Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Advanced Algorithms and Datasets | |||
| Reference | CMM301 | Version | 2 |
| Created | December 2020 | SCQF Level | SCQF 11 |
| Approved | April 2019 | SCQF Points | 15 |
| Amended | March 2021 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce the students to real-time analysis of streaming data, showing how data mining techniques can be adapted to address the challenges posed by the streaming nature of some applications. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise the challenges posed by the volume, variety and velocity of modern data sources. |
| 2 | Justify the selection of data analysis approaches and data structures for application to a given problem through appraisal of established literature. |
| 3 | Construct and defend an experimental analysis of a dataset through the application of advanced data analysis algorithms. |
| 4 | Critically evaluate state-of-the-art data science algorithms and data structures and their application to complex problems in the computing industry or computing science research fields. |
| Indicative Module Content |
|---|
| Supervised and unsupervised learning. Advanced data structures. Examples of problems requiring advanced algorithms and data structures. Specific topics to be drawn from: linear and logistic regression; classification and regression trees; Naive Bayes methods, kNN, SVM, PCA, Random Forest, advanced Neural Network techniques; deep learning; gradient-based optimisation; advanced algorithms for graphs and trees; fundamentals of data stream mining. |
| Module Delivery |
|---|
| This is a lecture-based module with associated practical exercises that will involve a number of advanced data analysis algorithms and data structures. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 40 | N/A |
| Non-Contact Hours | 110 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Examination | Weighting: | 50% | Outcomes Assessed: | 1, 2 |
| Description: | A written exam. | ||||
| Component 2 | |||||
| Type: | Coursework | Weighting: | 50% | Outcomes Assessed: | 3, 4 |
| Description: | A practical coursework to select, design and implement a network management solution. | ||||
| MODULE PERFORMANCE DESCRIPTOR | ||||||||
|---|---|---|---|---|---|---|---|---|
| Explanatory Text | ||||||||
| The calculation of the overall grade for this module is based on equal weighting of C1 and C2 components. An overall minimum grade D is required to pass the module. | ||||||||
| Examination: | ||||||||
| Coursework: | A | B | C | D | E | F | NS | |
| A | A | A | B | B | C | E | ||
| B | A | B | B | C | C | E | ||
| C | B | B | C | C | D | E | ||
| D | B | C | C | D | D | E | ||
| E | C | C | D | D | E | E | ||
| F | E | E | E | E | E | F | ||
| NS | Non-submission of work by published deadline or non-attendance for examination | |||||||
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Lantz, B (2019) Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition. Packt Publishing |
| 2 | Sayed-Mouchawek, M. (2018) Learning from Data Streams in Evolving Environments: Methods and Applications. Springer- |
| 3 | Verlag. Raschka, S. (2019) Python Machine Learning. Packt |
| 4 | Leskovec, J., Rajaraman, A., and Ullman, D. (2020) Mining of Massive Datasets. Cambridge University Press. |
| 5 | Steele, Chandler, Reddy. (2016) Algorithms for Data Science. Springer. |