Seminar in Statistics and Data Analysis
Group study of current topics related to statistics and data analysis.
Spring 2012 · Prediction: Machine Learning and Statistics
Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.
Readings
- Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach . 3rd ed. Prentice Hall, 2009. ISBN: 9780136042594.
- Cristianini, Nello, and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods . Cambridge University Press, 2000. ISBN: 9780521780193.
- Gelman, Andrew, et al. Bayesian Data Analysis . 2nd ed. Chapman and Hall/CRC, 2003. ISBN: 9781584883883.
Prerequisites & course information
Prereq: Permission of instructor G (Fall) Not offered regularly; consult department Units arranged
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2012
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.