ECOM7126A - Machine learning for business and e-commerce

Semester 2, 2022-23

Professor	Y. S. Paul Cheung
Teaching assistant	Ivan Law
Syllabus	This course provides the necessary fundamental concepts, theory and tools in Machine Learning (ML) to enable students to understand how Artificial Intelligence (AI) and ML can be applied in typical business applications in general, and for E-Commerce in particular.  As AI is a broad field of study, the course will focus on ML including an introduction to the fundamentals of ML, supervised and unsupervised learning, ML workflow, dataset preparation, handling and analysis, selection and training of ML models: regression, classification and clustering models; Support Victor Machines (SVM), decision trees, ensemble learning and random forests; introduction to Artificial Neural Networks (ANN) and other neural network models. The course will use ML projects and applications to demonstrate how ML can be used to solve real business problems.
Learning Outcomes	Course Learning Outcomes Relevant Programme Learning Outcome CLO1. Acquire the fundamental concepts and theory in Machine Learning ML) in the context of data analysis as applied to business decisions. PLO 1, 4 CLO2. Learn the necessary to process of ML workflow, dataset preparation, modeling and analysis tools in commonly ML models and techniques. PLO. 5, 8, 9 CLO3. Learn how to use the commonly available libraries, packages and tools for ML available, and real-life examples and applications of such tools in business cases under a variety of scenarios. PLO. 10, 11 View Programme Learning Outcomes
Pre-requisites	Students are expected to have some knowledge of Python to an extend of being able to read and make sense of Python programs, and also some basic knowledge in mathematics (college level) and statistics.  As some students may not have the necessary background, an optional free and non-credit earning course on basic Python course with introduction to basic mathematics used in this course will be offered about 4 weeks before the commencement of this course.
Compatibility	Students who have obtained credits for ECOM6022 in the academic year 2021-22 are not allowed to take ECOM7126.
Topics covered	Course Content No. of Hours Course Learning Outcomes 1. Landscape of Machine Learning – an Overview 3.0 CLO1 2. How ML is done 3.0 CLO1, CLO2 3. Classification systems 3.0 CLO1, CLO2 4. Training Models 3.0 CLO1, CLO2 5. Support Vector Machine 3.0 CLO2, CLO3 6. Decision Trees, Ensemble Learning and Random Forests 3.0 CLO2, CLO3 7. Unsupervised Learning Techniques 3.0 CLO2, CLO3 8. Artificial Neural Networks 3.0 CLO2, CLO3 9. Real Machine Learning Applications in Business 6.0 CLO3
Assessment	Description Type Weighting * Tentative Assessment Period / Examination Period ^ Course Learning Outcomes In-course assessments Continuous Assessment 100% - CLO1, CLO2, CLO3 * The weighting of coursework and examination marks is subject to approval ^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students are obliged to follow the examination schedule.  Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials	Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow, O’Reilly, 2022
Session dates	Date Time Venue Remark Session 1 17 Jan 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 2 31 Jan 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 3 7 Feb 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 4 14 Feb 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 5 28 Feb 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 6 7 Mar 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 7 14 Mar 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 8 21 Mar 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 9 28 Mar 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face Session 10 4 Apr 2023 (Tue) 7:00pm - 10:00pm MW-T3 Face-to-face MW - Meng Wah Complex
Add/drop	3 January, 2023 - 31 January, 2023
Maximum class size	60