Registry
Module Specifications
Archived Version 2016 - 2017
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Description This course will cover introductory topics and conventional algorithms needed to understand the field of Machine Learning. The course 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, Clustering and Principal Component Analysis. | |||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Understand the purpose and key applications of Machine Learning. 2. Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them. 3. Apply methods of Linear Regression to model and predict data dependencies. 4. Construct and apply Decision Tree learning to predict the values of variables of interest. 5. Build data classifiers by utilising Naive Bayes algorithms and Support Vector Machines. 6. Understand the concepts and application of Artificial Neural Networks in online learning and large data set applications. 7. Obtain insights from unstructured data by using unsupervised methods such as Clustering and Principle Component Analysis. 8. Apply selected Machine Learning methods using publicly available data sets in Python or similar. 9. Explore existing online ML frameworks, (Microsoft Azure ML, Amazon ML, Google Prediction API or other). | |||||||||||||||||||||||||||||||||||||
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 Fundamentals and Applications of Machine LearningPurpose and objectives of Machine Learning, basic terminology and concepts, real-world machine learning applications, supervised vs. unsupervised learning.Probability and Linear AlgebraSimple approaches to prediction, Linear algebra review, Probability Distributions (Gaussian), Least Squares, Nearest Neighbours, Decision Theory, Bayesian Methods.Supervised LearningMain 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 LearningPrincipal techniques used in unsupervised learning including Clustering, K-means, Principal Component Analysis. | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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