Course code

ULP6613-24297

old course code

Course title in Estonian

Masinõpe ja selle rakendused

Course title in English

Topics on Machine Learning and its Applications

ECTS credits

6.0

Assessment form

Examination

lecturer of 2023/2024 Spring semester

Not opened for teaching. Click the study programme link below to see the nominal division schedule.

lecturer of 2024/2025 Autumn semester

Not opened for teaching. Click the study programme link below to see the nominal division schedule.

Course aims

The course aims to provide the students an overview of machine learning models and an ability to make accurate predictions. The course introduces specific topics like reinforcement learning and machine learning on R and Python.

Brief description of the course

The course focuses on the data pre-processing. Regression: simple linear regression, multiple linear regression, polynomial regression, SVR, decision tree regression, random forest regression. Classification: logistic regression, K-NN, SVM, Kernel SVM, naive Bayes, decision tree classification, random forest classification. Clustering: K-means, hierarchical clustering. Reinforcement learning: upper confidence bound, Thompson sampling, PC7. Deep learning: artificial neural networks, convolutional neural networks.

Learning outcomes in the course

Upon completing the course the student:
- discusses the main topics and knows the main concepts related to machine learning and its applications;
- masters machine learning on R and Python;
- handles advanced techniques like dimensionality reduction;
- builds machine learning models and understands how to combine them to solve a problem.

Teacher

Daniel Filipe Sobral Fernandes

Study programmes containing that course

Artificial Intelligence for Sustainable Societies (DTSSM/24.DT)