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.