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. Discussion of potential bias in the real-world applications and implementation of machine learning algorithms will be included.
Sub-field: ARTIFICIAL INTELLIGENCE