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Artificial Intelligence And Machine Learning
Course delivery occurs outside of a physical classroom, and generally takes place online.
Microcreds overview
The module covers what is meant by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn. The module will look at how to learn from data, using basic techniques and trialling them on data sets, and will look at algorithms to make machine learning better.
What you'll learn
- The learning problem: feasibility of learning, error, and noise
- Theory of generalization: Effective number of hypotheses, VC bound, sample and model complexity, approximation-generalization trade-off, bias and variance
- Linear classification and regression, logistic regression, gradient descent, and feature space transformations
- Overfitting and regularisation
- Validation and model selection, data snooping
- Neural Networks: Perceptron’s, Multi-Layer Perceptron’s and the Back-Propagation training algorithm.
- Optimal Margin Classifiers and Support Vector Machines.
- Parametric vs. Non-Parametric classifiers.
- On successful completion of this module students will be able to: Demonstrate an understanding of the theory of generalisation and its practical implications for machine learning algorithms, the concept of model complexity, in particular the VC bound and its practical interpretation.
- Be able to apply regularization in order to prevent overfitting.
- Demonstrate an understanding of and be able to apply non-linear transformations to feature spaces.
- Recognise and manage under- and overfitting.
- Apply methods for selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset.
- Apply methods for model validation, the process where a trained model is evaluated with a testing data set.
- Apply a number of linear and non-linear and parametric and non-parametric machine learner training models e.g., linear regression, logistic regression, feed forward neural networks and Support Vector Machines.
- Differentiate and critique various techniques that could be used and be able to justify an appropriate classification technique for a given a classification problem.
- Demonstrate an awareness of and be able to implement appropriate protocols and practices to manage bias and data snooping when training a machine learner, for a given data set.
- Demonstrate an awareness of the impact of the availability of data, for a given data set used to train the machine learner, when assessing the machine learner's performance.
Requirements
Applicants must have a minimum Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline, or a Level 8 Honours degree in other disciplines, which has a significant mathematics and computing element
Got more questions?
If you have any questions about this MicroCred, or would like to speak to someone before you start an application, please contact by email: professionaleducation@ul.ie This MicroCred can be delivered in-house, employers interested in bespoke options please contact us.