machine_learning

? lecture

• Discriminitive vs generative models
• Introduce popular datasets we will work with: Fisher irises, Old Faithful, NIST, faces
• Add more algorithmic details: computational complexity, etc.
• Make a table where minimization functions for all of the topics are summarized.
• Add conclusions at the end of EVERY lecture.
• Make demo style consistent (consider using SNS).

Introductory lecture

• Example with non-balanced dataset (terrorist/non-terrorist) is ambiguous, redo

Python

• Too much attention to simple types, need more objects/classes

Numpy lecture

• add np.einsum consideration
• think on some visualization for np.arrays
• error in image with slicing
• add list/array memory representation image
• fix cycle/ufunction time comparison (add comparison with list)

Statistics lecture

• add Polya urn models (Beta distribution etc)

Bayes lecture

• Problem with 2 boxes, bead, and pearl. 2 boxes, one contains 1 bead and the other 1 pearl. We don’t know where pearl is (50/50). One more pearl is aced into box “B”, shuffled, randomply pulled back. Pearl is pulled back. What is the probability to have pearl in “A”? in “B”?

Regression lecture

• Add Logistic regression
• Mention orthogonal distance regression (scipy.odr)
• Regression (to mean) visualization
• Workbook, problem with error bars, add slider to change order of the fitting polynomial
• Workbook, add problem to demonstrate LAD robustness (compared to OLS)
• Set feaures on click in demo

Optimization lecture

• Coordinate descent (has connections to SVM and E-M algo)
• Use UI from GMM demo to set the initial point
• Use maximum descent algo to get 2D maximum in a matrix of integers as is done in https://www.youtube.com/watch?v=HtSuA80QTyo&t=37s

All lectures with demos

• Pull out not only fitting but classification function as well (from visualization).

PCA

• Add visualization as in http://setosa.io/ev/principal-component-analysis/
• Use idea with finding dissimilar image from https://dou.ua/lenta/articles/computer-vision-magic/

Manifold learning

• Visualize surface (do Delaune triangulation and draw as a bunch of triangles)