Module Code | STU33009 |
Module Name | Statistical Methods for Computer Science |
ECTS Weighting[1] | 5 ECTS |
Semester taught | Semester 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 Component | Brief Description | Learning Outcomes Addressed | % of total | Week set | Week Due |
Examination | Final assignment | LO1-LO5 | 60% | ||
Class test | Mid-Term Assignment | LO1-LO3 | 30% | 6 | 8 |
Assignments | Weekly Assignments | LO1-LO4 | 10% | 2-10 | 4-12 |
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 |
Lecture | 22 hours |
Laboratory | 0 hours |
Tutorial or seminar | 11 hours |
Other | 0 hours |
Independent study (outside scheduled contact hours), broken down by: | 83 hours |
Preparation for classes and review of material (including preparation for examination, if applicable | 47 hours |
completion of assessments (including examination, if applicable) | 36 hours |
Total Hours | 116 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
Link to lectures on blackboard:
https://eu.bbcollab.com/guest/9c2034cc7bd141a6a4139bc42963d405