Instructor: Gwen McKinley (gmckinley@ucsd.edu) Course email address: 180A-staff-5pm-G@ucsd.edu
Lectures: 2-2:50pm on MWF, in York Hall 2622
Discussion sections: Tuesday evenings in AP&M 7321
Office hours:
| Time | Room | Person |
|---|---|---|
| Mondays 12:30-1:30pm | HSS 5012 | Nik Castro |
| Mondays 3-5pm | AP&M 6333 | Gwen McKinley |
| Tuesdays 1-3pm | HSS 5072 | Nicholas Sieger |
| Fridays 12:30-1:30pm | HSS 4012 | Nik Castro |
| Fridays 3-4pm | AP&M 6333 | Gwen McKinley |
For information on grading, textbook, acommodations, and more: see the Course Syllabus.
Due dates: here is a calendar of all homework due dates and exam dates.
Exams: There will be two in-person midterm exams held in class on the following dates:
The final exam will be held in person on Monday, Mar 20, from 3-6pm, location TBA.
Weekly homework assignments are posted here. Homework is due by 11:59pm on the posted date (generally Friday), through Gradescope. These dates are also listed on the calendar of assignments.
Note: there may be some slight changes to the assigments posted below (e.g., shifting a problem to a later homework), but each assgnment will be finalized no later than one week before its due date.
Below is a tentative schedule of what will be covered in each lecture. This is a rough schedule, and there will be some give and take between lectures.
Here is a more detailed breakdown of the specific material covered under each topic, and the corresponding chapters/sections in both books: Lecture Notes for Introductory Probability by Gravner, and A First Course in Probability by Ross.
| Week | Date | Topic | |
|---|---|---|---|
| 1 | 1/9 | Definition of Probability, Course Logistics (warm-up) | |
| 1/11 | Properties of Probability (warm-up) | ||
| 1/13 | Combinatorial Probability (review videos on counting) | ||
| 2 | 1/16 | No class (MLK day) | |
| 1/18 | Conditional Probability (warm-up 1, warm-up 2) | ||
| 1/20 | Bayes' Formula (warm-up 1, warm-up 2) | ||
| 3 | 1/23 | Independent Events (warm-up, picture) | |
| 1/25 | Discrete Random Variables | ||
| 1/27 | Expectation of Discrete Random Variables | ||
| 4 | 1/30 | Variance of Discrete Random Variables (warm-up) | |
| 2/1 | Binomial and Geometric Distributions (warm-up, slide 1, slide 2) | ||
| 2/3 | Midterm 1 | ||
| 5 | 2/6 | Poisson Distribution | |
| 2/8 | Continuous Random Variables (warm-up, slide 1, slide 2) | ||
| 2/10 | Expectation and Variance (continuous) | ||
| 6 | 2/13 | Exponential Distribution (warm-up, slide 1) | |
| 2/15 | Normal Distribution (warm-up, slide 1, table) | ||
| 2/17 | Joint Distributions of Random Variables (warm-up, slide 1) | ||
| 7 | 2/20 | No class (Presidents' Day) | |
| 2/22 | Independence of Random Variables | ||
| 2/24 | Sums of Independent Random Variables (warm-up) | ||
| 8 | 2/27 | Covariance and Correlation (warm-up, slide 1, spurious correlations, sharks!, correlation graphs, game!) |
|
| 3/1 | Moment Generating Functions, Part 1 (warm-up, slide 1, slide 2,
cool reading on differentiating integrals) |
||
| 3/3 | Midterm 2 | ||
| 9 | 3/6 | Moment Generating Functions, Part 2 (warm-up) | |
| 3/8 | Markov and Chebyshev Inequalities (warm-up) | ||
| 3/10 | Law of Large Numbers (note from last lecture, warm-up) | ||
| 10 | 3/13 | Central Limit Theorem, Part 1 | |
| 3/15 | Central Limit Theorem, Part 2 (warm-up) | ||
| 3/17 | Catch-up/review | ||
| Finals | 3/20 | Final Exam (Monday, 3-6pm) |