KarlRosaen

1. ML Study
2. Learning Log

Learning Log

Day to day reflections as I work my way through the curriculum. Find related IPython notebooks here.

• Tue, 5/17—HN coverage of my sigmoid function notebook, working on a logistic regression implementation

• Mon, 5/16—A notebook exploring why a sigmoid function is used in logistic regression and conditional probability HW

• Thu, 5/12—More probability HW, making sense of the odds ratio underlying logistic regression

• Wed, 5/11—Collaborative filtering via Matrix Factorization, more probability homework, Skikit-learn version of perceptron

• Tue, 5/10—High level understanding of bayesian non-parametric models, more stats, and back to Python Machine Learning with stochastic gradient descent.

• Mon, 5/9—Grokking probability fundamentals paying off, stats problem set progress

• Thu, 5/5—Measure theory as it relates to Cumulative distribution functions (CDFs), working on problem sets.

• Wed, 5/4—Probability through the lens of measure theory

• Tue, 5/3—Finally grokking random variables and going back to review curriculum of probability and stats.

• Mon, 5/2—Mathjax in posts and resolving confusion about random variables vs probability functions

• Fri, 4/29—Probability defined (and figuring out how to host IPython notebooks)

• Thu, 4/28—Probability intro and simulating the birthday problem

• Wed, 4/27—Statistical sampling, Designing studies

• Wed, 3/30—Feature scaling to improve performance of gradient descent.

• Tue, 3/29—Python ML book ch2 and improving on the perceptron using gradient descent.

• Thu, 3/24—Getting familiar with pandas and plotting a scatter matrix of NBA player stats.

• Wed, 3/23—Two variable exploratory data analysis (scatter plots)

• Mon, 3/21—Basic exploratory data analysis and Simpson's Paradox

• Fri, 3/18—Ch2 of Python ML book: implementing the perceptron algorithm.

• Thu, 3/17—Getting into Python Machine Learning Book

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