### Course Overview

This page focuses on the course *15.097 Prediction: Machine Learning and Statistics* as it was taught by Professor Cynthia Rudin in Spring 2012.

This introductory course provides a selection of the most important topics from the subjects of machine learning and statistical methods.

### Course Outcomes

#### Course Goals for Students

- Gain a foundational understanding of how machine learning and statistical algorithms work
- Explore statistical learning theory, which provides the mathematical foundation for machine learning algorithms
- Learn about Bayesian analysis
- Leave the course with a toolbox of algorithms that can be used on students’ own datasets

#### Possibilities for Further Study/Careers

15.097 prepares students for more in-depth study in fields involving predictive modeling.

### Curriculum Information

#### Prerequisites

- Permission of instructor.
- Knowledge of MATLAB® may be helpful.

#### Offered

This course was offered once in Spring 2012.

### Instructor Insights

*In the following paper, Professor Cynthia Rudin describes various aspects of how she taught* 15.097 Prediction: Machine Learning and Statistics. *Topics include the organizational structure of the course and the strategy of using of large-font lecture notes.*

Teaching “Prediction: Machine Learning and Statistics” (PDF)

### Assessment

The students’ grades were based on the following activities:

- 50% Problem sets
- 10% Course project proposal
- 2% Course project advertisement
- 38% Course project paper and talk

### Student Information

#### Enrollment

36 students

#### Breakdown by Year

Mostly graduate students

#### Breakdown by Major

Predominantly students from the Operations Research Center

#### Typical Student Background

Students had a general background in mathematics. Topics from machine learning and statistical methods were new to them.

### How Student Time Was Spent

During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:

#### Lecture

- Met 2 times per week for 1.5 hours per session; 25 sessions total.
- Lectures based on notes (distributed in class and through the course webpage), with some additional materials, such as source papers or excerpts from textbooks
- Student project talks scheduled during the last two weeks of the class

#### Out of Class

- Problem sets
- Course projects