Module Code: H8MLE
Long Title Machine Learning
Title Machine Learning
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator:  
Module Author: Alex Courtney
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 Apply and evaluate the efficacy of data preparation methods
LO2 Build and evaluate advanced machine learning models in various problem domains
LO3 Extract, interpret and evaluate information and knowledge from non-trivial real-world data sets
LO4 Comprehend, analyse and evaluate key concepts in 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 3 of the BSc (Hons) in Computer Science. Learners should also have completed the Introduction to AI and ML module from stage 3 of the BSc (Hons) in Computer Science.
 

Module Content & Assessment

Indicative Content
Data Mining Methodologies and Ethics in Machine Learning
KDD & CRISP-DM. Ethics in data sourcing & handling. Regulatory & Privacy Components (including Data Protection Act). Ethical implications of machine learning
Data pre-processing and transformation (I)
Identifying and Handling Missing Values. Handling Outliers. Dimensionality Reduction (PCA, MCA, etc.)
Factors Affecting a Machine Learning Model
Bias-Variance Trade-off. Curse of Dimensionality. Understanding Factors that can affect model performance; e.g. Type III errors, selection bias, measurement errors, improper variable encoding
Regression
Revision of Simple Linear Regression. Multiple Linear Regression. Linear Model Selection and Regularization
Data pre-processing and transformation (II)
Measuring Predictor Importance. Transformations for single and multiple predictors. Feature Engineering. Understanding, Detecting and Handling (massive) class imbalance
Classification
Review of Logistic Regression and K-Nearest Neighbours. Naïve Bayes
Decision Trees
Decision Trees. Appropriate Use Cases. Regression and Classification Trees. Node Purity. Pruning
Ensembles
Random Forest. Bagging and Boosting Methods (e.g. XGBoost, AdaBoost, CART aggregation etc.)
Clustering
Notions of distance and similarity. k-means, k-medoids, hierarchical clustering. Applications of clustering. Plotting and understanding clusters. Cluster evaluation measures: DBIndex, WSSSE, scree plots
Introduction to Black Box Methods
Introduction to Support Vector Machines (SVMs). Introduction to Artificial Neural Networks (ANNs). Hyper-parameter Optimization techniques
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

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.
Assessment Type: Project % of total: 40
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
Group project focusing on the practical application of machine learning techniques to data sets. Individual components of the project may be assessed at earlier stages.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 4
Non-Marked: No
Assessment Description:
The end of semester examination will contain essay-style questions examining the theory behind machine learning techniques covered during the semester, and may require some calculation. Marks will be awarded based on clarity, structure, relevant examples, depth of topic knowledge and an understanding of the potential and limits of solutions.
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
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. Learning EnvironmentLearning will take place in a classroom/lab environment with access IT resources. Learners will have access to library resources, both physical and electronic and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the College’s virtual learning environment

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
  • Brett Lantz. (2019), Machine Learning with R - Third Edition, Packt Publishing, p.458, [ISBN: 9781788295864].
  • Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 9781461471370].
  • Christian Heumann,Michael Schomaker,Shalabh. (2017), Introduction to Statistics and Data Analysis, Springer, p.456, [ISBN: 978-3-319-46162-5].
Supplementary Book Resources
  • Kartik Hosanagar. (2019), A Human's Guide to Machine Intelligence, Penguin, p.272, [ISBN: 9780525560890].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: