This information is for the 2020/21 session.
Teacher responsible
Dr Joshua Loftus
Availability
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.
Pre-requisites
Students must have completed Elementary Statistical Theory (ST102).
Familiarity with statistics to the level of ST102 and familiarity with basic computer programming in R or Python.
Course content
The primary focus of this course is on the core machine learning techniques in the context of high-dimensional or large datasets (i.e. big data). The first part of the course covers elementary and important statistical methods including nearest neighbours, linear regression, logistic regression, regularisation, cross-validation, and variable selection. The second part of the course deals with more advanced machine learning methods including regression and classification trees, random forests, bagging, boosting, deep neural networks, k-means clustering and hierarchical clustering. The course will also introduce causal inference motivated by analogy between double machine learning and two-stage least squares. All the topics will be delivered using illustrative real data examples. Students will also gain hands-on experience using R or Python (programming languages and software environments for data analysis, computing and visualisation).
Teaching
This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours in Lent Term. This year, some or all 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 Lent 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.
Formative coursework
Students will be expected to produce 5 problem sets in the LT.
Indicative reading
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.
Assessment
Exam (70%, duration: 2 hours) in the summer exam period.
Project (30%) in the LT Week 11.
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).
Key facts
Department: Statistics
Total students 2019/20: Unavailable
Average class size 2019/20: Unavailable
Capped 2019/20: No
Value: Half Unit
Personal development skills
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.