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• Supervised learning: classification and regression
  Bias-variance trade off
  Logistic regression as a classifier
  Measuring classifier performance
  Support vector machines
  Neural networks
  Random forests
  Unsupervised learning: clustering, anomaly detetction
  principal component analysis
  autoencoders
  Advanced neural network architectures
  convolutional neural networks for image analysis
  recurrent neural networks for time-structured data
  the long short-term memory cell
  Practical examples of problems that AI can solve, e.g.
  image analysis
  forecasting complex financial series, such as stock prices,
  complex pattern recognition
  natural language processing
  recommender systems
  Software platforms used for AI applications:
  TensorFlow, Theano, Caffe and Keras
  AI at scale with Apache Spark: Mlib
  Understand limitations of AI methods: modes of failure, costs and common difficulties
  overfitting
  biases in observational data
  missing data
  neural network poisoning