Learning from Data (online) STATS5075
- Academic Session: 2018-19
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
This course introduces students to different approaches to learning from data, with a focus on frequentist and Bayesian model-based inference.
The course consists of short online lessons (each usually of at most 30 minutes length), totalling around 15-20 hours. Embedded in these lessons are formative quizzes and assessment tasks (not included in the above duration). These are flexible and can be taken (and re-taken) at any time. There also are 6-10 hours of tutorials and computer-based labs.
Requirements of Entry
The course is only available to students on the online MSc in Data Analytics.
Statistics 3I: Inference
Statistical Inference (Level M)
30% Continuous Assessment
70% Final exam (can be taken at test centres)
Main Assessment In: April/May
The aims of this course are:
■ to introduce students to different approaches to learning from data;
■ to present the fundamental principles of likelihood-based inference, with emphasis on the large sample results that are widely used in practice;
■ to introduce students to interval estimation and hypothesis testing;
■ to introduce Bayesian inference;
■ to show students how to implement these statistical methods using R.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ explain different approaches to learning from data and discuss their advantages and disadvantages in a given context;
■ define and contrast population and sample, parameter and estimate;
■ write down and justify criteria required of 'good' point estimators, and check whether or not a proposed estimator within a stated statistical model satisfies these criteria;
■ apply the principle of maximum likelihood to obtain point and interval estimates of parameters in one-parameter and multi-parameter statistical models, making appropriate use of numerical methods for optimisation;
■ justify and make use of the large sample properties of maximum-likelihood estimators to obtain confidence intervals with approximate coverage properties in a range of statistical models;
■ formulate and carry out hypothesis tests in Normal models, as well as general likelihood-based models, correctly using the terms null hypothesis, alternative hypothesis, test statistic, rejection region, significance level, power, p-value;
■ describe the rules for updating prior distributions in the presence of data, and for calculating posterior predictive distributions;
■ derive posterior distributions corresponding to simple low-dimensional statistical models, with conjugate priors;
■ implement these statistical methods using the R computer package;
■ frame statistical conclusions from interval estimates and hypothesis tests clearly.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.