Objectives
This course introduces machine learning methods and their applications for data analytics. Students taking this course will learn modern machine learning techniques including classification, regression and generative models and algorithms as well as how to apply them to data analytics. The course starts with machine learning basics and some classical machine learning methods, followed by supervised and unsupervised data clustering, data dimensionality reduction for visualization and data classification. The students are expected to have solid background knowledge on calculus, linear algebra, probability and basic statistics.
Who Should Attend
Data Analyst, Data Engineer, Business Intelligence Manager, Senior Data Engineer, Data Scientist, Information Architect, Senior Data Scientist
Entry Requirements
Relevant Bachelor's Degree in electrical/electronics/communications/computer engineering, computer science., and related disciplines. Knowledge in probability, statistics, linear algebra and basic programming.
Class Schedule
Thursday (6.00 PM - 9.00 PM), Week 7-13
Lesson Delivery
Classroom facilitated training 18 hours, Assessment (Written Exam) 1.5 hours.
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Please note that the mode of delivery is subject to change in light of the COVID-19 situation.
Courses marked ‘online’ may have compulsory face-to-face sessions such as laboratory or hands-on components and details should be sought from the schools or departments before learners register for them.
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Last updated: 09 May 2025