IB031 Introduction to Machine Learning

Faculty of Informatics
Spring 2020
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
doc. RNDr. Tomáš Brázdil, Ph.D. (lecturer)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Jaroslav Čechák (seminar tutor)
Guaranteed by
doc. RNDr. Tomáš Brázdil, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
Prerequisites
Recommended courses are MB102 a MB103.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
Fields of study the course is directly associated with
there are 40 fields of study the course is directly associated with, display
Course objectives
By the end of the course, students should know basic methods of machine learning and understand their basic theoretical properties, implementation details, and key practical applications. Also, students should understand the relationship among machine learning and other sub-areas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization.
Learning outcomes
By the end of the course, students
- will know basic methods of machine learning;
- will understand their basic theoretical properties, implementation details, and key practical applications;
- will understand the relationship among machine learning and other sub-areas of mathematics and computer science such as statistics, logic, artificial intelligence and optimization;
- will be able to implement and validate a simple machine learning method.
Syllabus
  • Basic machine learning: classification and regression, clustering, (un)supervised learning, simple examples
  • Decision trees: learning of decision trees and rules
  • Logic and machine learning: specialization and generalization, logical entailment
  • Evaluation: training and test sets, overfitting, cross-validation, confusion matrix, learning curve, ROC curve; sampling, normalisation
  • Probabilistic models: Bayes rule, MAP, MLE, naive Bayes; introduction to Bayes networks
  • Linear regression (classification): least squares, relationship wih MLE, regression trees
  • Kernel methods: SVM, kernel transformation, kernel trick, kernel SVM
  • Neural networks: multilayer perceptron, backpropagation, non-linear regression, bias vs variance, regularization
  • Lazy learning: nearest neighbor method; Clustering: k-means, hierarchical clustering, EM
  • Practical machine learning: Data pre-processing: attribute selection and construction, sampling. Ensemble methods. Bagging. Boosting. Tools for machine learning. Weka.
  • Advanced methods: Inductive logic programming, deep learning.
Literature
    recommended literature
  • Simon Rogers, Mark Girolami. A First Course in Machine Learning . Chapman and Hall, 2011.
  • BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003. 366 s. ISBN 8020010629. info
    not specified
  • BISHOP, Christopher M. Pattern recognition and machine learning. New York: Springer, 2006. xx, 738. ISBN 0387310738. info
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997. xv, 414. ISBN 0070428077. info
Bookmarks
https://is.muni.cz/ln/tag/FI:IB031!
Teaching methods
Lectures + practical exercises + project
Assessment methods
Intrasemestral exam, project, final exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/IB031