Experimental Design for Machine Learning

This course is designed to provide students with an understanding of the principles, methods, and practice of designing and analyzing experiments (A/B testing) to explore causality. The course will cover topics such as finding reference distribution, principles of designs, factorial designs, blocking and randomization. Students will be exposed to practical applications of experimental design using real-world datasets, the course will equip them with tools they can leverage to make decisions in real-world settings.

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