AI & ML Training

In this series of 5 videos you will learn about the following aspects

General introduction to AI & ML

  • What are AI & ML and what can they do?
  • What are the applications of AI & ML?
  • What are the 3 main types of AI & ML learning and how to design a learning system?
  • What are the important points regarding Training vs Test Distribution?
  • What are the different function representations and search/optimization algorithms?
  • What are the different metrics used to control the quality of the predictions?

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Data Wrangling

  • Why is data pre-processing necessary in AI/ML?
  • What data pre-processing steps need to be taken before building a model? - Data cleaning, data integration and transformation, data reduction, discretization and concept hierarchy generation.

Performance Measures

  • Accuracy
  • Weighted (Cost-Sensitive) Accuracy
  • Lift
  • Precision/Recall (F-Measure, Breaking Even Point)
  • Receiver Operating Characteristic (ROC) Curve
  • (ROC Area)

Neural Networks

  • What is the inspiration to build neural networks?
  • What are the different functions of an artificial neuron?
  • What are the different types, topologies and properties of neural networks?
  • What is the typical ML algorithm?
  • What are the Encoder/Decoder, as well as Transformer?
  • What are the applications of neural networks?

Case Study

  • Discussing how AI & ML can be used in Protein Engineering on the example of Complementary Factor I (CFI) protein