Course Outline

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

Requirements

There are no specific requirements needed to attend this course.

  28 Hours
 

Testimonials

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