#sdsc5001 #course information

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SDSC5001Course Overview

Course Code: SDSC5001

Course Name: Statistical Machine Learning I

Semester: First Semester, 2025/26 Academic Year

Instructor: Professor Xingyu Guo

Email: xingyguo@cityu.edu.hk

Office: Room 4462, Mong Man Wai Building

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

  • Consultation Hours: Monday 16:00 – 17:00

  • Teaching Mode: Face-to-face

  • Teaching Assistants:

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

Assessment Methods

Component Weight Details
Assignments 20% 3 assignments (not graded but must be submitted). Late submissions will be penalized.
Midterm Exam 10% Week 8 (October 25). Closed-book; one A4 note sheet allowed.
Project 20% Group work (4-5 people). See above for details.
Final Exam 50% Covers all content. Closed-book; one A4 note sheet allowed.

Schedule and Teaching

Week Date Activity Content Deadline
1 September 6 Lecture Course overview, policy introduction, TA introduction
Project and exam explanation
2 September 13 Lecture Probability and statistics review
Tutorial 1 Python introduction
3 September 20 Lecture Data exploration
Basics of statistical machine learning
Tutorial 2 Hands-on data exploration (Iris dataset)
4 September 27 Lecture Statistical machine learning
Linear regression		 Assignment 1 released
Tutorial 3 Cross-validation and linear regression practice
5 October 4 Lecture Linear regression
Model selection and regularization		 Assignment 1 due
Project team formation due
Tutorial 4 Subset selection, shrinkage methods, PCR and PLS
6 October 11 Lecture Model selection and regularization
Classification methods
Tutorial 5 Classification methods practice
7 October 18 Lecture Classification methods
Midterm exam Q&A
8 October 25 Lecture Midterm exam Assignment 2 released
9 November 1 Lecture Nonlinear models
Tutorial 6 Nonlinear methods practice Project proposal due
10 November 8 Lecture Tree methods Assignment 2 due (November 10)
Tutorial 7 Tree methods practice
11 November 15 Lecture Support Vector Machines (SVM) Assignment 3 released
Tutorial 8 SVM methods practice
12 November 22 Lecture Advanced SVM
13 November 29 Lecture Course summary
Project Q&A		 Assignment 3 due
Project report due
(Graded by all TAs)

Project Requirements (20% of total score)

Key Deadlines:

  1. Team Formation (1 point)

    • Due: October 5 (Sunday) 11:59 PM
    • File name: Team-Formation-XX (XX = team name)

  2. Project Proposal (4 points)

    • Due: November 2 (Sunday) 11:59 PM
    • Format: PDF proposal ≥0.5 pages
    • File name: Team-Proposal-XX
    • Submit to: Canvas → Assignment: Project Proposal

  3. Final Report (15 points)

    • Due: November 30 (Sunday) 11:59 PM
    • Submission:
      • Poster: A0 slide (1189×841 mm) → Team-Poster-XX (Submit to: Canvas → Assignment: Poster)
      • Main Report: ≤6-page PDF (structure: cover (course name, project title, student names and IDs, date), team contribution, background, data, methods, results, conclusion) → Team-MainReport-XX (Submit to: Canvas→Assignment: Main Report)
      • Appendix: Source code PDF → Team-Appendix-XX (Submit to: Canvas→Assignment: Appendix)
        Note: 1. Each team submits only one work. 2. No source code will result in zero score for the report.

Project Rules:

  • Topic: Self-selected (must use statistical/machine learning methods to solve real-world problems)

  • Data: Must use Hong Kong Government Open Data

  • Originality: Projects must be independently designed for this course; reuse of content from other courses/papers is prohibited.

  • Grading: Scientific value is core; plagiarism is strictly prohibited.