Machine Learning Course
As a junior in high school, I struggled through the basics of machine learning--I lacked the mathematical background to understand most university level resources, and there were few to no resources for high school students. That changes now. Hundreds of hours have gone into this. I am not a professor, and I do not recommend that you make this your only resource, but it serves as a great companion to Prof. Andrew Ng.'s (one of my greatest heroes) Coursera course, for those of you having difficulty. All but the CNN code is on Github. Recommended math level: single variable calculus with a willingness to learn partial differentiation. Recommended language: Matlab
Overview (IMPORTANT):
1. Primer, Intuition, Linear Regression, Gradient Descent
2. Logistic Regression (code focus)
3. Feed Forward Artificial Neural Networks
4. Convolution Neural Networks
* Follow sections 1-3 with a review of the code on Github. The best way to test your understanding, without taking a much more theoretical course, is to implement the algorithm
** Note: if you cannot open .key files, use an online file format converter
*** Note: I skip many important topics, including SVMs and clustering methods. This is a companion course.
1. Primer, Intuition, Linear Regression, Gradient Descent
2. Logistic Regression (code focus)
3. Feed Forward Artificial Neural Networks
4. Convolution Neural Networks
* Follow sections 1-3 with a review of the code on Github. The best way to test your understanding, without taking a much more theoretical course, is to implement the algorithm
** Note: if you cannot open .key files, use an online file format converter
*** Note: I skip many important topics, including SVMs and clustering methods. This is a companion course.
1. Primer, Intuition, Linear Regression, Gradient Descent
| mlcrashcoursepart1.pdf |
2. Logistic Regression (code focus)
| logregequations.pdf |
3. Feed Forward Artificial Neural Networks
| ffann_presentation.pdf |
4. Convolutional Neural Networks
| cnn_presentation.pdf |
| cnn_paper_pdf.pdf |
