Lecture Notes · Lecture Notes
MIT OpenCourseWare
Prediction: Machine Learning and Statistics
Art: Gaston, John, (John Gaston merchant ledger) - DPLA -…, 1773 · Public domain
- Instructor
- Prof. Cynthia Rudin
- Term
- Spring 2012
- Level
- Graduate
- What's inside
- 1 lecture note · 38 other files · 5 web pages
- Materials open
- MIT OCW
About this course
Source: MIT OpenCourseWare 15.097 Prediction: Machine Learning and Statistics, Spring 2012, Prof. Cynthia Rudin — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.
Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.
Before you start
Prerequisites are not listed here. Check MIT's own syllabus for this course.
How this course was organized
What's here, and what isn't
- A lecture sequence of 15 items is confirmed. No assignments or exams were found for this course, so it stays as organized materials rather than a full course order.
Not included here
7 items from the source could not be safely included:
- MIT's page format for this item wasn't recognized clearly enough to place it safely. (7 items)
Materials by section
Grouped by section, as MIT organized them. Not sequenced or ordered.
Instructor Insights
Lecture Notes
15 files
- 15.097 Lecture 1: Rule mining and the Apriori algorithm Download
- 15.097 Lecture 2: R for machine learning Download
- 15.097 Lecture 3: Fundamentals of learning Download
- 15.097 Lecture 4: Inference Download
- 15.097 Lecture 5: Clustering Download
- 15.097 Lecture 6: k-nearest neighbors Download
- 15.097 Lecture 7: Naïve Bayes Download
- 15.097 Lecture 8: Decision trees Download
- 15.097 Lecture 9: Logistic regression Download
- 15.097 Lecture 10: Boosting Download
- 15.097 Lecture 11: Convex optimization Download
- 15.097 Lecture 12: Support vector machines Download
- 15.097 Lecture 13: Kernels Download
- 15.097 Lecture 14: Statistical learning theory Download
- 15.097 Lecture 15: Bayesian analysis Download
Projects
- 15.097 Project Suggestions Download
- 15.097 Student Project: DC Programming Download
- 15.097 Student Project: Improving Tools for Medical Statistics Download
- 15.097 Student Project: Online k-Means Clustering Download
- 15.097 Student Project: Operator-Defined SUAV Classification Tree Algorithms Download
- 15.097 Student Project: Security Analysis using Machine Learning Download
Datasets
16 files
- Iris Download
- Iris description Download
- Wine Download
- Wine description Download
- Haberman’s Survival Download
- Haberman description Download
- Housing Download
- Housing description Download
- Blood Transfusion Service Center Download
- Transfusion description Download
- Car evaluation Download
- Car description Download
- Mushroom Download
- Mushroom description Download
- Pen-based recognition of handwritten digits Download
- Digits description Download
Other materials from this course
Course outline
Lecture Notes · Lecture Notes
Lecture 2: R for machine learning (PDF) (Courtesy of Allison Chang. Used with permission.)
Lecture Notes · Lecture Notes
Lecture 3: Fundamentals of learning (PDF)
Lecture Notes · Lecture Notes
Lecture 4: Inference (PDF)
Lecture Notes · Lecture Notes
Lecture 5: Clustering (PDF)
Lecture Notes · Lecture Notes
Lecture 6: k-nearest neighbors (PDF)
Lecture Notes · Lecture Notes
Lecture 7: Naïve Bayes (PDF)
Lecture Notes · Lecture Notes
Lecture 8: Decision trees (PDF)
Lecture Notes · Lecture Notes
Lecture 9: Logistic regression (PDF)
Lecture Notes · Lecture Notes
Lecture 10: Boosting (PDF)
Lecture Notes · Lecture Notes
Lecture 11: Convex optimization (PDF)
Lecture Notes · Lecture Notes
Lecture 12: Support vector machines (PDF)
Lecture Notes · Lecture Notes
Lecture 13: Kernels (PDF)
Lecture Notes · Lecture Notes
Lecture 14: Statistical learning theory (PDF)
Lecture Notes · Lecture Notes
Lecture 15: Bayesian analysis (PDF - 1.2MB) (Courtesy of Ben Letham. Used with permission.)
MIT OpenCourseWare, Prof. Cynthia Rudin, 15.097 Prediction: Machine Learning and Statistics, Spring 2012.
License: CC BY-NC-SA 4.0 · Edition: Spring 2012 · Open the original course on OCW ↗
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