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Printable Handouts
Navigable Slide Index
- Introduction
- Motivation (1)
- Motivation (2)
- Machine learning
- Machine learning- three main decisions
- Machine learning categories
- Data representation
- Learning success
- Learning a model
- Overfitting (1)
- Overfitting (2)
- Complexity control by design
- Supervised machine learning
- Prerequisites and scenarios (1)
- Quantitative Structure-Activity Relationship (1)
- Quantitative Structure-Activity Relationship (2)
- Molecular descriptors
- A descriptor based QSAR model
- A descriptor based QSAR model- an example
- Optimal machine learning
- Data representation in supervised learning
- Vectorial data representation
- Structured data representation
- Learning objective
- Machine learning evaluation
- Machine learning evaluation- pitfalls
- Unsupervised machine learning
- Prerequisites and scenarios (2)
- Unsupervised machine learning- subcategories
- Clustering
- Distribution estimation
- Virtual screening (1)
- Virtual screening (2)
- VS performance estimation (1)
- VS performance estimation (2)
- Take home message
- Thank you
Topics Covered
- Data representation in machine learning
- Learning success models
- Overfitting
- Complexity control by design
- Supervised & Unsupervised machine learning
- Quantitative Structure-Activity Relationship (QSAR)
- Molecular descriptors
- A descriptor based QSAR model
- Choosing a learning objective
- Machine learning evaluation
- Clustering
- Distribution estimation
- Virtual screening (VS)
- VS performance estimation
Talk Citation
Fechner, N. (2011, May 31). Introduction to machine learning and its application in chemistry [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 21, 2024, from https://doi.org/10.69645/CMMQ8229.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Nikolas Fechner has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.