| Module Code: |
H8MLF |
|
Long Title
|
Machine Learning Fundamentals
|
|
Title
|
Machine Learning Fundamentals
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Author: |
Isabel O'Connor |
| 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 |
Recognize the ethical implications of machine learning |
| LO2 |
Apply appropriate data sourcing and handling principles |
| LO3 |
Build and evaluate advanced machine learning models in various problem domains |
| LO4 |
Extract, interpret and evaluate information and knowledge from non-trivial real-world data sets |
| LO5 |
Comprehend, analyze 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 |
See section 4.2 Entry procedures and criteria for the programme including procedures recognition of prior learning.
|
Module Content & Assessment
| Indicative Content |
|
Data Mining Methodologies and Ethics in Machine Learning
KDD, CRISP-DM, SEMMA. 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 (I)
Bias-Variance Trade-off. Curse of Dimensionality
|
|
Factors Affecting a Machine Learning Model (II)
Understanding Factors that can affect model performance; e.g. Type III errors, selection bias, measurement errors, improper variable encoding. Ethically assessing biases
|
|
Regression (I)
What is a Regression Problem?. Simple Linear Regression
|
|
Regression (II)
Multiple Linear Regression. Linear Model Selection and Regularization
|
|
Data pre-processing and transformation (II)
Measuring Predictor Importance. Feature Engineering. Understanding, Detecting and Handling (massive) class imbalance
|
|
Classification (I)
What is a Classification Problem?. Logistic Regression
|
|
Classification (II)
K-Nearest Neighbours (kNN). Naïve Bayes (NB)
|
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Decision Trees
Decision Trees. Appropriate Use Cases. Measuring Node Purity. Pruning
|
|
Ensembles
Random Forest. Bagging and Boosting Methods (e.g. XGBoost)
|
|
Clustering
Notions of distance and similarity. Clustering methods: k-means, k-medoids, hierarchical. Cluster evaluation measures: DBIndex, WSSSE, scree plots
|
| Assessment Breakdown | % |
|---|
| Coursework | 60.00% |
| End of Module Assessment | 40.00% |
AssessmentsFull Time
| Coursework |
| 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. |
|
| Assessment Type: |
Project |
% of total: |
60 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
Project focusing on the practical application of data processing and machine learning techniques to data sets in order to extract insights and perform predictive analytics. Component parts of this project may be assessed at different dates. |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
40 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
5 |
| 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. |
|
| 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.
|
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) |
77 |
Per Semester |
6.42 |
| Total Weekly Contact Hours |
4.00 |
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].
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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 |
|---|
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