Module Code: A8DMML
Long Title Data Mining & Machine Learning
Title Data Mining & Machine Learning
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: Simon Caton
Module Author: Madita Feldberger
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Extract, transform, explore, and clean data in preparation for data mining and machine learning
LO2 Evaluate and apply statistical methods for prediction and forecasting in various problem domains
LO3 Build and evaluate data mining and machine learning models in various problem domains
LO4 Extract, interpret and evaluate information and knowledge from data for industry contexts
LO5 Articulate and evaluate Industry-focused questions using various data artefacts and methods from statistical learning, data mining and machine learning
LO6 Summarise, critique and present the results from data mining in various problem domains
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  
 

Module Content & Assessment

Indicative Content
Intro to data mining, key tools and core methodologies
n/a
Data Understanding I
Types of data (Categorical, Functional, Numerical, Hierarchical, Time Series etc.), Structured vs. Unstructured Data, Descriptive and Inferential Statistics Revisited for Data, Understanding, Data Mining, and Machine Learning Exploratory Data Analysis
Data Understanding II
Identifying and Handling Missing Values and Outliers Feature Engineering, and Dimensionality Reduction (Principal Component Analysis and Linear Discriminant Analysis) Normalisation methods Sampling and under sampling
Univariate and multivariate regression
Using Linear and Logistic Regression for Univariate and Multivariate Predictive Analytics, Applying Regression, Auto Regression and Vector Auto Regression for Time Series now- and forecasting
Time series I: Univariate data
Using Linear and Logistic Regression for Univariate and Multivariate Predictive Analytics, Applying Regression, Auto Regression and Vector Auto Regression for Time Series now- and forecasting
Time Series II: Multivariate data
Using Linear and Logistic Regression for Univariate and Multivariate Predictive Analytics, Applying Regression, Auto Regression and Vector Auto Regression for Time Series now- and forecasting
Clustering
Evaluation measures for unsupervised methods Exclusive (e.g. k-means / k-medoids) and Fuzzy Clustering (e.g. c-means / c-mediods) using various distance measures.
Association Rule Mining
Association Rule Mining
Introduction to Classification Models
Evaluation measures for supervised methods Hold-out, k-fold cross validation, and model bootstrapping, K-nearest neighbours
Decision Trees
Decision Trees: C5.0, CART, and Random Forests
Naïve Bayes and Intro to Bayesian Classification
Naïve Bayes and principals of Bayesian Classification
Introduction to Text Mining
Text (Pre)processing and Cleaning, Sentiment Analysis, Entity Extraction
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed:  
Non-Marked: Yes
Assessment Description:
Formative assessment will be included by the provision of class based problem solving exercises and short answer questions. Feedback will be provided individually or as a group in written and 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: CA 1 (0380) % of total: 20
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
The first test will assess apprentices’ competence in data understanding and the application of regression methods to an unseen data set.
Assessment Type: CA 2 (0390) % of total: 20
Assessment Date: n/a Outcome addressed: 2,3
Non-Marked: No
Assessment Description:
The second test will assess apprentices’ knowledge, understanding and practical competence in time series analysis and forecasting as well as unsupervised machine learning.
Assessment Type: CA 3 (0420) % of total: 20
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
The third test will assess apprentices’ knowledge, understanding and practical competence in supervised machine learning.
Assessment Type: Project (0050) % of total: 40
Assessment Date: n/a Outcome addressed: 5,6
Non-Marked: No
Assessment Description:
Learners will be assessed through a team project with both practical and research elements.
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.

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

 

Module Workload

Module Target Workload Hours 0 Hours
 

Module Resources

Recommended Book Resources
  • James, G., Witten, D., Hastie, T., & Tibshirani, R.. (2013), An introduction to statistical learning, Vol. 6. New York: Springer.
  • 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.
  • Lantz, B.. (2013), Machine learning with R., Packt Publishing Ltd.
  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J.. (2016), ). Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann.
Supplementary Book Resources
  • Berthold, M., & Hand, D. J.. (2003), Intelligent data analysis: an introduction, Springer Science & Business Media.
  • Han, J., Pei, J., & Kamber, M.. (2011), Data mining: concepts and techniques, Elsevier.
  • Leskovec, J., Rajaraman, A., & Ullman, J. D.. (2014), Mining of massive datasets, Cambridge University Press.
  • Raschka, S.. Python machine learning, Packt Publishing Ltd.
This module does not have any article/paper resources
Other Resources
Discussion Note: