Name | Role | Office hours (OH) | Zoom link for OH | |
Benson Au | Instructor | Th 5:30-7 PM PT Fri 1-2:30 PM PT |
See Canvas | bau@ucsd.edu |
Sheng Qiao | Teaching Assistant | Tu 1-2 PM PT Wed 2-3 PM PT |
See Canvas | altodd@ucsd.edu |
Alec Todd | Teaching Assistant | Th 1-3 PM PT | See Canvas | sqiao@ucsd.edu |
Note: in the event of an office hours change, the schedule below will reflect the latest information. You are welcome to attend the office hours of either of the TAs, not just your own.
Date | Time | Location | |
Lecture C00 (Au) | Monday, Wednesday, Friday | Asynchronous lectures | Video links below |
Discussion C01 (Todd) | Thursday | 9:00am - 9:50am | See Canvas |
Discussion C02 (Todd) | Thursday | 10:00am - 10:50am | See Canvas |
Discussion C03 (Qiao) | Thursday | 11:00am - 11:50am | See Canvas |
Discussion C04 (Qiao) | Thursday | 12:00pm - 12:50pm | See Canvas |
Week | Date | Details | |
Quiz 1 | 2 | Wednesday, Oct 14 | see Quizzes |
Quiz 2 | 3 | Wednesday, Oct 21 | see Quizzes |
Midterm 1 | 4 | Wednesday, Oct 28 | see Midterms Exams |
Quiz 3 | 5 | Wednesday, Nov 4 | see Quizzes |
Quiz 4 | 7 | Wednesday, Nov 18 | see Quizzes |
Midterm 2 | 8 | Monday, Nov 23 | see Midterm Exams |
Quiz 5 | 10 | Wednesday, Dec 9 | see Quizzes |
Final Exam | Finals week | Thursday, Dec 17 | see Final exam |
Welcome to Math 180A: a one quarter course introduction to probability theory. This course is the prerequisite for the subsequent courses Math 180B/C (Introduction to Stochastic Processes) and Math 181A/B (Introduction to Mathematical Statistics), as well as for MATH 114 (Introduction to Computational Stochastics), MATH 194 (The Mathematics of Finance), and Math 189 (Exploratory Data Analysis and Inference). According to the UC San Diego Course Catalog, the topics covered are probability spaces, random variables, independence, conditional probability, discrete and continuous probability distributions, joint distributions, variance and moments, the Laws of Large Numbers, and the Central Limit Theorem.
Here is a more detailed listing of course topics, in the sequence they will be covered, together with the relevant section(s) of the textbook. While each topic corresponds to approximately one lecture, there will be some give and take here. This is a rough schedule that will be updated during the term.
Date | Week | Topic | ASV | Slides | Lectures | Additional videos |
10/02 | 0 | Administrivia | ||||
10/05 | 1 | Definition of probability, Random sampling | 1.1, 1.2 | Lec 1 | Lec 1 | |
10/07 | 1 | Random sampling, Basic properties of probability | 1.2, 1.4 | Lec 2 | Lec 2 | |
10/09 | 1 | Conditional probability | 2.1 | Lec 3 | Lec 3 | |
10/12 | 2 | Bayes' rule, independence | 2.2, 2.3 | Lec 4 | Lec 4 | |
10/14 | 2 | Random variables and probability distributions | 1.5, 3.1 | Lec 5 | Lec 5 | |
10/16 | 2 | Probability distributions | 3.1, 3.2 | Lec 6 | Lec 6 | |
10/19 | 3 | Probability densities and the cumulative distribution function | 3.2 | Lec 7 | Lec 7 | |
10/21 | 3 | Binomial, geometric, and Poisson distributions | 2.4, 2.5, 4.4 | Lec 8 | Lec 8 | |
10/23 | 3 | Expected value | 3.3 | Lec 9 | Lec 9 | |
10/26 | 4 | Review | ||||
10/28 | 4 | Midterm 1 | Solutions | |||
10/30 | 4 | Expected value | 3.3 | Lec 10 | Lec 10 | |
11/02 | 5 | Variance, Normal (Gaussian) distribution | 3.4, 3.5 | Lec 11 | Lec 11 | |
11/04 | 5 | Gaussian distribution, Normal approximation | 3.5, 4.1 | Lec 12 | Lec 12 | |
11/06 | 5 | Normal approximation, Law of large numbers | 4.1, 4.2 | Lec 13 | Lec 13 | |
11/09 | 6 | Confidence intervals, Poisson approximation | 4.3, 4.4 | Lec 14 | Lec 14 | |
11/11 | 6 | Veterans day | ||||
11/13 | 6 | Exponential distribution | 4.5 | Lec 15 | Lec 15 | |
11/16 | 7 | Moment generating function | 5.1 | Lec 16 | Lec 16 | |
11/18 | 7 | Distribution of a function of a random variable | 5.2, 6.1 | Lec 17 | Lec 17 | |
11/20 | 7 | Review | ||||
11/23 | 8 | Midterm 2 | Solutions | Q1 and Q2 Q3 Q4 |
||
11/25 | 8 | Joint distributions | 6.1, 6.2 | Lec 18 | Lec 18 | |
11/27 | 8 | Thanksgiving | ||||
11/30 | 9 | Joint distributions, independence of random variables | 6.2, 6.3 | Lec 19 | Lec 19 | |
12/02 | 9 | Independence of random variables, convolution | 6.3, 7.1 | Lec 20 | Lec 20 | |
12/04 | 9 | Linearity of expectation, expectation and independence | 8.1, 8.2 | Lec 21 | Lec 21 | |
12/07 | 10 | Sums and moment generating functions, covariance, and corelation | 8.3, 8.4 | Lec 22 | Lec 22 | |
12/09 | 10 | Law of large numbers, central limit theorem | 9.2, 9.3 | Lec 23 | Lec 23 | |
12/11 | 10 | Review |
Prerequisite: The only prerequisites are calculus up to and including Math 20C (Multivariate Calculus) or Math 31BH (Honors Multivariable Calculus). Math 109 (Mathematical Reasoning) is also strongly recommended as a prerequisite or corequisite.
Lecture: You are responsible for material presented in the lecture whether or not it is discussed in the textbook. You should expect questions on the exams that will test your understanding of concepts discussed in the lecture.
Homework: Homework assignments are posted below, and will be due at 11:59 PM on the indicated due date. You must turn in your homework through Gradescope; if you have produced it on paper, you can scan it or simply take clear photos of it to upload. Your lowest homework score will be dropped. It is allowed and even encouraged to discuss homework problems with your classmates and your instructor and TA, but your final write up of your homework solutions must be your own work.
Quizzes: The quizzes will take place on the dates listed above.
Midterm Exams: The midterm exams will take place on Oct 28 and Nov 23 as listed above.
Final Exam: The final examination will be held at the officially scheduled time: 7 - 10 PM PST on Dec 17.
About 15% of students reported for cheating are suspended or dismissed from UCSD.
Administrative Links: Here are two links regarding UC San Diego policies on exams:
Regrade Policy:
Grading: Your cumulative average will be the best of the following two weighted averages:
Your course grade will be determined by your cumulative average at the end of the quarter, and will be based on the following scale:
A+ | A | A- | B+ | B | B- | C+ | C | C- |
97 | 93 | 90 | 87 | 83 | 80 | 77 | 73 | 70 |
The above scale is guaranteed: for example, if your cumulative average is 80, your final grade will be at least B-. However, your instructor may adjust the above scale to be more generous.
Academic Integrity: UC San Diego's code of academic integrity outlines the expected academic honesty of all students and faculty, and details the consequences for academic dishonesty. The main issues are cheating and plagiarism, of course, for which we have a zero-tolerance policy. (Penalties for these offenses always include assignment of a failing grade in the course, and usually involve an administrative penalty, such as suspension or expulsion, as well.) However, academic integrity also includes things like giving credit where credit is due (listing your collaborators on homework assignments, noting books or papers containing information you used in solutions, etc.), and treating your peers respectfully in class. In addition, here are a few of our expectations for etiquette in and out of class.
Weekly homework assignments are posted here. Homework is due by 11:59pm on the posted date, through Gradescope. Late homework will not be accepted.