Interactive Syllabus

Goal: Book on Current Trends in Machine learning

Areas

• Online machine learning
• Gradient boosting
• Concept drift
• Logistic regression
• Multi-label classification
• Belief networks
• Animations in data analytics
• Bayesian optimization
• Kernel methods
• Machine learning methods for quantile regression
• Probabilistic multirelational learning
• AutoML after AutoWeka and auto-scikit-learn
  
• (Visual data mining without animations)
  
• (Graph mining)
• (Statistics and ML, Statistical test)
• Gaussian processes
• (Quantum ML (ICML 2020 Tutorial))
• Novelty detection
• and more . . .
  

Choose/suggest an area (group by 4). Each group: Write a chapter on a state-of-the-art. Present it in the lecture time.

Project Part 2

Each member of the group: Find two algorithms/methods/tools from the area, one written in R, one in Python. Use an OpenML dataset (preferably the same for all algorithms) to test the algorithms. Do not choose a dataset with accuracy (check OpenML) bellow  60% or higher than 90%.

A final version of a template (a jupyter notebook) is already in Study materials.

Upload the second part of the project to the corresponding homework vault (q.v. link down below)

DEADLINE  (EXTENDED)   Sunday, June 6