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Course Outline

Supervised learning: classification and regression

  • Machine Learning in Python: introduction to the scikit-learn API
    • linear and logistic regression
    • support vector machines
    • neural networks
    • random forest algorithms
  • Establishing an end-to-end supervised learning pipeline using scikit-learn for government applications
    • managing data files
    • imputing missing values
    • processing categorical variables
    • data visualization techniques

Python frameworks for AI applications:

  • TensorFlow, Theano, Caffe, and Keras
  • Scalable AI implementation with Apache Spark MLlib

Advanced neural network architectures

  • convolutional neural networks for image analysis
  • recurrent neural networks for time-series data
  • long short-term memory (LSTM) cells

Unsupervised learning: clustering and 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

Understanding 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)

Requirements

This course has no prerequisites for government participation.

 28 Hours

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