DATASCI 3ML3 - Introduction to Neural Networks and Machine Learning

3 unit(s)

A practical introduction to neural network algorithms and their applications in machine learning using Python. Supervised learning (logistic regression, perceptrons, classification, trees, cost functions, non-linear regression, boosting) and unsupervised learning (autoencoder, principal component analysis, clustering, k-means) will be discussed. Students will conduct a series of projects to explore topics such as optimization, spam classification, image recognition, fraud detection and medical data analysis.
Three lectures; one term
Prerequisite(s): One of COMPSCI 1MD3, DATASCI 2G03, MATH 1MP3, PHYSICS 2G03; one of MATH 1B03, 1ZC3; and one of ARTSSCI 1D06 A/B, ISCI 1A24 A/B, MATH 1AA3, 1LT3, 1XX3, 1ZB3; or permission of the instructor
Antirequisite(s): PHYSICS 3G03
This course is administered by the Department of Physics and Astronomy.