Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Repeat examination |
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Description The principal purpose of the module is to provide students with knowledge in areas of mathematics that are required for Machine Learning. The knowledge is also required for many other engineering applications such as advanced circuit theory, quantum electronics and communication systems. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Perform vector and matrix operations and apply to problems of data representation and transformation. 2. Apply the basic principles of probability theory, including the concepts of random variables, probability distributions, and conditional probability to model and analyze uncertainty in data. 3. Apply linear and non-linear regression techniques to model relationships between variables in data, evaluate model performance using appropriate metrics, and use these models to make predictions 4. Identify appropriate statistical analysis techniques and apply them to assess the quality of data models. 5. Compute gradients, partial derivatives, and directional derivatives and apply multivariate calculus to optimization problems. | |||||||||||||||||||||||||||||||||||||||||||
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
IntroductionWhat is machine learning? Mathematical underpinnings of machine learning.Review of Linear AlgebraVectors, matrices, inner and outer products, eigenvalues and eigenvectors, matrix inversionProbabilityBasic definition, probability density function, cumulative distribution function, common distributions, Normal distribution, Bayes’ Theorem, random variables, expectation, variance, moments, operations on random variablesStatisticsParameter estimation, confidence intervals, hypothesis testing.Review of Multivariate CalculusMultivariate chain rule, Taylor series, gradients, partial derivatives.RegressionLinear, non-linear and normal regression. Performance evaluation of regression models.OptimizationWhat is optimization? Gradient descent as an optimization technique. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||