Bi9680en Artificial Intelligence in Biology, Chemistry, and Bioengineering

Faculty of Science
Autumn 2019
Extent and Intensity
2/0/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
Stanislav Mazurenko, PhD (lecturer)
Guaranteed by
prof. Mgr. Jiří Damborský, Dr.
Department of Experimental Biology – Biology Section – Faculty of Science
Contact Person: Stanislav Mazurenko, PhD
Supplier department: Department of Experimental Biology – Biology Section – Faculty of Science
Timetable
Wed 15:00–16:50 B11/333
Prerequisites
During the course all the necessary information will be provided; however, a basic knowledge of biochemistry and high school mathematics is welcome.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
(i) Introduce basic principles and concepts of Artificial Intelligence and Machine Learning; (ii) demonstrate applications of Machine Learning to tackle modern problems in biology, chemistry, and bioengineering; (iii) discuss data interpretation, major pitfalls and limitations of the methods.
Learning outcomes
After completing the course, a student will be able to:
- identify and describe main principles of ML algorithms to solve regression, classification, and clustering problems;
- describe main steps in the design of ML predictors;
- identify major challenges in developing an efficient ML predictor and explain performance measures;
- analyze modern bioengineering problems in terms of ML applicability.
Syllabus
  • 1. Modern bioengineering: data analysis perspective;
  • 2. Artificial Intelligence and Machine Learning: main types, examples, features, basic statistics;
  • 3. Unsupervised learning: clustering and principal component analysis;
  • 4. Supervised learning: main steps in the design of Machine Learning predictors;
  • 5. Classification algorithms: K-nearest neighbors, Bayesian classifiers, Support-vector machines;
  • 6. Decision trees and Random forests;
  • 7. Linear, logistic, and partial least squares regressions;
  • 8. Neural networks;
  • 9. Major challenges: overfitting and underfitting;
  • 10. Feature extraction and selection;
  • 11. Features used in Biology, Chemistry, and Bioengineering;
  • 12. Modern trends in data collection and analysis.
Literature
    recommended literature
  • WITTEN, I. H. and Eibe FRANK. Data mining : practical machine learning tools and techniques. 2nd ed. Amsterdam: Elsevier, 2005, xxxi, 525. ISBN 0120884070. info
  • MARSLAND, Stephen. Machine learning : an algorithmic perspective. Boca Raton: CRC Press, 2009, xvi, 390. ISBN 9781420067187. info
Teaching methods
Lectures, reading
Assessment methods
There will be a final exam in the form of a written test: 25 multiple-choice questions or open questions requiring short answers, for 1h. Each correct answer awards 1 point. The grading is then as follows: 10-E, 12-D, 14-C, 17-B, 21-A.
Language of instruction
English
Further Comments
Study Materials
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2019, recent)
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