Module Code: H9DMML1
Long Title Data Mining and Machine Learning I
Title Data Mining and Machine Learning I
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: MICHAEL BRADFORD
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc/PhD 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 fundamental data mining and knowledge discovery methodologies in order to assess best practice guidance when applied to data mining problems in specific contexts
LO2 Extract, transform, explore, and clean data in preparation for data mining and machine learning.
LO3 Build and evaluate data mining and machine learning models on various datasets and problem domains.
LO4 Extract, interpret and evaluate information and knowledge from various datasets.
LO5 Critically review current data mining research and assess research methods applied in the field
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
Overview of Data Mining and Machine Learning
History and Evolution. Revision of data science methodologies: KDD, CRISP-DM. Data Security. Taxonomy and overview of data mining and machine learning techniques
General data pre-processing and transformation strategies
Intro to prediction. Identifying and Handling Missing Values. Looking for Outliers. Transformations for Single/Multiple Predictors. Adding/removing predictors. Binning . Feature Selection
Prediction models evaluation
Data Splitting and Sampling Methods (Holdout, Cross-fold Validation, Stratification, etc.). Model Tuning and Overfitting. Determining the best model
Regression Models
Quantitative Methods of Performance. The Variance/Bias Trade-off. Linear Regression
Regression Models
Partial Least Squares Regression. K-Nearest Neighbours Regression
Regression Models
Regression Trees. Model-based Regression Trees
Regression Models
Rule-based Models. Model Tuning via LASSO, ElastiNet, and similar. Computing Considerations
Classification Models
Logistic Regression. Linear Discriminant Analysis
Classification Models
K-Nearest Neighbours. Naïve Bayes
Classification Models
Decision Trees (e.g., C5.0, Random Forests, etc.)
Unsupervised Machine Learning
Notions of distance and similarity. Euclidian vs. non-Euclidian spaces. Clustering: k-means, k-medoids
Unsupervised Machine Learning
Clustering for outlier detection. Plotting and understanding clusters. Cluster evaluation measures: DBIndex, WSSSE, scree plots
Assessment Breakdown%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
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: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
Produce a portfolio of studies that critically compare the performance of different machine learning methods applied to at least 3 related large datasets.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The repeat strategy for this module is to repeat the project, learners may build upon previous submissions.

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) 77 Every Week 77.00
Total Weekly Contact Hours 48.00

Module Resources

Recommended Book Resources
  • Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J.. (2016), Data Mining: Practical machine learning tools and techniques (4th ed), Morgan Kaufmann.
  • Lantz, B.. (2015), Machine learning with R (2nd ed), Packt Publishing Ltd.
  • Kelleher, J. D., Mac Namee, B., & D'Arcy, A.. (2015), Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies, MIT Press.
Supplementary Book Resources
  • Mueller, A. C.. (2016), Introduction to machine learning with Python, O’Reilly.
  • Hofmann, M., & Klinkenberg, R.. (2013), RapidMiner: Data Mining Use Cases and Business Analytics Applications, CRC Press.
  • Han, J., Pei, J., & Kamber, M.. (2011), Data mining: concepts and techniques (3rd ed), Elsevier.
  • Berthold, M., & Hand, D. J.. (2003), Intelligent data analysis: an introduction, Springer Science & Business Media.
This module does not have any article/paper resources
Other Resources
Discussion Note: