15.097 | Spring 2012 | Graduate
Prediction: Machine Learning and Statistics

Lecture Notes

These lecture notes occasionally refer to slides, such as at the end of lectures 5 and 7. These slides are not available on MIT OpenCourseWare.

1 Rule mining and the Apriori algorithm (PDF)
2 R for machine learning (PDF) (Courtesy of Allison Chang. Used with permission.)
3 Fundamentals of learning (PDF)
4 Inference (PDF)
5 Clustering (PDF)
6 k-nearest neighbors (PDF)
7 Naïve Bayes (PDF)
8 Decision trees (PDF)
9 Logistic regression (PDF)
10 Boosting (PDF)
11 Convex optimization (PDF)
12 Support vector machines (PDF)
13 Kernels (PDF)
14 Statistical learning theory (PDF)
15 Bayesian analysis (PDF - 1.2MB) (Courtesy of Ben Letham. Used with permission.)

Course Info
As Taught In
Spring 2012
Learning Resource Types
notes Lecture Notes
group_work Projects with Examples
co_present Instructor Insights