Latest Module Specifications
Current Academic Year 2025 - 2026
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Description This module aims to review and complement foundation statistical knowledge and to establish the context for a range of methods, used in the analysis of simple and complex systems. The emphasis is on an intuitive understanding of the principles and a practical ability to apply these to data examples drawn from diverse systems. This module will cover introductory topics and conventional algorithms needed to understand the field of Machine Learning. It will 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 and Clustering. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Statistical Data Analysis: Distinguish between descriptive and inferential statistical quantities in the theory and practice of statistics and in data analytics, use a range of analytical statistical techniques and interpret outcomes. 2. Fundamentals and applications of machine learning: Different levels of measurement and data types, Purpose and objectives of Machine Learning, basic terminology and concepts, real-world machine learning applications, supervised vs. unsupervised learning. 3. Probability and Linear Algebra: Simple approaches to prediction, Linear algebra review, Probability Distributions (Gaussian), Understanding and application of underlying probability principles and distribution examples, Least Squares, Nearest Neighbours, Decision Theory, Bayesian Methods. 4. 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. 5. Unsupervised Learning: Principal techniques used in unsupervised learning including Clustering, K-means, Principal Component Analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 |
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Indicative Content and Learning Activities
Topic 1 Summary Statistics, Conditional Probability, Data Types Topic 2 Statistical Distributions Topic 3 Hypothesis Testing and Confidence Intervals Topic 4 Linear Regression and Logistic regression Topic 5 Naïve Bayes, Decision Tree Topic 6 Basics of Neural Networks and Perceptron Topic 7 Categorical Variables, Regression Problem, hyperparameters Topic 8 Introduction to feature Engineering – Clustering/ K-means clustering, Principal Component Analysis – Dimensional | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||