This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students. This course cannot be taken with ST309 Elementary Data Analytics.
This course is available on the BSc in Actuarial Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students. This course cannot be taken with ST309 Elementary Data Analytics.
15 hours of lectures, 20 hours of seminars and 5 hours of help sessions in the MT. This course will be delivered through a combination of classes, lectures & Q&A sessions totalling a minimum of 30 hours in Michaelmas Term. This year, some of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Michaelmas Term Students are required to install R/Python in their own laptops.? Student not having a laptop of their own, will be offered to use personal computers available in seminar rooms. Week 6 will be used as a reading week.
Students will be expected to produce 5 problem sets in the MT.
James, G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Springer, 2017. Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edition, Springer,? 2009.? Efron, B. and Hastie, T. Computer Age Statistical Inference. Cambridge University Press, 2016. Wickham, H, and Grolemund, G. (2017). R for Data Science. O'Reilly.
Exam (70%, duration: 2 hours) in the summer exam period. Project (30%) in the LT Week 3. Students are required to submit a group project by applying machine leanring methods covered in this course on some real data examples using R/Python (which accounts for 30% of the final assessment). In addition to some real data examples, the focus of this course is to introduce some theoretical and methodological concepts in machine learning. These components will be tested by a written exam (which accounts for 70% of the final assessment).