Topic outline

Class
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.)
Exercises
 Clone the following repository and create a short presentation
 Read the pep8 and read and do some of the exercises here
More exercises (if you have time)
 Create a repository on Télécom's gitlab (use the signin 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 https://www.overleaf.com/latex/templates/tagged/cv for templates
 Create yourself a personal webpage
 Digital life (mails, calendars, backups, online presence, security)

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 tradeoff
 Realworld case studies
Machine learning systems and realworld 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 propositional and firstorder logic
 Propositional logic (syntax, valuations, truth tables, proof, satisfaction)
 Predicate logic (syntax, semantics, models, validity, proof, completeness)