DS4400 (Machine Learning) course reviews and ratings at Northeastern (NEU). Average rating 3.3/5. Compare professors with RateMyProfessor reviews.
Introduces supervised and unsupervised predictions, modeling, and essential machine learning concepts. Uses tools and libraries to analyze datasets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms such as regression; classification (including support vector machines and logistic regression); dimensionality reduction (including principal-component analysis); clustering, gradient descent, regularization techniques, multiclass data, and algorithms; boosting; and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Applies concepts to common problem domains.
Data updated October 2026.