IERG4300/ESTR4300 Web-Scale Information Analytics / Fall 2025

Announcements

Tutorial Time: Every Tuesday, 3:30-4:15PM at ICS (Institute of Chinese Studies) L1.
Please note that Late-add students MUST attend the lectures/ tutorials from the very beginning of the term.
The due date for HW0 is strict for all. Late-add student will NOT be granted extra time extension for submission. For HW submission of late-add students, please email your submission directly to the course instructor [wclau at ie.cuhk.edu.hk] and the lead TA [lk122 at ie.cuhk.edu.hk].
Released: [Homework 0]. Due: Sat, September 13, 11:59 PM.
Website account: bigdata, password: Fall2025bigdata

Course Description

The course discusses data-intensive analytics, and automated processing of very large amount of structured and unstructured information. We focus on leveraging the MapReduce and other related paradigms to create parallel algorithms that can be scaled up to handle massive data sets such as those collected from the World Wide Web or other Internet systems and applications. We organize the course around a list of large-scale data analytic problems in practice. The required theories and methodologies for tackling each problem will be introduced. As such, the course only expects students to have solid knowledge in probability, statistics, linear algebra and computer programming skills. Topics to be covered include:

The MapReduce computational model and its system architecture and realization in practice;
Finding Frequent Item-sets and Association Rules ; Finding Similar Items in high-dimensional data;
Dimensionality Reduction techniques ; Clustering ; Recommendation systems;
Analysis of Massive Graphs and its applications on the World Wide Web;
Large-scale supervised machine learning;
Processing and mining of Data Streams and their applications on large-scale network/ online-activity monitoring.

Please check Blackboard for important announcements, assignment submissions, grades, etc.

Course Assessment

Homework/ Programming Assignments (5 sets in total): 65%
Final Exam: 35%

Student/Faculty Expectations on Teaching and Learning

http://mobitec.ie.cuhk.edu.hk/StaffStudentExpectations.pdf

Academic Honesty

You are expected to do your own work and acknowledge the use of anyone else’s words or ideas. You MUST put down in your submitted work the names of people with whom you have had discussions.

Refer to http://www.cuhk.edu.hk/policy/academichonesty for details

When scholastic dishonesty is suspected, the matter will be turned over to the University authority for action.

You MUST include the following signed statement in all of your submitted homework, project assignments and examinations. Submission without a signed statement will not be graded.

I declare that the assignment here submitted is original except for source material explicitly acknowledged, and that the same or related material has not been previously submitted for another course. I also acknowledge that I am aware of University policy and regulations on honesty in academic work, and of the disciplinary guidelines and procedures applicable to breaches of such policy and regulations, as contained in the website http://www.cuhk.edu.hk/policy/academichonesty/.

Academic Honesty Slides from Associate Dean of Faculty of Engineering

Generative AI Policy

You are NOT allowed to use any Generative AI (e.g., ChatGPT, Claude etc.) in this course, unless it is explicitly approved by the instructor in advance. Anyone who uses LLMs for completing the homework will be treated as cheating.

Previous Offerings

Fall 2024

Time and Venue

Lectures:

Wed 09:30AM - 11:15AM
LT1 @ Mong Man Wai Bldg
Fri 09:30AM - 11:15AM (Incl. 1 extra hour lecture for Postgraduate & ESTR students)
L1 @ Science Centre
Tue 09:30AM - 12:30PM, Dec 2nd (Make-up lecture)
TBD

Tutorials: