All students must validate 30 ECTS per semester.We offer two speciality tracks:

Track 1: Vision and Applications
The first track focuses on various applications of computer vision: biomedical applications, people detection, object tracking, computational photography.

Track 2: Vision and devices
The second track focuses on devices to capture images (intelligent sensors, medical imaging systems) and to visualise and interact with them (augmented reality)

 

 

 

Course name: Data Mining and Machine Learning Credits: 5

  • Class type: lecture/practical/lab
  • Hours per week: 3/1/1
  • Type of the exam: oral exam
  • Prerequisites (if exist): Database Systems II.

Content

Introduction of terms (concept, sample, attributes and its types). Data preparation – handling of missing and noise data. Knowledge representation formats – decision tables and trees, classification and association rules, clusters. Simple algorithms – 1R, Naive Bayes, covering algorithms (ID3, Prism), mining association rules, linear models, instance based learning (NN method). Evaluation methods – training and test data, cross-validation, leave-out-one, bootstrap, counting the cost, evaluating numeric predictions, MDL principle. Complex algorithms – C4.5, support vector, model tree, generalization of clusters. Attribute selection, data cleansing, combining multiple models. Bioinformatical applications.

Required reading

Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations, by Ian Witten and Eibe Frank, 2000, Morgan Kaufmann Publishers.

Recommended reading

  • Lecturer (name, position, degree): Dr. Gergely Luk√°cs, associate professor, PhD

The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken abroad by mobile students through the transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.