# 18.650

## Table of contents

- Course Info
- Realistic Prerequisites
- Subject Matter
- Course Staff
- Lectures
- Problem Sets
- Exams
- Resources
- Grading
- Advice to Future Students

## Course Info

Class Size | 135 |

Hours/Week | 8.1 (49 responses) |

Instructors | Tyler Maunu (Lecturer), Ashwin Narayan (Recitation Instructor), Jonathan Tidor (Recitation Instructor) |

# of Responses to Course 18 Underground Questions | 12/52 [CHANGE THIS] |

## Realistic Prerequisites

- Consider
**18.600**(or a substitute like 6.041) a**hard prerequisite**. - Students should be very comfortable with material from
**18.02**. - Some students found linear algebra experience from
**18.06**and knowledge of convergence from**18.100**helpful.

## Subject Matter

- Mostly
**theoretical**, but provides some real-world**applications**. - Fundamentals of statistics are covered thoroughly.
- Students learn to “confidently set up a statistical model for a given problem and derive useful statistical tools like estimators, confidence intervals, and hypothesis testing.”
- Some felt that the mock datasets felt a bit meaningless. Some also felt that results could have been proved more thoroughly.

## Course Staff

**Caring, engaging, and invested in the students’ learning.**- Prof. Maunu is very understanding, and was
**very active in answering questions on Piazza**. - Office hours, recitations, and review sessions were very good.
**Jonathan was especially helpful**, consistent, and patient when explaining concepts to students, even staying late after office hours.

## Lectures

- Lectures were
**engaging**, but**a bit slow**. - Prof. Maunu works through
**guiding examples**during the lecture, which students found helpful. - At times, there was not enough time in the lecture for Prof. Maunu to finish teaching the material or give enough guiding motivation for results.

## Problem Sets

- There was a problem set due every two weeks.
- The problem sets were fairly
**challenging**, but**followed the lectures**closely. - Students found them important for mastering the course material.
- Problem sets were
**somewhat lengthy**, taking some students up to 10 hours, but others about 6-8 hours. - Some students found later problem sets, which contained more calculations, tedious.

## Exams

- Difficulty was
**reasonable**. Problems required**little creativity**and closely matched material covered in problem sets. - Exams were
**open book**and**open notes**. - Due to the
**12 hour time limit**, exams were**not stressful**. However, they were designed to take 90 minutes to complete. - Some students found it easy to make silly computation mistakes and lose points for them.

## Resources

- There was
**no textbook**. While the professor recommended an optional, supplemental textbook, students**sorely missed**having an official textbook. - The main resource was
**lecture slides**annotated by Prof. Maunu, which students often found**inadequate**.

## Grading

**Very lenient and fair**.- Students performed quite well on exams. Many grades were in the 80s and 90s.
- Partial credit can sometimes be hard to predict, and cutoffs were not very transparent.

## Advice to Future Students

- Take the probability prereqs for this class or study up beforehand, go to office hours, find some PSet buddies.
- Have a good understanding of probability.
- If you wanted to take this class because statistics is the foundation of machine learning, I think you’ll also need to take some more class beyond this one. This class is a reasonable intro that would probably help with more advanced classes though.