Going for a little Benoit B. Mandelbrot recursion joke here with the title.
Seth Juarez (github) recently spoke to Pasadena .NET user group on the topic of Practical Machine Learning using nuML. Seth is a wonderful speaker, educator and nuML is an excellent library to get started with machine learning in .NET. His explanations are very intuitive; even for people who have been working in the field for a while. During the talk and follow up discussions, there were various technical references made which went beyond the scope of talk. To be fair with Seth, he covered lot of material in an hour and a half; probably couple of weeks worth in a traditional ML course.
Therefore I decided to provide links to these underlying topics for the benefit of attendees in case anyone is interested in knowing more about them.
- No free lunch in search and optimization
- Probably approximately correct learning
- Kernalized Sorting for NLP Presentation - Paper by Seth
- QP Solver
- NP-Complete Problems
- Intuitive Explanation of Expectation Maximization
- Multi-class classification
- REPL
- Rosylyn and Roslyn CTP Introduces Interactive Code for C#
- Expando Objects
- Cardinality vs Selectivity
- Microsoft Automatic Graph Layout Library
- Positive Definite Matrix
- Kernel Perceptron in Python
- Perceptrons and Kernels
- math.net numerics
- Matrix Slicing
- Vectors and Matrices
- CodeMash 2013 Repo and readme
- What is EM algorithm?
- k-means clustering
- Clustering Algorithms
- Bag of Words Model
- Cosine similarity vs Hamming distance
- Time series regression and generalized least squares
- Machine Learning Techniques for Stock Prediction
- Causality, Correlation and Browian Motion
Happy Machine Learning!
