SDSC5001 Course Information

#SDSC5001 #Course Information

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SDSC5001 Course Overview

Course Code: SDSC5001

Course Title: Statistical Machine Learning I

Semester: Semester A 2025/26

Instructor: Prof. GUO Xingyu

Email: xingyguo@cityu.edu.hk

Office: Mong Man Wai Building, Room 4462

  • Lecture Time: Saturday 9:00 – 11:50

  • Office Hour: Monday 16:00 – 17:00

  • Teaching Mode: Face-to-face

  • TAs:

    • LIN Jiajun (jiajunlin4-c@my.cityu.edu.hk)
    • LI Xiaopeng (Lee.Xiao-Peng@my.cityu.edu.hk)

Assessments

Component Weight Details
Homework 20% 3 assignments (ungraded but mandatory). Late submissions penalized.
Midterm 10% Week 8 (Oct 25). Closed book; 1 A4 notesheet allowed.
Project 20% Group work (4-5 students). Details above.
Final Exam 50% Covers all materials. Closed book; 1 A4 notesheet allowed.

Schedule & Teaching

Week Date Activity Content Due Dates
1 6-Sep Lecture Course Overview, Policy, TA Introduction
Project, Exam
2 13-Sep Lecture Review of Probability & Statistics
Tutorial 1 Introduction on Python
3 20-Sep Lecture Data Exploration
Statistical Machine Learning
Tutorial 2 Data exploration (Iris data)
4 27-Sep Lecture Statistical Machine Learning
Linear Regression		 Assignment 1 Posted
Tutorial 3 Cross validation, linear regression
5 4-Oct Lecture Linear Regression
Model Selection and Regularization		 Assignment 1 Due
Project Group Due
Tutorial 4 Subset selection, Shrinkage methods, PCR and PLS
6 11-Oct Lecture Model Selection and Regularization
Classification
Tutorial 5 Classification methods
7 18-Oct Lecture Classification
(Q&A for Midterm)
8 25-Oct Lecture Midterm Exam Assignment 2 Posted
9 1-Nov Lecture Model beyond Linearity
Tutorial 6 Nonlinear methods Project Proposal Due
10 8-Nov Lecture Tree methods Assignment 2 Due (Nov 10)
Tutorial 7 Tree methods
11 15-Nov Lecture SVM Assignment 3 Posted
Tutorial 8 SVM methods
12 22-Nov Lecture SVM
13 29-Nov Lecture Summary
Project Q&A		 Assignment 3 Due
Project Report Due
(Graded by all TAs)

Project Details (20% of total grade)

Key Deadlines:

  1. Team Formation (1 point)

    • Submit team name and members by Oct 5 (Sun) @ 11:59 PM
    • File name: Team-Formation-XX (XX = team name)

  2. Project Proposal (4 points)

    • Submit PDF proposal (≥0.5 pages) by Nov 2 (Sun) @ 11:59 PM
    • File name: Team-Proposal-XX
    • Submit via Canvas → Assignment: Project Proposal

  3. Final Report (15 points)

    • Submit by Nov 30 (Sun) @ 11:59 PM
    • Components:
      • Poster: A0 slide (1189×841 mm) → Team-Poster-XX
      • Main Report: ≤6-page PDF (structure: Cover, Contributions, Background, Data, Methods, Results, Conclusion) → Team-MainReport-XX
      • Appendix: Source code in PDF → Team-Appendix-XX

Project Requirements:

  • Topic: Open (statistical/ML methods applied to real-world problems)

  • Data: Must use datasets from HK Government Data

  • Originality: Projects must be new and exclusive to this course.

  • Grading: Scientific value is key; plagiarism prohibited.