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

Short Description

This course introduces students to different approaches to learning from data, with a focus on frequentist and Bayesian model-based inference.

Timetable

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.

Excluded Courses

Inference 3

Statistics 3I: Inference

Statistical Inference (Level M) 

Co-requisites

-/-

Assessment

30% Continuous Assessment

70% Final exam (can be taken at test centres)

Main Assessment In: April/May

Course Aims

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.