Basics of Machine Learning

Basics of Machine Learning Course Notes
slides and audio from university course. Watch along on YouTube.

homepages.inf.ed.ac.uk/vlavrenk/iaml.html
Basics of Machine Learning: Naive Bayes, decision trees, zero-frequency, missing data, ID3 algorithm, information gain, overfitting, confidence intervals, nearest-neighbour method, Parzen windows, K-D trees, K-means, scree plot, gaussian mixtures, EM algorithm, dimensionality reduction, principal components, eigen-faces, agglomerative clustering, single-link vs. complete link, lance-williams algorithm

A Primer on Deep Learning | DataRobot

• a collection of statistical machine learning techniques
• used to learn feature hierarchies
• often based on artificial neural networks