Machine-Learning-Assignments

My code for the CSC311: Introduction To Machine Learning course assignments at University of Toronto

Course Background

CSC311: Introduction to Machine Learning was a supplemental course for my Certificate in Artificial Intelligence (AECERAIEN) during my B.A.Sc. at University of Toronto. This course covered the mathematical theory and implementation of the most commonly used machine learning algorithms spanning supervised and unsupervised learning.

Supervised Learning Methods

Nonparametric Models: kNNs, Decision Tree Classifiers

Parametric Models: Linear Regression, Logistic Regression, Softmax Regression, Neural Networks, Naive Bayes, Gaussian Discriminant Analysis

Unsupervised Learning Methods

Principal Component Analysis, Matrix Completion, Autoencoders, K-Means, Expectation Maximization

Assignments Overview (To Be Updated)

Assignment 2: Using kNN classifier and logistic regression methods to classify MNIST Data

• Libraries used: NumPy, Matplotlib

Citations

[1] All data for these assignments was provided by the CSC311 teaching staff with the appropriate citation listed in the 'Handout' document.

[2] Helper functions in the following files were provided by the CSC311 teaching staff:

A2: kNNs and Logistic Regression/utils.py, A2: kNNs and Logistic Regression/l2_distance.py