The aim of this course is to introduce students to machine learning, which is a relatively new approach to data analytics at the intersection between statistics, computer science, and artificial intelligence. Students will be taught how to master the theory and the techniques that allow turning information into knowledge and value by “letting the data speak”.
The teaching approach will be based on graphical language and intuition more than on algebra. The course will make use of instructional as well as real-world examples and will balance theory and practical sessions with the software Stata.
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
Implement factor-importance detection
Perform signal-from-noise extraction
Evaluate correct model specification
Understand model-free classification, both from a data-mining and a causal perspective
By the end of this course, participants should have knowledge and ability to:
Engage in abstract thinking by extracting the essential features of complex systems to facilitate problem solving and decision-making.
Communicate and present complex arguments in oral and written form with clarity and succinctness.
Apply basic statistical techniques to analyse economic and financial datasets
Work effectively both individually and within a team environment.
By the end of this course, students should be able to:
Machine Learning is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.
The total number of contact hours is 15 hours. This leaves 85 hours for private study.
This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
There will be optional clinics on the last day of the course.
The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact firstname.lastname@example.org.