Module Code: H8MACL
Long Title Machine Learning
Title Machine Learning
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Sophie Flanagan
Module Author: ORLA LAHART
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Lecturer must have MSc or PhD degree in computer science or cognate discipline. Experience in lecturing machine learning and coding in Python. May also have industry experience. Lab Assistants are required for tutorials and they should have experience in Python coding and reasonable knowledge of machine learning techniques.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Comprehend, compare and contrast fundamental machine learning concepts and techniques
LO2 Comprehend and assess potential ethical implications of machine learning.
LO3 Extract, transform, explore and clean data in preparation for machine learning
LO4 Build and evaluate machine learning models on various problem domains
LO5 Summarise, critique and present results from machine learning for decision-making
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
Introduction and Ethics in Machine Learning
Forms of learning (Supervised, Unsupervised, Reinforcement) Ethics in data sourcing & handling Review of regulatory & privacy components (including the Data Protection Act) Ethical implications of Machine Learning Methodologies (e.g., KDD, SEMMA & CRISP-DM) Review of basic data exploration statistics
Data Preprocessing
Data cleaning (i.e., handling missing values, outliers, noise data) Data integration (i.e., entity integration problem, and handling of redundant, correlated, duplicated, and conflicting data) Data transformation (i.e., normalization, binning, log transformation, scaling) Data reduction (i.e., dimensionality reduction like PCA and MCA, attribute subset selection, sampling)
Review of linear and multiple linear regression Assessing the model’s accuracy Model selection (i.e., AIC and BIC) Measuring predictors’ importance Subset selection Shrinkage methods
Introduction to classification Review of logistic regression Review of k-nearest neighbours Classification performance measures (e.g., Confusion matrix, precision and recall, ROC curve)
Model Evaluation and Selection
Bias-Variance trade-off Curse of dimensionality Evaluation methods (i.e., split validation, cross-validation, and bootstrap methods) Understanding, detecting and handling (massive) class imbalance
Unsupervised Learning
Introduction to unsupervised learning Notions of distance and similarity Partitioning methods (e.g., k-Means, k-Medoids) Plotting and understanding clusters Cluster evaluation metrics (i.e., DBIndex, silhouette coefficient)
Tree-Based Models
Decision Trees Regression and classification trees Node purity Pruning
Ensemble Models
Bagging Random Forest Boosting Stacking
Naïve Bayes Classification
Introduction to Naïve Bayes Bayes theorem Maximum a posteriori hypothesis Class conditional independence Naïve Bayes classifier
Introduction to Artificial Neural Networks
Feedforward neural network architecture Sigmoid activation function Backpropagation
Introduction to Deep Learning
Introduction to deep learning Deep feedforward networks Recurrent and recursive neural network Evaluation of deep learning
Text Analysis
Text tokenization Text normalization Feature extraction (e.g., Bag of words model, TF-IDF model) Sentiment analysis
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: Assignment % of total: 25
Assessment Date: Week 4 Outcome addressed: 2,3
Non-Marked: No
Assessment Description:
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. In addition, learners will be required to identify and discuss ethical implications of handling and applying machine learning to these datasets.
Assessment Type: Project % of total: 75
Assessment Date: Week 12 Outcome addressed: 1,3,4,5
Non-Marked: No
Assessment Description:
This assessment will evaluate learner’s comprehension of fundamental machine learning theory and concepts, their applicability and limitations to different problems. Learners will have to (1) identify a topic of interest and one relevant research or business question in that topic; (3) select at least two datasets useful to answer the question; (3) apply data pre-processing and transformation techniques to prepare the datasets for machine learning analysis; (4) perform exploratory analysis in these datasets; (5) apply, evaluate and optimize suitable machine learning techniques to extract knowledge from the selected datasets useful for a decision-making process in the topic of choice; (6) report and interpret the findings to answer the question of interest, and (7) elaborate a video presentation highlighting the project’s main objectives, methodology, main findings, challenges faced.
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
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
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture On-line/Classroom activities 24 Per Semester 2.00
Tutorial Practical & Tutorial activities 36 Per Semester 3.00
Independent Learning Independent Learning activities 190 Per Semester 15.83
Total Weekly Contact Hours 5.00

Module Resources

Recommended Book Resources
  • Ethem Alpaydin. (2020), Introduction to Machine Learning, 4th ed.. MIT Press, Cambridge, MA, p.712, [ISBN: 978-0262043793].
  • Shai Shalev-Shwartz, Shai Ben-David. (2015), Understanding Machine Learning, 2nd. Cambridge University Press, New York, NY, p.397, [ISBN: 978-1107512825].
  • Sebastian Raschka, Vahid Mirjalili. (2019), Python Machine Learning, Packt Publishing, Birmingham, p.770, [ISBN: 978-1789955750].
Supplementary Book Resources
  • Kartik Hosanagar. (2019), A Human's Guide to Machine Intelligence, Penguin, London, p.272, [ISBN: 9780525560890].
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman. (2017), The Elements of Statistical Learning, 2nd ed.. Springer, New York, NY, p.767, [ISBN: 978-0387848570].
  • John D. Kelleher, Brian Mac Namee, Aoife D'Arcy. (2020), Fundamentals of Machine Learning for Predictive Data Analytics, 2nd ed.. MIT Press, Cambridge, MA, p.856, [ISBN: 9780262044691].
  • Wes McKinney. (2017), Python for Data Analysis, O'Reilly Media, Sebastopol, CA, p.550, [ISBN: 978-1491957660].
  • Aurélien Géron. (2019), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly, Sebastopol, CA, p.819, [ISBN: 978-1492032649].
  • CHARU C. AGGARWAL. (2019), Neural Networks and Deep Learning, Springer, p.524, [ISBN: 978-3030068561].
  • Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, Cambridge, MA, p.775, [ISBN: 978-0262035613].
  • Dipanjan Sarkar. (2016), Text Analytics with Python, Apress, Bangalore, p.385, [ISBN: 978-1484223871].
  • Benjamin Bengfort, Tony Ojeda, Rebecca Bilbro. (2018), Applied Text Analysis with Python, O'Reilly Media, Sebastopol, CA, p.310, [ISBN: 978-1491963043].
  • Valentina E. Balas, Sanjiban S. Roy, Dharmendra Sharma, Pijush Samui. (2019), Handbook of Deep Learning Applications, Springer, Cham, p.383, [ISBN: 978-3-030-11478-7].
Recommended Article/Paper Resources
Supplementary Article/Paper Resources
Other Resources
Discussion Note: