Welcome to CSCI1420!
How can artificial systems learn from examples, and discover information buried in massive datasets? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include empirical risk minimization, probably approximately correct learning, maximum likelihood parameter estimation, kernel methods, neural networks, the expectation maximization algorithm, and principal component analysis.
- Fill out the Collaboration Policy Form. You will not be able to hand in assignments without doing so.
- Set up your iClicker/REEF account before Tuesday's class. Instructions available here .
- Sign up for Piazza. Please use Piazza for all questions about homeworks and course material.
- The Canvas page for this course can be found here.
Machine Learning in the News
- M.I.T. Plans College for Artificial Intelligence, Backed by $1 Billion
- A.I. Is Helping Scientists Predict When and Where the Next Big Earthquake Will Be
- A.I. Art at Christie's Sells for $432,500
Being a student can be very stressful. If you feel you are under too much pressure or there are psychological issues that are keepingyou from performing well at Brown, we encourage you to contact Brown's Counseling and Psychological Services (CAPS.). They provide confidential counseling and can provide notes supporting accommodations for health reasons.