F | C- | C | C+ | B- | B | B+ | A- | A | A+ |
< 34 | 34-46 | 46-51 | 51-57 | 57-62 | 62-67 | 67-73 | 73-88 | 81-88 | > 88 |
Name | Role | Office | Office hours | |
Yuriy Nemish | Instructor | AP&M 6422 | ynemish@ucsd.edu |
|
Toni Gui | Teaching Assistant | AP&M 1220 | ttgui@ucsd.edu |
|
Andrew Ying | Teaching Assistant | AP&M 1111 | anying@ucsd.edu |
|
Date | Time | Location | |
Lecture A00 (Nemish) | Monday, Wednesday, Friday | 9:00am - 9:50am | CENTR 113 |
Discussion A01 (Gui) | Monday | 6:00pm - 6:50pm | CENTR 218 |
Discussion A02 (Gui) | Monday | 7:00pm - 7:50pm | CENTR 218 |
Discussion A03 (Ying) | Monday | 8:00pm - 8:50pm | CENTR 218 |
Discussion A04 (Ying) | Monday | 9:00pm - 9:50pm | CENTR 218 |
First Midterm Exam | Wednesday, Oct 23 | 9:00am - 9:50am | CENTR 113 |
Second Midterm Exam | Wednesday, Nov 20 | 9:00am - 9:50am | CENTR 113 |
Final Exam | Wednesday, Dec 11 | 8:00am - 10:59am | CENTR 113 |
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) and Math 189 (Exploratory Data Analysis and Inference). It is also prerequisite for the new Data Science topics course DSC 155 (Hidden Data in Random Matrices) in Winter 2020. 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 textboox. While each topic corresponds to approximately one lecture, there will be some give and take here.
Date | Week | Topic (updated) | ASV (updated) | Preliminary slides | Final slides |
---|---|---|---|---|---|
09/27 | 0 | Definition of Probability | 1.1 | - | - |
09/30 | 1 | Random sampling | 1.2 | - | - |
10/02 | 1 | Basic Properties of Probability | 1.4 | - | - |
10/04 | 1 | Conditional Probability | 2.1 | - | - |
10/07 | 2 | Bayes' Rule. Independence | 2.2-2.3 | - | - |
10/09 | 2 | Random Variables | 1.5, 3.1 | - | Lecture 6 |
10/11 | 2 | Probability Distributions | 3.1-3.2 | - | - |
10/14 | 3 | Independent Trials and Sampling | 2.4-2.5 | Lecture 8 | Lecture 8 |
10/16 | 3 | Binomial, Geometric, and Poisson Distributions | 2.4-2.5, 4.4 | Lecture 9 | Lecture 9 |
10/18 | 3 | Expected Value | 3.3 | Lecture 10 | Lecture 10 |
10/21 | 4 | Review | Lecture 11 | Lecture 11 (updated) | |
10/23 | 4 | Midterm 1 | |||
10/25 | 4 | Variance | 3.4 | Lecture 13 | Lecture 13 |
10/28 | 5 | Normal (Gaussian) Distribution | 3.5 | Lecture 14 | Lecture 14 |
10/30 | 5 | Normal Approximation | 4.1-4.2 | Lecture 15 | Lecture 15 |
11/1 | 5 | Confidence Intervals | 4.3 | Lecture 16 | Lecture 16 |
11/4 | 6 | Poisson Approximation | 4.4 | Lecture 17 | Lecture 17 |
11/6 | 6 | Exponential Distribution | 4.5 | Lecture 18 | Lecture 18 |
11/8 | 6 | Moment Generating Function | 5.1-5.2 | Lecture 19 | Lecture 19 |
11/11 | 7 | Veterans Day | |||
11/13 | 7 | Joint Distributions | 5.2-6.1 | Lecture 20 | Lecture 20 |
11/15 | 7 | Joint distrubutions | 6.1-6.2 | Lecture 21 | Lecture 21 |
11/18 | 8 | Review | |||
11/20 | 8 | Midterm 2 | |||
11/22 | 8 | Independence of Random Variables | 6.3 | Lecture 22 | Lecture 22 |
11/25 | 9 | Expectations of sums and products | 8.1-8.3 | Lecture 23 | Lecture 23 |
11/27 | 9 | Covariance, correlation, and variance of sums | 8.4 | Lecture 24 | Lecture 24 |
11/29 | 9 | Thanksgiving | |||
12/2 | 10 | Tail probabilities | 9.1 | Lecture 25 | Lecture 25 |
12/4 | 10 | Law of Large Numbers. Central Limit Theorem | 9.2-9.3 | Lecture 26 | Lecture 26 |
12/6 | 10 | Review |
Prerequisite: The only prerequisites are calculus up to and including Math 20C (Multivariate Calculus). Math 109 (Mathematical Reasoning) is also strongly recommended as a prerequisite or corequisite.
Lecture: Attending the lecture is a fundamental part of the course; 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 11:59pm 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.
Midterm Exams: The two midterm exams will take place during the lecture time at the dates listed above.
Final Exam: The final examination will be held at the date and time stated above.
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 three 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 studentd 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.