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
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