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
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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