Machine learning basics with MATLAB

Special thanks to Prof. Sim for your excellent guidance.

This course covered several problems related to machine learning, from optimization to neural networks. I really enjoyed this course so I tried to summarize and visualize the results with animation as shown in the Matlab livelink file.

When I started to learn machine learning, I tried to jump directly into the "hot" keywords like neural network, cnn, rnn... The more I study, however, the more I realized that I had to start from the very basic definition, and the most important: MATH.

Hopefully, these documents can help someone on the way self-studying machine learning.

Cheers.

Dai Tran

Course contents

Optimization problems

Using fmin and fmincon to solve the optimization problem.

Using gradient descent

Regression

Linear/ nonlinear regression Denoising signal

Classification

SVM

k-means

monte carlo simulation

PCA

FDA

ANN

Code explanation

In Review, I summarized the basics content of this course, as well as some solutions for weekly assignments.

Some helper functions like backpropagation sigmoid or softmax should stay in the same folder of the main source code.