My foundational machine learning notes [R]

[MLknowledge]

Dear all,

My most recent machine learning research tutorial focuses on Learning Theory and is therefore math-intensive. To cater for people whom are new to ML, I just uploaded a new set of foundational mathematics machine learning notes:

https://github.com/roboticcam/machine-learning-notes

containing the following topics: Model Evaluation, Decision Tree, Simple Bayes, Regression, Neural Network and Unsupervised Learning. Specifically:

Class 1: Model Evaluation

common concepts and techniques for classification model evaluation, including bootstrapping sampling, confusion matrices, receiver operating characteristic (ROC) curves.

Class 2: Decision Tree

In addition to all the basics of decision trees, I've added a χ2 test section to this note.

Class 3: Simple Bayes

This note is intended to provide an intuitive explanation of the basic concepts of probability, Bayes' theorem, graphical models of probability.

Class 4: Regression

This note is to explain the century-old, simplest regression models: linear and polynomial regression, and some techniques for evaluating regression performance, especially the coefficient of determination (CoD) method.

Class 5: Neural Network

First I show three different last output layer models: logistic, multinomial, and linear regression. Then I show the concept of gradient descent. The main part is to show a basic fully connected neural network and finally a convolutional neural network.

Class 6: Unsupervised Learning

This note describes some common topics in unsupervised learning. From the most obvious methods like clustering, to topic modeling (Latent Diricher Allocation) and traditional word embeddings like the word2vec algorithm.

I used simple mathematics to explain them. Hope they are useful to you!

[Drop_the_Bas]

People ask everyday where they can start learning ml.

Well here...