CSE 8673 Machine Learning

This post is intended for preview purposes regarding course selection. Please note that the finalized syllabus may undergo changes.

Last updated on Jul 8, 2024 4 min read

Tentative Course Schedule

This schedule is subject to change depending on the progress of the course

Index	Lecture	Ref	Due
1	Course Intro
2	Supervised Learning: Intro, linear regression	Intro: 1.2, 10, 11; Applied: Supervised Learning; Hands-on: 3, 4, 5
3	Supervised Learning: linear regression, logistic regression	Intro: 10, 11; Applied: Supervised Learning; Hands-on: 3, 4, 5
4	Supervised Learning: linear regression, logistic regression	Intro: 10, 11; Applied: Supervised Learning; Hands-on: 3, 4, 5	Term Paper - Phase 0, Topic selection
5	Supervised Learning: linear regression, logistic regression	Intro: 17; Adv: 18; Hands-on: 3, 4, 5
6	Supervised Learning: logistic regression	Quiz 1: Supervised Learning; Intro: 17; Adv: 18; Hands-on: 3, 4, 5
7	Supervised Learning: logistic regression	Quiz 1 Explanation; Intro: 21; Applied: unsupervised Learning; Hands-on: 9	Term Paper - Phase 1, report
8	Supervised Learning: LDA	Release of HW 1; Intro: 21; Applied: unsupervised Learning; Hands-on: 9
9	Supervised Learning: LDA	Intro: 21; Applied: unsupervised Learning; Hands-on: 9	Term Paper - Phase 1, report revision
10	Supervised Learning: SVM	Quiz 2: Logistic regression, LDA; Intro: 2.3, 3.3, 4.6, 5.1, 5.2, 11.7, 17.2; Adv: 3, 6, 15, 18
11	Supervised Learning: SVM	Quiz 2 Explanation; Intro: 2.3, 3.3, 4.6, 5.1, 5.2, 11.7, 17.2; Adv: 3, 6, 15, 18	Term Paper (Oct 3) - Phase 2, report
12	Midterm, Term Paper Presentation 1/3	Group 1-4; Term Paper - Phase 2, in-class presentation (no submission)
13	Midterm, Term Paper Presentation 2/3	Group 5-10
14	Midterm, Term Paper Presentation 3/3	Group 11-16; Intro: 2.3, 3.3, 4.6, 5.1, 5.2, 11.7, 17.2; Adv: 3, 6, 15, 18
15	Supervised Learning: SVM	Intro: 2.3, 3.3, 4.6, 5.1, 5.2, 11.7, 17.2; Adv: 3, 6, 15, 18	HW 1
16	Supervised Learning: SVM	Intro: 9; Adv: 6; Hands-on: 10, 11	Term Paper - Phase 2, report revision, and presentation slides
17	Supervised Learning: SVM	Intro: 9; Adv: 6; Hands-on: 10, 11
18	Unsupervised Learning: Clustering	Quiz 3; Intro: 9; Adv: 6; Hands-on: 10, 11
19	Unsupervised Learning: Clustering	Intro: 9; Adv: 6; AP: 29
20	Unsupervised Learning: Clustering	Quiz 3 Explanation; Intro: 9; Adv: 6; AP: 29	Release of HW 2 (Nov 3)
21	Optimization: Linear Programming	Intro: 9; Adv: 6; AP: 29	Term Paper - Phase 3, report
22	Optimization: Linear Programming	Intro: 9; Adv: 6; AP: 29
23	Optimization: Integer Programming	Quiz 4; Intro: 9; Adv: 6; IP, 7, 8
24	Optimization: Network Simplex	Quiz 4 Explanation; Intro: 9; Adv: 6; IP, 7, 8	HW 2 (Nov 17)
25	Final, Term Paper Presentation 1/3	Group 1-4, (~12 min / group); Intro: 9; Adv: 6; IP, 7, 8	Term Paper - Phase 4, report; Phase 4, in-class presentation (no submission)
26	Final, Term Paper Presentation 2/3	Group 5-10, (~12 min / group)
27	Final, Term Paper Presentation 3/3	Group 11-15, (~12 min / group); Async Presentation: Group 11, Group 14, Group 15
28	No Class, Reserved for Final Report
29	No Class, Reserved for Final Report		Final Project Report, including revision of Phase 4

Reference Books

[Intro] Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022.
[Adv] Murphy, Kevin P. Probabilistic machine learning: Advanced topics. MIT press, 2022.
[Applied] Rafatirad, Setareh, Houman Homayoun, Zhiqian Chen, and Sai Manoj Pudukotai Dinakarrao. Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective. Springer Nature, 2022.
[Hands-on] Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O’Reilly Media, Inc.", 2022.
[IP] Integer programming (2021) Laurence A. Wolsey
[AP]Cormen, Thomas H., Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to algorithms. MIT press, 2022.

Grading

20% In-Class Quiz (5% * 4)
20% Homework (10% * 2)
60% Term Paper (Group Project)

Grade Policy

10 pt Normal Grade Mode

Letter Grade	Range
A	100% to 90%
B	< 90% to 80%
C	< 80% to 70%
D	< 70% to 60%
F	< 60% to 0%

Examinations and Assignments

All assignments must have your name, student ID and course name/ number. Examinations will heavily emphasize the conceptual understanding of the material. No make-up exams will be given for any other reason than those approved by the college office (serious illness, medical emergency). The exam format will be a mixture of multiple-choice questions and short/long answer questions. If you do not think that your test was graded appropriately, you need to send a valid written explanation for the requested change. This must be done within three days from the date the test was returned to you.

Late Submission Policy

Assignments are required to be submitted by the specified deadlines. Subsequent to the deadline, a penalty of 1% will be deducted from the total score for each hour past the due time, up to a maximum deduction of 50%. If an assignment is submitted after the in-class homework explanation (typically occurring 1-2 weeks post-deadline), submissions will not be accepted, resulting in a score of 0 for the assignment.