Machine Learning for Intelligent Systems

Wedi cwblhau'r modiwl dylai'r myfyrwyr fedru:

1. Demonstrate competence with the machine learning methods and tools considered in this scheme.
2. Show proficiency in analysing data sets using the appropriate tools.
3. Demonstrate skills in designing, running and documenting experiments using machine learning.
4. Demonstrate capability to write and present a detailed analysis of an application of machine learning.

This module will equip students with the main concepts in Machine Learning by engaging them in seminar-based discussions on scientific papers. It will then help the students build towards a term paper, which will describe their practical investigation of the issues involved in applying two machine learning methods to an appropriate data set that they will have found.

Introduction to machine learning
- Basic concepts and assumptions
Supervised learning
- Decision Trees, Overfitting
- Naive Bayes and Bayesian Networks, Bayesian decision theory
- K Nearest Neighbours
- Linear models: Linear Regression, Logistic Regression
- Support Vector machines, kernel trick
- Ensemble methods: Bias-variance tradeoff, Boosting, Bagging, Random Forests
Unsupervised learning
- Principal Component Analysis
- Clustering: Hierarchical clustering, K-Means
- Expectation Maximisation, Gaussian Mixture Models
Neural networks and deep learning
- Multilayer Perceptrons, Stochastic Gradient Decent, Backpropagation
- Regularisation methods: L1 and L2 regularisation, Dropout, data augmentation
- Convolutional neural networks
- Recurrent Neural Networks, Long Short-Term Memory
- Autoencoders, Embeddings
- Generative Adversarial Networks
- Transfer learning
Practical methodology
- Data preprocessing, Feature extraction and selection
- Performance evaluation, Hyperparameter tuning
Other topics and in machine learning
Reinforcement Learning

Mae'r modiwl hwn yn cydymffurfio a FfCChC Lefel 7