Moon's day  Woden' s day  Frigga's day 

Jan 9 
Prolegomena. Course introduction Slides 


Jan 11 
Basic ML terminology with example Slides 


Jan 13 
Lab: Python libraries 



Jan 18 
The nature of data. From object to vectors Slides 


Jan 20 
K nearest neighbors Slides 


Jan 23 
Linear regression. Simple linear regression and ordinary least squares Slides 


Jan 25 
Lab: Linear (and related) regression techniques 


Jan 27 
Newton's method and gradient descent Slides 


Jan 30 
Continuing gradient descent 


Feb 1 
Training regression using gradient descent Slides 


Feb 3 
Logistic regression. From linear regression to classification Slides 


Feb 6 
Lab: Applying logistic regression 


Feb 8 
Training logistic regression Slides 


Feb 10 
Gaussian mixture models Probability and distributions Slides 


Feb 13 
Lab: From histograms to Gaussians 



Feb 17 
Expectationmaximization 



Feb 22 
Support vector machines. Linear programming 



Feb 27 
Lab: Support vector classification 










Mar 20 
Principal component analysis. PCA concepts Slides 


Mar 22 
Lab: PCAs and facial recognition 


Mar 24 
Eigenvectors and eigenvalues 



Mar 29 
Neural nets. The perceptron model, multilayer perceptrons 



Apr 3 
Perceptron training 


Apr 5 
The feedforward and backpropogatio algorithma 



Apr 10 
Deap learning: CNNs 


Apr 12 
Deep learning: RNNs 


Apr 14 
Lab: Deep learning 







