STU33009 – Statistical Methods for Computer Science

Module CodeSTU33009
Module Name Statistical Methods for Computer Science
ECTS Weighting[1]5 ECTS
Semester taughtSemester 2
Module Coordinator/s  Doug Leith

Module Learning Outcomes

On successful completion of this module, students will be able to:

LO1. Describe the basic properties of random events and random variables
and calculation of probabilities
LO2. Explain Bayes theorem and its use in Bayesian inference
LO3. Develop simple probabilistic models from application descriptions
LO4. Understand confidence intervals and how to calculate them
LO5. Use linear and logistic regression and apply it to noisy data

Module Content

Topics covered in this module include:

  • Experiments, events, probability of an outcome.
  • Conditional probability and Bayes Theorem.
  • Independence.
  • Marginalisation.
  • Mean, variance, covariance
  • Law of Large Numbers, Central Limit Theorem and Normal distribution.
  • Confidence intervals and their calculation using chebyshev bounds, central limit theorem, bootstrapping
  • Maximum likelihood and MAP estimates.
  • Linear and logistic Regression

Teaching and learning Methods

Lectures, tutorials.

Assessment Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
ExaminationFinal assignmentLO1-LO560%
Class testMid-Term AssignmentLO1-LO330%68
AssignmentsWeekly AssignmentsLO1-LO410%2-104-12

Reassessment Details

Online assignment (100%)

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 33 hours
Lecture22 hours
Laboratory0 hours
Tutorial or seminar11 hours
Other0 hours
Independent study (outside scheduled contact hours), broken down by:83  hours
Preparation for classes and review of material (including preparation for examination, if applicable47 hours
completion of assessments (including examination, if applicable)36 hours
Total Hours116 hours

Recommended Reading List

A First Course In Probability, Sheldon Ross, Prentice-Hall

Module Pre-requisites

Prerequisite modules: None

Other/alternative non-module prerequisites: Basic algebra and programming (we will use Matlab in examples/labs)

Module Co-requisites

None

Module Website

Blackboard

Link to lectures on blackboard:

https://eu.bbcollab.com/guest/9c2034cc7bd141a6a4139bc42963d405