Caltech: Learning From Data

Online Machine Learning course from Caltech that I have done.
Course website: https://work.caltech.edu/telecourse.html

Lectures/slides: https://work.caltech.edu/lectures.html

Homework 1

https://work.caltech.edu/homework/hw1.pdf

• Implemented Perceptron Learning Algorithm from scratch

Homework 2

https://work.caltech.edu/homework/hw2.pdf

• Implemented Least Squares based Linear Regression classifier from scratch

• Used nonlinear transformations for linear regression

Homework 3

https://work.caltech.edu/homework/hw3.pdf

• Investigated VC dimension and growth function for perceptrons and other classifiers

Homework 4

https://work.caltech.edu/homework/hw4.pdf

• Investigated various VC dimension bounds
• Investigated bias/variance decomposition for linear models

Homework 5

https://work.caltech.edu/homework/hw5.pdf

• Implemented logistic regression using gradient descent and coordinate descent

Homework 6

https://work.caltech.edu/homework/hw6.pdf

• Investigated effect of L2 regression on Linear Regression
• Computed Legendre Polynomials using Gram-Schmidt orthogonalization with SymPy
• Implemented neural network forward mode
• Implemented Neural Network Backpropagation from scratch using Gradient Descent

Homework 7

https://work.caltech.edu/homework/hw7.pdf

• Investigated various train/test splitting schemes
• Looked into k-fold cross validation for model selection
• Implemented hard-margin SVM from scratch using quadratic programming

Homework 8

https://work.caltech.edu/homework/hw8.pdf

• Learned about SVM with soft-margins
• Learned about SVM with RBF kernel

Final

https://work.caltech.edu/homework/final.pdf

• Investigated more about L2 regularization and linear regression with nonlinear transforms

• More SVMs. Polynomial kernels, RBF kernel.

• Implemented K Means clustering algorithm from scratch

• Used Regular Radial Basis Functions in combination with kmeans for classification, and compared the results with SVM with RBF kernel.