Module Code: H6DMML
Long Title Data Mining and Machine Learning
Title Data Mining and Machine Learning
Module Level: LEVEL 6
EQF Level: 5
EHEA Level: Short Cycle
Credits: 10
Module Coordinator: Arghir Moldovan
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science 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 Contrast fundamental data mining and machine learning concepts and techniques, and discuss their applicability to different problems.
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 Summarise, critique and present the results from data mining and machine learning.
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

Learners should have attained the knowledge, skills and competence gained from stage 1 of the BSc (Hons) in Data Science
 

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 and ethical implications of machine learning 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%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous 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: Continuous Assessment % of total: 40
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
This assessment will evaluate learner’s comprehension of fundamental data mining and machine learning theory and concepts, their applicability and limitations to different problems. In addition, learners may be provided with one or more datasets and will be required to apply suitable data cleaning, pre-processing and transformation operations on different attributes of the datasets.
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
Learners will be assessed through a practical project that will evaluate all learning outcomes. Learners will have to identify or and extract one or more datasets; apply data pre-processing, transformation and exploration techniques; apply suitable machine learning techniques to extract knowledge from the datasets; and report and interpret the findings.
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 reassessment strategy for the Data Mining and Machine Learning module will consist of a project that will assess all learning outcomes. Students who fail the module will be afforded an opportunity to do the repeat project over the Summer months.

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 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 24 Per Semester 2.00
Independent Learning Independent learning (hours) 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

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
  • [Website], UC Irvine Machine Learning Repository http://archive.ics.uci.edu/ml/.
  • [Website], Kaggle platform for predictive modelling competitions https://www.kaggle.com/.
  • [Website], Website: Datasets for Data Mining and Data Science http://www.kdnuggets.com/datasets/index. html.
Discussion Note: