Data Mining and Machine Learning

Course	T-809-DATA
Semester	20243
ETCS	8
Core	No

Year	1. year
Semester	Fall 2024
Level of course	4. Second cycle, introductory
Type of course	Elective
Prerequisites	No prerequisites.
Schedule	No schedule found.
Lecturer	Jón Guðnason
Content
Pattern recognition system, classifier design cycle and learning. Statistical pattern recognition, Bayesian decision theory, maximum likelihood and Bayesian parameter estimation. Linear models for classification. Principal component analysis. Multilayer neural networks. Nonparametric methods: k-nearest neighbours and Parzen kernels. Kernel methods and support vector machines. Unsupervised classification, K-means clustering, Gaussian mixture models and expectation maximization. Combination of classifiers, bagging and boosting.
Learning outcome - Objectives
After the course the students should be able to recall, describe and define, the following terms:Pattern recognition system, classifier design cycle and learning. Statistical pattern recognition, Bayesian decision theory, maximum likelihood and Bayesian parameter estimation. Linear models for classification. Principal component analysis. Multilayer neural networks. Nonparametric methods: k-nearest neighbours and Parzen kernels. Kernel methods and support vector machines. Unsupervised classification, K-means clustering, Gaussian mixture models and expectation maximization. Combination of classifiers, bagging and boosting.After the course the students should be able to apply the data mining methods and implement the machine learning algorithms presented in the course using standard programming languages such as Python or Matlab and software packages such as scikit-learn andWeka.After the course the students should be able to design a suitable machine learning algorithm for a real world problem, evaluate its performance, compare different designs and implementations and interpret the results. The students should also be able to present findings and new results in the subject.
Course assessment
Reading material
No reading material found.
Teaching and learning activities
Language of instruction	English