Skip to content

Caltech Machine Learning course notes and homework. Implements from scratch algorithms like SVM, neural networks, backpropagation, perceptrons and other linear classifiers.

Notifications You must be signed in to change notification settings

roessland/learning-from-data

Repository files navigation

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.

About

Caltech Machine Learning course notes and homework. Implements from scratch algorithms like SVM, neural networks, backpropagation, perceptrons and other linear classifiers.

Topics

Resources

Stars

Watchers

Forks