[Day 124] MLx Fundamentals Day 1: Intro to ML, Naive Bayes, Factorization methods

 Hello :)
Today is Day 124!


A quick summary of today:
  • Naive Bayes practical session by Richard Willis from King's College London


Introduction to Machine learning by Volodymyr Kuleshov from Cornell Univeristy 

It was really an amazing introduction. Personally it did not cover new stuff for me, but as an intro I believe it was top. What is supervised ML, OLS, Covered Non-Linear Least Squares, Overfitting, Regularization

Naive Bayes practical session by Richard Willis from King's College London

The interesting bit was implementing a Naive Bayes classification model from scratch. 

I feel like this is one of the simplest explanations of Bayes theorem that I have seen/read so far (of prior, likelohood and posterior). Richard Willis is a Phd


Implementation
Computing the prior
Computing the likelihoods 

Computing log posterior

Complete NaiveBayesClassifier

Factorization methods by Cho-Jui Hsieh from UCLA

I went over the recording and took notes.

Covered topics:

Matrix factorization approach, Altering Least Squares, SGD, extreme multi-label classification, two-tower models, low-rankness for efficient DL, param efficient fine-tuning


I am writing this blog in the break of Day 2. I will share what Day 2 of MLx Fundamentals is about tomorrow.


That is all for today!

See you tomorrow :)

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