HW8 Grades Released : Homework 8 has been graded, and your grades have been released; please check Gradescope (where you will find the correct answers in the rubric for each problem). Regrade requests will be active from 8am - 11pm on Friday, December 13 ONLY.Lab 7 Grades Released : Lab 7 has been graded, and your grades have been released; please check Gradescope. Solutions have been posted in the datahub. Regrade requests will be active from 8am - 11pm on Thursday, December 12 ONLY.The Final Exam : took place on Monday, December 9, from 11:00am-2:30pm, in the REC GYM.- Here are SOLUTIONS for the Final Exam.
- Here are 180A-Final-Practice-Solutions.pdf.

HW7 Grades Released : Homework 7 has been graded, and your grades have been released; please check Gradescope (where you will find the correct answers in the rubric for each problem). Regrade requests will be active from 8am - 11pm on Tuesday, December 10 ONLY.Homework 8 Solutions : Since Homework 8 will not be graded before the Final Exam, here are solutions to those problems (short numerical solutions for those turned in, just as you will later see in the grading rubric, and full solutions for the 5 you did not turn in from Chapter 9).

180A-HW8-Solutions.pdf.Lab 6 Grades Released : Lab 6 has been graded, and your grades have been released; please check Gradescope. Solutions have been posted in the datahub. Regrade requests will be active from 8am - 11pm on Thursday, December 5 ONLY.Midterm Exam 2 : Grades have been released, and statistics sent out to the class via email. Regrade requests for the midterm will be active from 8am - 11pm on Wednesday, December 4 ONLY.HW6 Grades Released : Homework 6 has been graded, and your grades have been released; please check Gradescope (where you will find the correct answers in the rubric for each problem). Regrade requests will be active from 8am - 11pm on Tuesday, December 3 ONLY.Midterm Exam 2 : took place on Wednesday, November 20, from 8-10pm, in CENTR 101.- Here are SOLUTIONS for Midterm 2.
- Here are 180A-Practice-Midterm2-Solutions.pdf.

Midterm Exam 1 : took place on Wednesday, October 23, from 8-10pm, in PCYNH 109.- Here are SOLUTIONS for Midterm 1.
- Here are 180A-Practice-Midterms-Solutions.pdf.

Podcasts and Slides : The lectures are being podcast, both as screencast and video. In additon, you can find the slides (both before and after class) below.Computer Labs : Your sections have been rescheduled to take place in computer labs. Please refer to the locations below. You may need a door code to access the lab. You can look up your personal door code by logging into https://sdacs.ucsd.edu/~icc/index.php.Welcome : to Math 180A: Introduction to Probability (for Data Science), in Fall 2019!

Textbook : The required textbook for this course is

*Introduction to Probability*, by David F. Anderson, Timo Seppäläinen, and Benedek Valkó; DOI 10.1017/9781108235310

As an auxiliary source, we will also refer to the (free) online textbook for*Probability for Data Science*(Prob 140 at UC Berkeley) available at

*Probability for Data Science*prob140.org/textbook

Reading and homeowork assignments will refer to these two sources (labeled as ASV and DSC).Coursework : There will be weekly homework assignments due on Fridays (starting in Week 1); they are posted below. There are 7 data science labs, partly done during discussion sections, partly to be done on your own and due on Mondays (starting in Week 2); they can be accessed through DataHub. There will be two evening midterm exams and a final exam; dates, times, and locations posted below.DataHub is UC San Diego's implementation of jupyter hub: a web-based live coding platform. We will use it to host the Data Science Labs component of this course. After classes have begun, you should be able to login to the DataHub and access the Math 180A Data Science Labs. Your first discussion section will be devoted to helping you become familiar with jupyter notebooks.Piazza is an online discussion forum; we will use Piazza for the three lectures of Math 180A combined. It will allow you to post messages (openly or anonymously) and answer posts made by your fellow students, about course content, homework, exams, etc. The instructors and TAs will also monitor and post to Piazza regularly. You can sign up here.**Note:**Piazza has an opt-in "Piazza Careers" section which, if you give permission, will share statistics about your Piazza use with potential future employers. It also has a "social network" component, based on other students who've shared a Piazza-based class with you, that comes with the usual warnings about privacy concerns. Piazza is fully FERPA compliant, and is an allowed resource at UCSD. Nevertheless, you are not required to use Piazza if you do not wish.Gradescope is an online tool for uploading and grading assignments an exams (it is now under the umbrella of Turnitin). You will turn in your homework and labs through Gradescope, and you will access your graded exams there as well. Access the class Gradescope site here.

Name | Role | Office | |

Todd Kemp | Instructor | APM 5202 | tkemp@ucsd.edu |

Denise Rava | Teaching Assistant | APM 2220 | drava@ucsd.edu |

Jiangchuanhai Wang | Teaching Assistant | APM 1220 | jiw078@ucsd.edu |

Lin Zheng | Teaching Assistant | APM 2000A | liz176@ucsd.edu |

Date | Time | Location | |

Lecture B00 (Kemp) | Monday, Wednesday, Friday | 1:00pm - 1:50pm | CENTR 115 |

Discussion B01 (Rava) | Tuesday | 5:00pm - 5:50pm | ERC 117 |

Discussion B02 (Rava) | Tuesday | 6:00pm - 6:50pm | ERC 117 |

Discussion B03 (Wang) | Tuesday | 7:00pm - 7:50pm | ERC 117 |

Discussion B04 (Wang) | Tuesday | 8:00pm - 8:50pm | ERC 117 |

Discussion B05 (Zheng) | Tuesday | 8:00pm - 8:50pm | CSB 115 |

Discussion B06 (Zheng) | Tuesday | 9:00pm - 9:50pm | CSB 115 |

First Midterm Exam | Wednesday, Oct 23 | 8:00pm - 9:50pm | PCYNH 109 |

Second Midterm Exam | Wednesday, Nov 20 | 8:00pm - 9:50pm | CENTR 101 |

Final Exam | Monday, Dec 9 | 11:30am - 2:29pm | REC GYM |

All lectures of this course are podcast, both as a screencast and classroom video; they podcasts are available beginning right after the lecture at podcast.ucsd.edu.

The lectures are typically given via tablet, on notes/slides with some information prepared before lecture, and some filled-in during the lecture. Below, you will find the before and after slides for each lecture (as they are produced).

Math 180A is 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.

This lecture (B00) is a new version of Math 180A, which is targeted towards data science theory and applications. The course material is the same as the other lecture of Math 180A; the primary addition is the Python-based lab component of this course, which can be accessed through Jupyter Hub. We will also refer to the online textbook Probability for Data Science by Adhikari and Pitman as a secondary resource for the lecture.

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 | ASV | DSC |

09/27 | 0 | Administrivia and Motivation | ||

09/30 | 1 | Definition of Probability, Sampling, Combinatorics | 1.1-1.2 | 1.1-1.2, 2.0 |

10/02 | 1 | Uniform Probability, Basic Properties of Probability | 1.3-1.4 | 2.0-2.2, 5.2 |

10/04 | 1 | Conditional Probability, Bayes' Rule | 2.1-2.2 | 2.3-2.5 |

10/07 | 2 | Independence | 2.3 | 4.5 |

10/09 | 2 | Random Variables | 1.5, 3.1 | 3.0-3.1 |

10/11 | 2 | Probability Distributions | 3.1-3.2 | 3.2, 15.1 |

10/14 | 3 | Independent Trials and Sampling | 2.4-2.5 | |

10/16 | 3 | Binomial, Geometric, and Poisson Distributions | 3.1-3.2, 4.4 | 6.0-6.2, 6.5 |

10/18 | 3 | Expected Value | 3.3 | 8.1, 8.3, 15.3 |

10/21 | 4 | Variance | 3.4 | 12.1 |

10/23 | 4 | Review (+ Evening Midterm) |
||

10/25 | 4 | Normal (Gaussian) Distribution | 3.5 | 18.1 |

10/28 | 5 | Normal Approximation | 4.1-4.2 | |

10/30 | 5 | Confidence Intervals | 4.3 | |

11/1 | 5 | Poisson Approximation | 4.4 | 6.5, 7.0 |

11/4 | 6 | Exponential Distribution | 4.5 | 15.4 |

11/6 | 6 | Poisson Process | 4.6 | |

11/8 | 6 | Moment Generating Function | 5.1 | 16.1-16.3 |

11/11 | 7 | Veterans Day |
||

11/13 | 7 | Functions of Random Variables | 5.2 | 19.2 |

11/15 | 7 | Joint Distributions | 6.1-6.2 | 4.1, 4.3, 17.1, 17.3 |

11/18 | 8 | Independence of Random Variables | 6.3 | 4.5, 17.2 |

11/20 | 8 | Review (+ Evening Midterm) |
||

11/22 | 8 | Expectations of sums | 8.1-8.3 | 8.2, 19.2 |

11/25 | 9 | Covariance, correlation, and variance of sums | 8.4 | 13.0-13.3 |

11/27 | 9 | Law of Large Numbers | 9.1-9.2 | 12.3, 14.4 |

11/29 | 9 | Thanksgiving |
||

12/2 | 10 | Central Limit Theorem | 9.3 | 14.3, 19.3 |

12/4 | 10 | Review | ||

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.
For the lab component of the course, some familiarity with any coding language (ideally Python) is helpful, but not
required.

**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. 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.

**Labs:** The data science labs are accessible through DataHub. The turn-in components
should be exported as pdf files and turned in through Gradescope; they are due at 11:59pm on the dates indicated on the labs.

**Lowest two scores:** There will be 15 assignments throughout the term: 8 homework sets and 7 labs. Among these, only the 13 highest scores
will be counted towards your grade; the two lowest scoring assignments be dropped.

**Midterm Exams:** The two midterm exams will take place in the evenings of the dates listed above.
The scheduled time for each midterm exam is 2 hours; however, the exam itself is designed for you to complete in 50 minutes. The 2 hours
time-limit will also be lax. *We do not want time pressure to be a factor in your exam performance.*

- You may bring
**one**8.5 by 11 inch sheet of paper with handwritten notes (on both sides) with you to each midterm exam; no other notes (or books) will be allowed. - No calculators, phones, or other electronic devices will be allowed during the midterm exams.
**There will be no makeup midterm exams.**

**Final Exam:** The final examination will be held at the date and time stated above.

- It is your responsibility to ensure that you do not have a schedule conflict involving the final examination; you should not enroll in this class if you cannot take the final examination at its scheduled time.
- You may bring
**two**8.5 by 11 inch sheets of paper with handwritten notes (on both sides) with you to the final examination; no other notes (or books) will be allowed. - No calculators, phones, or other electronic devices will be allowed during the final examination.
**There will be no makeup final exam.**

**Administrative Links:** Here are two links regarding UC San Diego policies on exams:

- Exam Responsibilities An outline of the responsibilities of faculty and students with regard to final exams
- Policies on Examinations The Academic Senate policy regarding final examinations (These are the rules!)

**Regrade Policy:**

- Your exams, homeworks, and labs will be graded using Gradescope.
You will be able to request regrades
through Gradescope for a specified window of time. Be sure to make your request within the specified window of time; no regrade requests will be accepted after the deadline.*directly from your TA***Note:**Your grader will consider your regrade request only if you have explained clearly, thoroughly, and politely why you think an error in grading was made.

**Grading:**
Your cumulative average will be the best of the following two weighted averages:

- 15% Homework, 15% Labs, 15% Exam I, 15% Exam II, 40% Final Exam
- 15% Homework, 15% Labs, 15% Best Midterm Exam, 55% Final Exam

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.

**Entering/exiting class:**Please arrive on time and stay for the entire class/section period. If, despite your best efforts, you arrive late, please enter quietly through the rear door and take a seat near where you entered. Similarly, in the rare event that you must leave early (e.g. for a medical appointment), please sit close to the rear exit and leave as unobtrusively as possible.**Noise and common courtesy:**When class/section begins, please stop your conversations. Wait until class/section is over before putting your materials away in your backpack, standing up, or talking to friends. Do not disturb others by engaging in disruptive behavior. Disruption interferes with the learning environment and impairs the ability of others to focus, participate, and engage.**Electronic devices:**Please do not use devices (such as cell phones, laptops, tablets, iPods) for non-class-related matters while in class/section. No visual or audio recording is allowed in class/section without prior permission of the instructor (whether by camera, cell phone, or other means).**E-mail etiquette:**You are expected to write as you would in any professional correspondence. E-mail communication should be courteous and respectful in manner and tone. Please do not send e-mails that are curt or demanding.

Weekly homework assignments are posted here. Homework is due by 11:59pm on the posted date, through Gradescope. Late homework will not be accepted.

- Homework 0, due Monday, September 30.
- Homework 1, due Friday, October 4.
- Homework 2, due Friday, October 11.
- Homework 3, due Friday, October 18.
- Homework 4, due Friday, November 1.
- Homework 5, due Friday, November 8.
- Homework 6, due Friday, November 15.
- Homework 7, due Wednesday, November 27.
- Homework 8, due Friday, December 6.