Section outline

►Week 1 (September 10-11)
  • "Probability theory is nothing but common sense reduced to calculation." Pierre-Simon de Laplace, 1812 (see other interesting quotes from Pierre-Simon de Laplace)

    Lectures:

    • In-person in the room DIA 004 on Wed 1-4 PM and ELG 120 on Thu 9-12 AM.
    • Thursday lectures will take place only on select weeks. Other weeks Thursdays are reserved for the exercise session. This will be clearly marked each week on Moodle.

    Exercise Sessions:

    • In-person in the room ELG 120 on Thu 9-12 PM on select weeks. This will be clearly marked each week on Moodle. 
    • Problem sets will be posted for each unit covered in lecture. You are expected to work on them outside of class and during exercise sessions. Problem sets will not be graded. However, it is important that you do them regularly if you would like to succeed in the course.

    Grading Scheme:

    • Midterm exam #1 - 25%
    • Midterm exam #2 - 25% 
    • Final exam - 50%

    Midterm Exam #1: Wednesday, October 8, 1:15pm - 4pm, room DIA 004 and AAC 0 08.

    •  Allowed material: one cheat sheet (i.e., two single-sided A4 handwritten pages).

    Midterm Exam #2: Wednesday, November 26, 1:15pm - 4pm, AAC 2 31.

    •  Allowed material: two cheat sheets (i.e., four single-sided A4 handwritten pages). You may re-use the cheatsheet from midterm 1, and create a new additional page. Or, create two pages from scratch.


    Final Exam: Wednesday, January 21, 2026, 9:15am - 12:15pm, room CM 1 121.

    • Allowed material: two cheat sheets (i.e., four single-sided A4 handwritten pages).
    • Please note that the exam content will focus more on the part of the course not covered on the midterms, but will also cover material already covered by the midterms.

    Course Instructor:

    Prof. Yanina Shkel     || INR 131 || yanina.shkel@epfl.ch
    Teaching and Student Assistants:

    Cemre Çadir || INR 031 || cemre.cadir@epfl.ch
    Anas Himmi || anas.himmi@epfl.ch
    Pierre Fasterling || pierre.fasterling@epfl.ch

    Course Webpage:


    References:

    • Terence Tao, An Introduction to Measure Theory, Preprint, Softcover ISBN: 978-1-4704-6640-4
    • Sheldon M. Ross, Erol A. Pekoz, A Second Course in Probability, 1st edition, 2007.
    • Jeffrey S. Rosenthal, A First Look at Rigorous Probability Theory, 2nd edition, World Scientific, 2006.
    • Geoffrey R. Grimmett, David R. Stirzaker, Probability and Random Processes, 3rd edition, Oxford University Press, 2001.
    • Sheldon M. Ross, Stochastic Processes, 2nd edition, Wiley, 1996.
    • William Feller, An Introduction to Probability Theory and Its Applications, Vol. 1&2, Wiley, 1950.
    • (more advanced) Richard Durrett, Probability: Theory and Examples, 4th edition, Cambridge University Press, 2010.
    • (more advanced) Patrick Billingsley, Probability and Measure, 3rd edition, Wiley, 1995.


    Mediaspace channel for the course (please note that these videos were made for a previous version of the course taught by Olivier Lévêque: there will be some differences with this year's version)

    Recordings of live lectures (from 2023, Olivier Lévêque)

►Week 1 (September 10-11)