Course: Data Science 6 credits: 5
- Course code
- BFVM23DATASCNC6
- Name
- Data Science 6
- Study year
- 2023-2024
- ECTS credits
- 5
- Language
- English
- Coordinator
- F. Feenstra
- Modes of delivery
-
- Lecture
- Assessments
-
- Deep Learning - Assignment
- Unsupervised Learning - Assignment
Learning outcomes
You explain for several frequently used machine learning strategies and algorithms how they work and when they are applicable
You implement machine learning algorithms in Python for prediction and classification
You check the validity of outcomes from the methods and algorithms used
You design a (pre)processing pipeline to extract features from image data
You implement a convolutional neural network to perform image classification and image recognition
Content
This course provides an overview of the key concepts and techniques used in predictive modeling, focusing on machine learning algorithms. Students will learn a wide range of machine learning algorithms, including k-nearest neighbor, logistic regression, decision trees, support vector machines, and neural networks. The course covers optimization and evaluation techniques such as ensemble techniques, feature selection, cross-validation, over-/underfitting, regularization, learning curves, confusion matrices, and ROC curves.
In addition, the course includes image analysis techniques using deep learning by means of convolutional neural networks.
Students will gain practical experience implementing these techniques in Python.
Finally, the course concludes with a comprehensive overview of the real-world applications of artificial intelligence in the field of life sciences.
Included in programme(s)
School(s)
- Institute for Life Science & Technology