Name  Role  Office  Office hours  
Yuriy Nemish  Instructor  AP&M 6321  ynemish@ucsd.edu  Wednesday 57 PM (AP&M 1260) 
Sheng Qiao  Teaching Assistant  HSS 5056  sqiao@ucsd.edu 

Kejin Wu  Teaching Assistant  HSS 5053  kwu@ucsd.edu  Thursday 3  5 PM 
Toni Gui  Teaching Assistant  HSS 4086A  ttgui@ucsd.edu 

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.
Date  Time  Room  
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 
Week  Date  Time  Room  Policy  
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  1.31.4  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  
10/19  4  Midterm 1  
10/21  4  Independent Trials. Binomial, Geometric, and Poisson Distributions  2.42.5, 4.4  Slides 12  Slides 12  
10/24  5  Poisson distribution. Expected Value  3.3  Slides 13  Slides 13  
10/26  5  Expectation  3.33.4  Slides 14  Slides 14  
10/28  5  Variance. Normal (Gaussian) Distribution  3.43.5  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  
11/11  7  Veterans Day  
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  6.16.3  Slides 23  Slides 23  
11/21  9  Joint distribution. Independence  6.16.3  Slides 24  Slides 24  
11/23  9  Expectations of sums and products  8.18.3  Slides 25  Slides 25  
11/25  9  Thanksgiving  
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  9.29.3  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.
Midterm Exams: The two midterm exams will take place during the lecture time on the dates listed above.
Final Exam: The final examination will be held at the date and time stated above.
The above exam policies will be applied to inperson 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:
Regrade Policy:
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:
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 zerotolerance 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.