Resources

• Biomedical Knowledge Discovery in Networks
  Through Language/Graphical Models and Machine Learning
  • Natural Language Processing
  • Natural Language Understanding
  • Addressing Information Overload
  • Knowledge Graphs
  • Transfer and Multitask Learning
  • Explainable (Interpretable) Models
  • In silico Modeling
  • Biomedical Applications of Machine Learning

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• Dataset Descriptions
• Geometry
• Glossary
• Graph Databases
• Graph Signal Processing
  • Graph Signal Processing: Background
• Machine Learning Notes
• Matrix / GNU Octave Basics
• "Memory" in Neural Networks
• Performance Measures (Metrics)

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To fully understand the basics of machine learning, you need to study at least a bit of linear algebra (matrix mathematics: transformations, multiplications, …), partial derivatives (needed for the chain rule, used in backpropagation, …), etc. If you are at all serious about machine learning, Andrew Ng’s Machine Learning course is a must, if only to understand the basics.

Here are my old notes from that course (2013; some of the images are missing, but those omissions are trivial as these notes are very comprehensive):

• Victoria's Machine Learning Notes
• Victoria's Cost (Loss) Function Notes

As well, here is Chris Olah’s Backpropagation blog post, with my handwritten partial differentiation calculations.