Bi9680en Artificial Intelligence in Biology, Chemistry, and Bioengineering

Faculty of Science
Autumn 2024
Extent and Intensity
2/0/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
prof. Mgr. Jiří Damborský, Dr. (lecturer)
Stanislav Mazurenko, PhD (lecturer)
Faraneh Haddadi (assistant)
Ing. Pavel Kohout (assistant)
Ing. Jan Velecký (assistant)
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
Thu 18:00–19:50 B11/305
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 limitations, and ethical aspects 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, Support Vector Machines, Decision Trees;
  • 6. Random Forests, Linear and Logistic regressions;
  • 7. Artificial Neural Networks;
  • 8. Overfitting and underfitting;
  • 9. ML in biotechnology and drug design;
  • 10. ML for genetics, genomics, and protein engineering;
  • 11. Self-supervised learning and generative models;
  • 12. Ethics and ML.
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, a small homework project
Assessment methods
There will be a small project worth 5 points and a final exam in the form of a written test: 25 multiple-choice or open questions requiring short answers, for 30 mins; each correct answer awards 1 point. The grading is then as follows: 17-E, 19-D, 21-C, 24-B, 27-A. In the case of official restrictions, the exam will be conducted online via MS Teams.
Language of instruction
English
Follow-Up Courses
Further Comments
Study Materials
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
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