2023-2 Machine Learning (CSED515-01) The course syllabus

1.Course Information

Course No. CSED515 Section 01 Credit 3.00
Category Major elective Course Type Classroom Course prerequisites
Postechian Core Competence
Hours TUE, THU / 15:30 ~ 16:45 / Hogil Kim Bldg[308]Lecture Room Grading Scale G

2. Instructor Information

Ok Jungseul Name Ok Jungseul Department Grad. School of AI
Email address jungseul@postech.ac.kr Homepage https://sites.google.com/view/jungseulok
Office ML-LAB Office Phone 054-279-2242
Office Hours upon appointment

3. Course Objectives

Machine learning is the study of algorithms and statistical methods that computers use to "learn" patterns and inferences in order to perform a specific task without explicit instruction. This course mainly aims at providing mathematical/statistical methods, which are essential in machine learning. A wide range of topics will be covered, including but not limited to density estimation, latent variable models, mixture models, clustering, classification, dimensionality reduction, regression, support vector machines, kernel methods, multi-layer perceptrons, and deep learning.

4. Prerequisites & require

A basic understanding of probability/statistics and linear algebra
Python programming language

5. Grading

Assignments: 30% (five or six assignments)
Midterm exam: 30%
Final exam: 30%
Pop-up quiz + attendance: 10% (no exception unless excuses are given in advance)

6. Course Materials

Title Author Publisher Publication
Year/Edition
ISBN
Machine Learning: A Probabilistic Perspective Kevin P. Murphy MIT Press 2012 0262018020
Pattern Recognition and Machine Learning Christopher M. Bishop Springer 2013 8132209060

7. Course References

Machine Learning: A Probabilistic Perspective (by K. Murphy, 2012)
Pattern Recognition and Machine Learning (by C. Bishop, 2006, Springer)

8. Course Plan

https://docs.google.com/spreadsheets/d/1hyLHHOfrCPbpAXmP0PT6z1V5xQ7DvIHlFsf_4cy3sUg/edit?usp=sharing

9. Course Operation

Lecture: 15:30-16:45 Tuesday/Thursday
Assignment: every two weeks (approx.)
Exam: midterm and final (closed book)

10. How to Teach & Remark

-

11. Supports for Students with a Disability

- Taking Course: interpreting services (for hearing impairment), Mobility and preferential seating assistances (for developmental disability), Note taking(for all kinds of disabilities) and etc.

- Taking Exam: Extended exam period (for all kinds of disabilities, if needed), Magnified exam papers (for sight disability), and etc.

- Please contact Center for Students with Disabilities (279-2434) for additional assistance