|Yuriy Nemish||Instructor||AP&M firstname.lastname@example.org||Wednesday 5-7 PM (AP&M 1260)|
|Sheng Qiao||Teaching Assistant||HSS email@example.com||
|Kejin Wu||Teaching Assistant||HSS firstname.lastname@example.org||Thursday 3 - 5 PM|
|Toni Gui||Teaching Assistant||HSS 4086Aemail@example.com||
Please, check the following calendar for possible reschedulings of the office hours. You are welcome to attend the office hours of either of the TAs, not just your own.
|Lecture (YN)||Monday, Wednesday, Friday||4 - 4:50 PM||CENTR 105|
|Discussion C01 (SQ)||Tuesday||5 - 5:50 PM||AP&M 2301|
|Discussion C02 (SQ)||Tuesday||6 - 6:50 PM||AP&M 6402|
|Discussion C03 (KW)||Tuesday||7 - 7:50 PM||AP&M 6402|
|Discussion C04 (TG)||Tuesday||8 - 8:50 PM||AP&M 2301|
|Midterm Exam 1||4||Wednesday, Oct 19||4 - 4:50 PM||CENTR 105||Midterm Exams|
|Midterm Exam 2||8||Wednesday, Nov 16||4 - 4:50 PM||CENTR 105||Midterm Exams|
|Final Exam||Finals||Tuesday, Dec 6||3 - 6 PM||TBA||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 textboox. 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||Preliminary slides||Final slides|
|9/23||0||Administrivia. Definition of Probability||1.1||Slides 1||Slides 1|
|9/26||1||Random sampling||1.2||Slides 2||Slides 2|
|9/28||1||Basic Properties of Probability||1.3-1.4||Slides 3||Slides 3|
|9/30||1||Conditional Probability||2.1||Slides 4||Slides 4|
|10/3||2||Conditional probability||2.1||Slides 5||Slides 5|
|10/5||2||Bayes' rule||2.2||Slides 6||Slides 6|
|10/7||2||Independence||2.3||Slides 7||Slides 7|
|10/10||3||Random variables||3.1||Slides 8||Slides 8|
|10/12||3||Probability Distributions. CDF and PDF||3.1||Slides 9||Slides 9|
|10/14||3||CDF and PDF||3.2, 2.4||Slides 10||Slides 10|
|10/17||4||CDF and PDF. Review||Slides 11||Slides 11|
|10/21||4||Independent Trials. Binomial, Geometric, and Poisson Distributions||2.4-2.5, 4.4||Slides 12||Slides 12|
|10/24||5||Poisson distribution. Expected Value||3.3||Slides 13||Slides 13|
|10/26||5||Expectation||3.3-3.4||Slides 14||Slides 14|
|10/28||5||Variance. Normal (Gaussian) Distribution||3.4-3.5||Slides 15||Slides 15|
|10/31||6||Normal (Gaussian) Distribution||3.5||Slides 16||Slides 16|
|11/2||6||Normal Approximation. Law of Large Numbers||4.1||Slides 17||Slides 17|
|11/4||6||Confidence intervals||4.3||Slides 18||Slides 18|
|11/7||7||Confidence intervals. Poisson approximation||4.4||Slides 19||Slides 19||Prerecorded video|
|11/9||7||Exponential Distribution||4.5||Slides 20||Slides 20|
|11/14||8||Moment generating function||5.1||Slides 21||Slides 21|
|11/16||8||Moment generating function||5.1||Slides 22||Slides 22|
|11/18||8||Joint distribution. Independence||6.1-6.3||Slides 23||Slides 23|
|11/21||9||Joint distribution. Independence||6.1-6.3||Slides 24||Slides 24|
|11/23||9||Expectations of sums and products||8.1-8.3||Slides 25||Slides 25|
|11/28||10||Covariance, correlation, and variance of sums||8.4||Slides 26||Slides 26|
|11/30||10||Correlation. Tail probabilities||9.1||Slides 27||Slides 27|
|12/2||10||Law of Large Numbers. Central Limit Theorem||9.2-9.3||Slides 28||Slides 28|
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.
The above exam policies will be applied to in-person exams. The exam policies will be changed in case of the change in exam modality. More detailed instructions will be posted on this website later.
Administrative Links: Here are two links regarding UC San Diego policies on exams:
Grading: Your cumulative average will be computed as the best of the following 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:
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 11:59 PM on the posted date, through Gradescope. Late homework will not be accepted.