Program Overview

CB2310 Applied Machine Learning in Molecular Biotechnology

The course provides students with the skills to apply, improve, interpret, and evaluate machine learning methods in biotechnology. The course introduces practical applications of machine learning in genomics, transcriptomics, proteomics, and biomedical research.

Course Contents

The course covers the following topics:

• Introduction to machine learning and its applications in biotechnology
• Supervised models in biotechnology I: Classification strategies
• Supervised models in biotechnology II: Regression models
• Model validation and optimization: Key metrics and strategies
• Data normalization and regularization: Limitations, challenges, and best practices
• Unsupervised models in biotechnology I: Clustering and pattern search
• Unsupervised models in biotechnology II: Dimensionality reduction
• Artificial neural networks in biotechnology: Building networks of algorithms
• Deep learning transforming biotechnology: From structure predictions to functional assays
• Society, ethics, and broader impacts of machine learning

Intended Learning Outcomes

After completion of the course, students shall have knowledge to:

• Explain key concepts of machine learning, including supervised and unsupervised learning, neural network architectures, model training and validation, feature selection, and data regularization
• Apply machine learning to a variety of biological data types to address real-world challenges in biotechnology
• Evaluate and improve model performance using appropriate metrics
• Understand concepts and strategies for optimizing model robustness and generalizability
• Identify and discuss ethical and societal implications of machine learning in life sciences
• Communicate machine learning processes and results effectively to both technical and non-technical audiences

Literature and Preparations

Specific Prerequisites

• Completed degree project 15 credits
• 20 credits in biotechnology, genomics, bio(medical) science, computer science, or biostatistics
• 6 credits in mathematics and English B/6

Literature

Information about course literature can be found in the course memo for the course offering or in the course room.

Examination and Completion

Grading Scale

A, B, C, D, E, FX, F

Examination

• TEN1 - Written exam, 4.0 credits, grading scale: A, B, C, D, E, FX, F
• LAB1 - Computational analyses, 2.0 credits, grading scale: P, F
• PRO1 - Group project, 1.5 credits, grading scale: P, F

Ethical Approach

• All members of a group are responsible for the group's work
• In any assessment, every student shall honestly disclose any help received and sources used
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution

Further Information

Offered By

CBH/Gene Technology

Main Field of Study

Biotechnology

Education Cycle

Second cycle

Credits

7.5 credits