COMP 347 Machine Learning
Many recent advances in artificial intelligence are not in symbolic systems, but in statistical machine learning and data science. This course offers a balance between the theory and practice of data science, from collecting and understanding the data, to developing supervised and unsupervised models (such as linear regression, support vector machines, K-means and Gaussian mixture clustering, principle component analysis, neural networks), and finally interpreting the predictions and their limitations. Computer Science sub-field: Artificial Intelligence.
Prerequisite
COMP 229 or permission of instructor