIT Press, 2012. ISBN: 0262018020. (Better suited for advanced readers)
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, "Deep Learning". The MIT Press, 2016. ISBN: 0262035618. (An HTML version can be found here)
The following may be subject to change, so please check back regularly.
Note that we have two (2) correction days on Oct. 8th (Fri.) and Dec. 9th (Thurs.)
To access the lecture slides, you'll need to have logged in with your stonybrook.edu account.
The first two weeks are mostly preliminaries.
Week 1: Introduction to ML (8/30), Preliminary math (9/1)
Week 2: Preliminary math + Maximum likelihood estimation (9/6, 9/8)
Week 3: Bayesian parameter estimation (9/13), Linear regression (9/15)
Week 4: No classes - Happy Chuseok (9/20, 9/22)
Week 5: Linear regression , Logistic regression (9/27, 9/29)
Week 6: No classes (10/4), Midterm 1 (10/6), K-NN classifier (10/8) <-- Make-up day
Week 7: No classes (10/11), Decision trees (10/13)
Week 8: Naive Bayes classifier (10/18), Generative & discriminative models (10/20)
Week 9: Model selection (10/25), Clustering (10/27)
Week 10: Principal component analysis (11/1) Support vector machines (11/3)
Week 11: Support vector machines (11/8) Kernel trick (11/10)
Week 12: Midterm 2 (11/15), Reinforcement learning (11/17)
Week 13: Reinforcement learning (11/22), Neural networks (11/24)
Week 14: Convolutional neural networks (11/29), Recurrent neural networks (12/1)
Week 15: Autoencoders (12/6), PyTorch exercise (12/8), Advanced DL topics (12/9) <-- Make-up day
Final: 12/13, 09:00 - 11:30
Homework - 5 assignments, 6% each = 30%
Pop quizzes - 5 quizzes, 3% each = 15%
Mid-term exams - 2 exams, 7.5% each = 15%
Final exam - 1 cumulative final exam = 30%
Participation - 5% in-class participation + 5% actual attendance = 10%
Students should pursue their academic goals in an honest way that does not put you at an unfair advantage over other students. You are responsible for all work you submitted and representing other’s work as yours is always wrong. Faculty is required to report any suspected instance of academic dishonesty to the school. Regarding your homework, you are encouraged to discuss it with others, but you should write your own code. For more information please refer to http://www.stonybrook.edu/commcms/academic\_integrity/index.html.
If you have a physical, psychological, medical or learning disability that may impact your course work, please let the instructor know. Reasonable accommodation will be provided if necessary and appropriate. All information and documentation are confidential.
Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of Judicial Affairs any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures.