This repository stores work done by Ye Huang in CSC311H5 2020 Fall.

CSC311 is the introductory machine learning course taught at UofT, where we use various python packages to implement the popular machine learning algorithm and proving their correctness.

CSC311 Course Description:

This course is a broad introduction to machine learning. It will start with basic methods of regression and classification and problems of over fitting and the evaluation of learning algorithms, and then move on to more sophisticated methods such as neural networks.

Besides reinforcing what you learn in class, the homework assignments will extend your Python skills and introduce you to the basics of scientific programming, data visualization and computational statistics, all of which are ubiquitous in machine learning.

As a fringe benefit, you will also find out what all that math you learned is actually used for!

Materials taught in CSC311:

Python (Numpy,Scipy,sklearn)

Linear/Logistic regression

K-Nearest-Neighbours(KNN)

Neural Network

K-Means

Expectation Maximization(EM)

Decision trees

Principle-Component-Analysis(PCA)

Breakdown of Assignments:

Note: the breakdown gives keywords of what an assignment was on, and the keywords follow the same order as the question given in the assignment

A1: Basic Numpy and python operations, perform linear/logistic regression on given data set, KNN on MNIST data set for classificaiton.

A2: Feature mapping, perform Gaussian Descriminant Analysis/ Gaussian Naive Bayes to solve clustering problem, prove correctness of forward/backward propogation of Neural Network, build 2 layers shallow Neural Network with basic Numpy operations.

A3: Implement PCA and use it to reduce the dimensionality of MNIST data set, find the best regularization term for Gaussian Descriminant Analysis model, use K-means to solve clustering problem on a 3D dataset, use EM to solve clustering problem on a 3D dataset