COM6012 Scalable Machine Learning

Aims/Description: This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelisation of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice such as SPARK, programming languages such as Python/Scala and deployment on high performance computing platforms/clusters.

Staff Contact: ZHOU SHUO

Teaching Methods: Lectures, Seminars, Problem solving, Independent Study

Assessment: Formal Exam, Course work

Information on the department responsible for this unit (Computer Science):

Departmental Home Page

Teaching timetable

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Teaching methods and assessment displayed on this page are indicative for 2025-26.