Course
Uczenie maszynowe w przetwarzaniu danych molekularnych (5 rok Biotechnologii)Overview
This course covers many topics in machine learning including with a particular focus on applications in the molecular biology domain. The covered machine learning techniques include supervised leaning (classification, regression, and time series), unsupervised learning, dimensionality reduction, and reinforcement learning. The emphasis is on the practical aspects of applying these techniques in molecular biology. However, the theory behind each learning algorithm will be discussed. Students will learn how to design and develop predictive models. Students will utilize scikit-learn and Keras, which are libraries for machine learning in Python.
Teaching method
- Labs (30 h) [godz. 8:00-10:15]
Teacher
- dr. Hani Z. Girgis
- [email protected]
Labs room K32
| # | Date | Topic | Lab | Materials |
|---|---|---|---|---|
| 01 | 15.11.2024 | Decision trees | - | - |
| 02 | 22.11.2024 | Ensemble learning and random forest | - | |
| 03 | 29.11.2024 | Linear regression | - | - |
| 04 | 06.12.2024 | Support Vector Machines | - | - |
| 05 | 13.12.2024 | Hidden Markov Models | - | - |
| 06 | 20.12.2024 | Dimensionality Reduction | - | - |
| 07 | 10.01.2024 | Unsupervised Learning | - | - |
| 08 | 17.01.2025 | Artificial neural networks | - | - |
| 09 | 24.01.2025 | Deep convolutional and recurrent networks | - | - |
| 10 | 31.01.2025 | Reinforcement learnin | - | - |