This document is an Octave tutorial for machine learning that covers basic operations, moving data around, computing on data, plotting data, control statements like for, while and if loops, vectorial implementation, vectorization examples comparing unvectorized and vectorized code, and gradient descent. It provides instructions and examples for using Octave for machine learning tasks.

1. Octave Tutorial
Basic operations
Machine Learning

2. Octave Tutorial
Moving data around
Machine Learning

3. Octave Tutorial
Computing on data
Machine Learning

4. Octave Tutorial
Plotting data
Machine Learning

5. Octave Tutorial
Control statements: for,
while, if statements
Machine Learning

6. Octave Tutorial
Vectorial implementation
Machine Learning

7. Vectorization example.
Unvectorized implementation
prediction = 0.0;
for j = 1:n+1,
prediction = prediction +
theta(j) * x(y)
end;
Vectorized implementation
prediction = theta’ * x;

8. Vectorization example.
Unvectorized implementation
double prediction = 0.0;
for (int j = 0; j < n; j++)
prediction += theta[j] * x[y];
Vectorized implementation
double prediction
= theta.transpose() * x;

9. Gradient descent
(for all )

10. (for all )