Module: Introduction to Machine Learning

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Introduction to Machine Learning
Module Code	CA6071 (ITS) / CSC1128 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Marija Bezbradica
Module Teachers	-
NFQ level	9	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

This course will cover introductory topics and conventional algorithms needed to understand the field of Machine Learning. The course will cover foundation statistical knowledge in order to prepare students to understand and distinguish between main groups of methods used in Machine Learning, supervised and unsupervised, and how and when they are applicable. Key topics will include Regression, Decision Trees, Naive Bayes, Neural Networks, Clustering and Principal Component Analysis.

Learning Outcomes

1. Apply knowledge about the purpose and key applications of Machine Learning in choosing appropriate Machine Learning methods to given application tasks.
2. Distinguish between descriptive and inferential statistics including different levels of measurement and data types.
3. Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them.
4. Implement machine learning model for i) Linear Regression to model predict data dependencies ii) Decision Tree learning to predict the values of variables of interest and iii) classification utilizing Naive Bayes algorithms and Support Vector Machines.
5. Apply knowledge of the concepts and application of different types of Artificial Neural Networks in selecting appropriate neural-network based systems to given application contexts.
6. Analyse insights from unstructured data by using unsupervised methods such as Clustering and Principal Component Analysis.
7. Evaluate selected Machine Learning methods using publicly available data sets in Python or similar.
8. Experiment with existing ML libraries, tools and platforms such as Scikit-Learn and HuggingFace.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	Lectures and in-class tutorials covering key topics of the course. Full class notes are provided in advance of lectures and material is divided by lecture and topics covered. Course content and supplementary material on key topics are available online.
Laboratory	24	Laboratory hands-on experience running machine learning algorithms on existing data sets both locally and using online frameworks.
Independent Study	127.5	No Description
Total Workload: 187.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Fundamentals and Applications of Machine Learning
Purpose and objectives of Machine Learning, basic terminology and concepts, real-world machine learning applications, supervised vs. unsupervised learning.

Probability and Linear Algebra
Simple approaches to prediction, Linear algebra review, Probability Distributions (Gaussian), Least Squares, Nearest Neighbours, Decision Theory, Bayesian Methods.

Supervised Learning
Main methods used in supervised learning including Regression, Decision Trees, Naive Bayes, Support Vector Machines, validation and model evaluation, Basics of Neural Networks, Ensembles, Perceptron.

Unsupervised Learning
Principal techniques used in unsupervised learning including Clustering, K-means, Principal Component Analysis.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Laboratory Portfolio	Four supervised lab submissions assessing the material covered in the previous two weeks each comprising some independent coding work and some commentary. .	40%	As required
Project	Project (written report) on an application area of machine learning including examples of how technologies work, and discussion of ethical aspects; each student must choose a different topic.	30%	Once per semester
Loop Exam	End-of-semester supervised lab exam exam covering all module topics.	30%	Sem 1 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

John D. Kelleher, Brian Mac Namee and Aoife D'Arcy: 2015, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Example, and Case Studies, 9780262029445
Trevor Hastie, Robert Tibshirani, Jerome Friedman: 2009, 2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2, 0387848576
Tom M. Mitchell: 1997, Machine learning, McGraw-Hill, New York, 9780070428072
Richard O. Duda, Peter E. Hart, David G. Stork: 0, Pattern Classification, 2, 0471056690

Other Resources

None