Topic outline

  • Course program

    • Monday 6th 8:30 to 11:45 Algorithms and datastructures by Angelos Anadiotis
    • Monday 6th 13:30 to 16:45 Formal languages  by Fabian Suchanek
    • Tuesday 7th 8:30 to 11:45 Computer usage  by Louis Jachiet
    • Tuesday 7th 13:30 to 16:45 Statistics and probability by Tiphaine Viard
    • Wednesday 8th 13:30 to 16:45 Introduction to Logic by Jean-Louis Dessalles
    • Friday, all day (8:30 to 11:45 and 13:30 to 16:45) lab exercises
    From Monday to Wednesday the course will happen in Amphi 2, accessible from the entrance as shown here:
    Going from fountain to Amphi 2

    There will be an online version. By default the teaching will happen at the following zoom (but check this page before each course as some teachers might us different tools / links) :

    • Algorithms and datastructures

      This class will do a review over a set of standard data structures and algorithms that are often met in data science workloads. It will include a lecture and a practice part and will cover topics such as basic APIs, tree and sorting algorithms as well as string matching. During practice, we will combine the knowledge of the lectures into practical problems.

    • Formal languages

      We discuss the Chomsky hierarchy of formal grammars, Turing machines, regular expressions, and decidability.

    • Computer use


      This class will tackle three broad topics to help you be more efficient in using a computer especially as a computer scientist:
      • Digital life (mails, calendars, backups, online presence, security)
      • Tools for computer scientists (LaTeX, Git, Zotero, Inkscape, Jupyter notebooks...)
      • Good practices for programming (tests, comments, virtualenv, etc.)


      More exercises (if you have time)

      • Create a repository on Télécom's gitlab (use the sign-in with Shibboleth and use your Télécom's account
      • Install a password manager (e.g. KeepassX)
      • Start doing backups
      • Make sure your disk is encrypted (if not consider doing it someday)
      • Write yourself a résumé using LaTeX (see for templates
      • Create yourself a personal webpage
    • Statistics and probability

      We will cover some basics of statistics and probabilities for machine learning and data science, such as:

      • How to treat new data,
      • How to get a descriptive overview of it
      • Probabilistic intuitions and the Bayes theorem
      • Underfitting, overfitting and the bias variance trade-off
      • Real-world case studies

      Machine learning systems and real-world datasets can fool us without a grounded understanding of these basic concepts, and so we will give particular attention to describe and identify the ways this can be avoided.

      There is no prerequisite beyond high school mathematics.

    • Introduction to Logic

      Introduction to propositional and first-order logic

      • Propositional logic (syntax, valuations, truth tables, proof, satisfaction)
      • Predicate logic (syntax, semantics, models, validity, proof, completeness)
    • Lab exercises

      Do the exercises of each of part of the class (in the order that you prefer).