Spring 2022: Math 210, Math in the age of information


Key info

Lectures: TR, 1:45pm-3:15pm

  • Lecture notes on Canvas

Instructor: Truong-Son Van,   Email: tsvan+210@sas.upenn.edu,   Office: DRL 3N8C

Office Hours (Instructor): T: 5-6pm (zoom), W:4-5pm (in person)

TA: TBD,   Office Hours (TA):

Prerequisites: Math 114

Penn’s COVID-19 guidance

  • Masks are required indoors in public and shared spaces for ALL, including those who are fully vaccinated.
    • Exceptions to the masking requirement include single occupancy offices and shared spaces where 6ft distancing can be maintained, with roommates in our college house suites/rooms, and by permission in instructional settings for academic reasons.

Important dates

  • Midterm 1: Feb 15
  • Midterm 2: Mar 22
  • Presentation: TBD
  • First day of class: Jan 12
  • MLK Jr. Day (no classes): Jan 17
  • Course selection period ends: Jan 25
  • Drop period ends: Feb 21
  • Spring break: March 5-13
  • Last day to withdraw: Mar 28

Textbook(s) and References

There is no official textbook for this course. I will provide lecture notes as we go along. However, materials will be taken from these sources:

  • Convex Analysis by Boyd and Vandenberghe [link]. This book is a goto reference of many professionals. The level is a bit higher than that of this course but a good book to have on the shelf.

  • Lecture notes by Griffin [link]. This looks nice and goes straight to linear programming, which is one of the main goal of this course.

  • Lecture notes by Laurent Ghaoui [link].

  • Lecture notes by Calder [link]. This is a very good set of notes for the mathematics of image and data analysis.

  • Information theory, inference, and learning algorithm by MacKay [link].

  • Deep learning by Goodfellow [link]. Written by one of the designer of generative adversarial network.

Course description and learning objectives

This course is an attempt for me to learn together with the students some of the common tools that are used to process information. Depending on time, topics such as linear programming, probability, information theory, game theory, and machine learning will be discussed. With luck, we will explore how to relate some of these concepts to understand the world.

Class Policies (subject to change)


  • Lectures will be recorded and livestreamed but the recordings will not be readily available (see attendance section for more details).
  • If you must sleep, please don’t snore. (Thanks Gautam Iyer for this amazing policy!)
  • Please be respectful to your classmates.


  • Attendance is strongly encouraged, either in person or on a live Zoom meeting. If you have to be absent for any reason, please submit a Course Absence Report. Only those students who submit the reports will have access to lecture recordings in case then want to catch up.


  • Homework must be turned in by 23:59 p.m. ET on the due date.
  • All homework must be scanned and submitted electronically (I will NOT take homework slipped under my door).
  • Collaboration for homework is strongly encouraged but you MUST write up your own work. Word-to-word copying is plagiarism.
  • Generously credit all of the people who you collaborate with at the beginning of your work.
  • If you use outside sources (internet, books, friends, etc.) for a particular problem, acknowledge them at the beginning of the problem. You will NOT be penalized for consulting outside sources as long as you credit them.
  • Late homework policy:
    • Late homework wll NOT be accpeted. However, the two worst homeworks will be dropped.
  • Advice:
    • Eat well and get enough sleep.
    • Start early. One problem per day is more pleasant than seven problems in one night.
    • Try to understand the materials rather than rote memorization. This will show in exams.
    • Try to write clearly and demonstrate clarity of thoughts.


There are two midterms. Please make sure to adhere to the following points:

  • NO collaboration
  • NO calculator


There will be one group project and a presentation of the project by the end of the class.

  • You will need to pair up with other people to do projects. Each team will consist of 3 or 4 students. Teams with fewer members will need to have a good reason for a permission.

  • The topics will be posted on Canvas.

  • Timeline:

    1. Select teammates and send the list of members to the instructor by Friday, Feb 25 2022.

    2. Schedule a group meeting (with all members of the group) with the instructor by Friday, March 4 to select the topic you’d like to present and the role of each member. During this meeting, we will also schedule a 30-minute weekly meeting for progress report.

Grading (subject to change)

Homework is 60% 75%, each exam is 15%, and the final project is 25% of the grade.

A: 93% or above, B: 83% or above, C: 73% or above, D: 63% or above.


In general, take good care of your health. You’re a human being first, before a student. Your academinc performance will be affected if you are not in good health. If you experience mental health issues, please consider counseling at Penn’s Counseling & Psychological services. It is NOT a weakness to seek help. I do that from time to time.

  • 24/7 mental health hotline (CAPS): 215-898-7021.

Accomodations for Students with Disabilities

If you have a disability and have a letter from the Student Disabilities Services office, please meet and discuss with me as early as possible so I can make appropriate accomodations for you.

Academic Integrity

Please read the Code of Academic Integrity carefully.

Cheating will NOT be tolerated and will be reported to the Office of Student Conduct. In the worst case, it can result in expulsion.

That said, make sure you keep the following points:

  • Discussing homework is not cheating and strongly encouraged.
  • You need to write up your own solutions after discussions. Word-by-word copying is cheating.