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• Supervised learning: classification and regression
  Machine Learning in Python: intro to the scikit-learn API
  linear and logistic regression
  support vector machine
  neural networks
  random forest
  Setting up an end-to-end supervised learning pipeline using scikit-learn
  working with data files
  imputation of missing values
  handling categorical variables
  visualizing data
  Python frameworks for for AI applications:
  TensorFlow, Theano, Caffe and Keras
  AI at scale with Apache Spark: Mlib
  Advanced neural network architectures
  convolutional neural networks for image analysis
  recurrent neural networks for time-structured data
  the long short-term memory cell
  Unsupervised learning: clustering, anomaly detection
  implementing principal component analysis with scikit-learn
  implementing autoencoders in Keras
  Practical examples of problems that AI can solve (hands-on exercises using Jupyter notebooks), e.g.
  image analysis
  forecasting complex financial series, such as stock prices,
  complex pattern recognition
  natural language processing
  recommender systems
  Understand limitations of AI methods: modes of failure, costs and common difficulties
  overfitting
  bias/variance trade-off
  biases in observational data
  neural network poisoning
  Applied Project work (optional)