CSE 4693/6693 Introduction to 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 3 min read

Tentative Course Schedule

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

Index	Lecture	Due
1	Course Introduction	Quiz 0: Pre-course Survey
2	Fundamental: Formulation of ML task, Classification/Regression
3	Fundamental: Formulation of ML task, Classification/Regression
4	Fundamental: Analytic Methods for Regression
5	Fundamental: Analytic Methods for Regression	Quiz 1
6	Fundamental: Analytic Methods for Regression	release: HW 1
		Project: Group and Topic
7	Fundamental: Gradient Methods for Regression
8	Fundamental: Logistic method for Classification
9	Fundamental: Logistic method for Classification	Quiz 2
10	Probabilistic: Bayes Rule	HW 1
11	Probabilistic: Bayes Rule	Project: Proposal
12	Project Presentation
13	Project Presentation
14	Probabilistic: Bayes Rule
15	Probabilistic: Bayes Rule	Quiz 4
16	Probabilistic: Bayes Rule	Explain HW1, Midterm, Quiz 4
17	Probabilistic: Bayes Rule
18	Probabilistic: Bayes Rule	release: HW 2
		Project: Mid-point progress check
19	Probabilistic: Bayes Rule
20	Advance: Gaussian Process
21	Advance: Gaussian Process	Quiz 5
22	Advance: Bayesian Optimization	HW 2
23	Project Presentation
24	Project Presentation
25	Project Presentation
26		Final Report Due

Grading

25% In-Class Quiz
- 2 graded surveys (5% = 2 * 2.5%)
- 4 graded quizzes (20% = 4 * 5%)
20% Homework (10% * 2)
15% Midterm
40% Group Project
- topic selection and team building, 5%
- mid-term presentation, 10%
- mid-point check, 5%
- final presentation, 20%

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%

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.

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.