Module Code: H9DMML2
Long Title Data Mining and Machine Learning II
Title Data Mining and Machine Learning II
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: MICHAEL BRADFORD
Module Author: Jenette Carson
Departments: School of Computing
Specifications of the qualifications and experience required of staff


PhD/MSc degree in a computing or cognate discipline. May have industry experience also.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically analyse advanced data mining and knowledge discovery methodologies in order to assess best practice guidance when applied to complex data mining problems
LO2 Investigate and evaluate key concepts and advanced data mining techniques and assess when to apply such techniques on complex datasets and problem domains.
LO3 Contextualise, research and utilise current data mining approaches, applications and technologies in order to provide strategies to address processing of datasets with a variety of characteristics
LO4 Critically review and apply appropriate data mining research and assess research methods
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

A level 8 degree or its equivalent in any discipline
 

Module Content & Assessment

Indicative Content
General Strategies Revisited
Increasing data complexity and size with fundamental methods. . Considerations of Complexity on Computing Requirements
General Strategies Revisited
Dimensionality Reduction (PCA, MCA, etc.). Feature Engineering. Measuring Predictor Importance
General Strategies Revisited
Understanding, Detecting and Handling (massive) class imbalance.. Understanding Factors that can Affect Model Performance; e.g. Type III errors, selection bias, measurement errors, improper variable encoding. Ethically assessing biases.. .
Advanced Regression Models
Regression revision, and penalised models
Advanced Regression Models
Generalised Linear Modelling
Advanced Regression Models
Automated Linear Modelling via Bagging and Boosting
Ensembles
Ensembles:. Random Forest. Voting. Stacking.
Ensembles
Bagging and Boosting Methods (e.g. XGBoost, AdaBoost, CART aggregation etc.)
Black Box Methods
Support Vector Machines and Support Vector Regression
Black Box Methods
Neural Networks:. Classic Topologies and Activation Functions. Back Propagation. Gradient Descent and Stochastic Gradient Descent. Hyperparameter Optimisation techniques
Black Box Methods
Algorithmic Accountability, Ethical issues with black-box methods
Deep Regression Models
A brief introduction to deep learning applied to regression problems (e.g. GLMNet). Special emphasis to be played on when these methods are(n’t) appropriate (e.g. data volumes required).
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Propose and execute a research project using data mining techniques as a team of 3-4 participants.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
The examination will be a minimum of three hours in duration and may include a mix of: short answer questions, vignettes, essay based questions and case study based questions requiring the application of core module competencies. Marks will be awarded based on clarity, appropriate structure, relevant examples, depth of topic knowledge, and evidence of outside core text reading.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
The repeat strategy for this module is by repeat assessment/project that covers all learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 24 Every Week 24.00
Independent Learning Independent learning (hours) 202 Every Week 202.00
Total Weekly Contact Hours 48.00
 

Module Resources

Recommended Book Resources
  • Hastie, T., Tibshirani, R. & Friedman, J.. (2016), The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed), Springer Series in Statistics.
  • James, G., Witten, D., Hastie, T. & Tibshirani, R.. (2017), An Introduction to Statistical Learning: with Applications in R, Springer Texts in Statistics.
  • Kuhn, M. & Johnson, K.. (2013), Applied Predictive Modeling, Springer.
  • Shalev-Shwartz, S. & Ben-David, S.. (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.
Supplementary Book Resources
  • Downey, B.. (2014), Think Stats: Exploratory Data Analysis, (2nd ed).
  • Goodfellow, I., Bengio, Y., & Courville, A.. (2016), Deep Learning, The MIT Press.
  • Hearty, J.. (2016), Advanced Machine Learning with Python, Packt Publishing Ltd.
  • Leskovec, J. Rajaraman, A., & Ullman, J.. (2014), Mining of Massive Datasets, Cambridge University Press.
  • Wickham, H. & Grolemund, G.. (2017), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly.
This module does not have any article/paper resources
Other Resources
Discussion Note: