Course Outline

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)


There are no specific requirements needed to attend this course.

  28 Hours


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