Question 1

How do I run the demos on Windows with Octave?

Accepted Answer

Install Octave from the official website, open a terminal in the algorithm folder (e.g., neural-network), launch Octave, and type 'demo' to execute the script. Charts will open in separate windows as shown in the README screenshots.

Question 2

Can I use this code for my academic project or homework?

Accepted Answer

Yes, it's ideal for learning and academic purposes to grasp ML mathematics. However, ensure you understand and modify the code appropriately, and cite the repository to avoid plagiarism in graded assignments.

Question 3

Is this better than the Python version for learning ML basics?

Accepted Answer

The MatLab/Octave version emphasizes mathematical rigor and manual implementation, which can deepen understanding. The Python version (homemade-machine-learning) might be more accessible due to Python's popularity, but both serve similar educational goals.

Question 4

How to extend the neural network for my own dataset?

Accepted Answer

Study the MLP implementation in the neural-network folder; modify the input data loading, adjust layer sizes, and retrain using the provided backpropagation code. Refer to the mathematical explanations to ensure correct parameter tuning.

Question 5

Does this cover anomaly detection or just clustering?

Accepted Answer

It includes both anomaly detection using Gaussian distribution and k-means clustering, with separate demos and explanations for each unsupervised learning technique, as outlined in the README.

Question 6

What math background do I need to understand this repository?

Accepted Answer

Basic knowledge of linear algebra, calculus, and probability is recommended, as the explanations assume familiarity with concepts like matrices, derivatives, and distributions, often tied to course materials from Andrew Ng.

Question 7

How does this compare to using Octave's built-in ML functions?

Accepted Answer

This repository implements algorithms from scratch to teach the underlying math, while Octave's built-in functions are optimized black boxes. Use this for learning internals, and built-in functions for quicker, less educational applications.

Machine Learning in MatLab/Octave

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