AI & ML Training videos

Part 1: 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?

Part 2: 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.

Part 3: Performance Measures

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

Part 4: 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?

Part 5: Case Study

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

Check also our ML & AI Case Studies and other Resources: