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
https://work.caltech.edu/homework/hw1.pdf
- Implemented Perceptron Learning Algorithm from scratch
https://work.caltech.edu/homework/hw2.pdf
- Implemented Least Squares based Linear Regression classifier from scratch
- Used nonlinear transformations for linear regression
https://work.caltech.edu/homework/hw3.pdf
- Investigated VC dimension and growth function for perceptrons and other classifiers
https://work.caltech.edu/homework/hw4.pdf
- Investigated various VC dimension bounds
- Investigated bias/variance decomposition for linear models
https://work.caltech.edu/homework/hw5.pdf
- Implemented logistic regression using gradient descent and coordinate descent
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
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
https://work.caltech.edu/homework/hw8.pdf
- Learned about SVM with soft-margins
- Learned about SVM with RBF kernel
https://work.caltech.edu/homework/final.pdf
- Investigated more about L2 regularization and linear regression with nonlinear transforms
- More SVMs. Polynomial kernels, RBF kernel.
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Implemented K Means clustering algorithm from scratch
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Used Regular Radial Basis Functions in combination with kmeans for classification, and compared the results with SVM with RBF kernel.
