Midterm Scheduling Form

Required for all students: If you haven't already, please complete this Midterm scheduling form at your earliest convenience.

Midterm 1

Date: Friday, April 24th, in class + 15min. (12:35-2:05, see details at midterm scheduling form link)
Location: NVIDIA
EXAM SOLUTIONS PDF

Topics Coverage:

• PSets 1-2
• Lectures 1-8 (through Wednesday 4/15)
• This Quick Reference Guide summarizes the main topics of the quarter so far. They include:
• Counting (combinations, permutations, multinomial)
• Probability (Conditional probability, Bayes, breaking into cases, chain rule) HW2 was full of these.
• Discrete distributions (Bernoulli, Binomial, Poisson, Geometric, Hypergeometric, Negative Binomial) We didn't spend much time on Geometric etc in class, but you should be ready to apply them to appropriate scenarios--use the Quizlet tutor to help you identify scenarios.
• Expectation, Variance, and Standard Deviation (calculating "from scratch" and applying known Distribution formulae)
• UPDATE:the Quick Reference Guide includes a section a section at the end on Independent Random Variables. You can ignore that one, that topic is for Midterm 2. You do need to know independence with events, which is an earlier section. Sorry for any confusion.

Other Info:

• You are allowed to bring one page of notes, front and back.
• Practice Exam, Solutions
• A midterm review session will be held right after class tomorrow (Wednesday), from 2:15 to 3:00 (right after lecture), in the same room (NVIDIA). SCPD will be recording it, so if you can't make it you can always watch the video.
Midterm 2

Date: Wednesday, May 27, in class + 25min. (12:25-2:05pm, see details at midterm scheduling form link)
Location: NVIDIA

Topics Coverage:

• PSets 1-5 (emphasis on 3-5)
• Lectures 1-20 (through Friday 5/15)
• This Quick Reference Guide summarizes the main topics of the quarter so far (updated for the whole course).
• Some of the new topics for Midterm #2:
• Continuous distributions (Uniform, Normal, Standard Normal, Exponential)
• Joint distributions, getting marginals
• Independence
• Convolution
• Conditional distributions and conditional expectation
• Covariance, correlation
• Moment Generating Functions