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.