International Bachelor of Science
in Artificial IntelligenceUniversité PSL · Paris School of AIThe first year builds the mathematical and computational foundations everything else rests on: algebra, analysis, probability and statistics, and programming, taught as full-year sequences with lectures and tutorials every week. Alongside them, a first look at what artificial intelligence and machine learning actually are, an introduction to database management, and the economics and law that shape how AI is used.
The year opens with Math in Practice, an intensive week that brings a cohort arriving from many different school systems to a common footing in calculus and mathematical writing.
9 courses · 30 ECTS · 360 contact hours in the timetabled courses · 1 optional course besides
Taught by Maja Ivkovic and Lucas Gnecco Heredia.
An intensive week held before the semester proper: calculus revision, and an introduction to mathematical reasoning and writing – how a proof is built, and how it is written down so that someone else can check it.
Taught by Lucas Gnecco Heredia, Alice Lacan and Changhong Wang.
Linear algebra from the ground up: systems of linear equations and Gaussian elimination, matrices, vector spaces and linear maps, determinants, orthogonality, eigenvalues, and diagonalization, with complete, rigorous proofs throughout. The language in which almost every machine learning method is eventually written.
Taught by Maja Ivkovic, Nat Mathews and Jonathan Chirinos Rodríguez.
A rigorous introduction to real analysis: the real numbers and their completeness, sequences and series, limits and continuity, and differentiation up to Taylor’s theorem, with the proofs that make them reliable.
Taught by Pierre Senellart, Changhong Wang, Angelo Saadeh and Panagiotis Patsilinakos.
Programming from first principles, in Python and C, with a fragment of C++: how a machine represents data and manages memory, structured and object-oriented programming, the common data structures and algorithmic techniques, and how to measure the complexity of an algorithm – together with the habits of the working programmer: testing, debugging, version control. Weekly hands-on sessions on the students’ own laptops; no prior experience assumed.
Taught by Maja Ivkovic and Nat Mathews.
Probability from its axioms: counting, conditional probability and independence, discrete and continuous random variables, expectation and variance, joint distributions, the law of large numbers, and the central limit theorem.
Taught by Angelo Saadeh.
What the field covers and where it came from: search and optimization, logic and reasoning, statistical and deep learning, transformers, generative and agentic systems, and the ethical and societal questions they raise – with an honest account of what these systems can and cannot do.
Taught by Edgar Jimenez Bedolla.
A first introduction to microeconomics, each session starting from a real problem: decisions under scarcity, strategic interaction and cooperation, how institutions and bargaining power decide who gets what, firms and markets, and market failures and the policies that address them. No prior economics is assumed.
Writing and speaking about scientific work in English: reading a paper, giving a talk, writing clearly about technical material.
A weekly sports slot, chosen from the disciplines PSL Sport offers across the campus.
An optional language course taken at the language centre of PSL: French as a foreign language, or another language. It is not compulsory and carries no credit.
9 courses · 30 ECTS · 357 contact hours in the timetabled courses · 1 optional course besides
Taught by Lucas Gnecco Heredia and Maxime Ferrer.
A second course in linear algebra: Euclidean and Hermitian spaces, orthogonality and least squares, the spectral theorem, quadratic forms and positive definite matrices, the matrix decompositions that data analysis is built on (QR, spectral, singular value), the discrete Fourier transform, and the Jordan normal form.
Taught by Maja Ivkovic.
The Riemann integral, sequences and series of functions and their uniform convergence, and a first look at topology: open, closed, and compact sets, and metric spaces.
Taught by Alice Lacan, Jonathan Chirinos Rodríguez and Lucas Pinol.
Estimation, confidence intervals, and hypothesis testing; maximum likelihood; linear regression. What a statistic actually tells you about the population it came from.
Taught by Anna Mosolova and Félix Chavelli.
Algorithms and advanced data structures – search trees, hash tables, graphs and the algorithms on them – and how to reason about the cost of a program before running it, with a first look at numerical methods. Lab sessions in Python and C++, and a group project, build fluency at writing code that works.
Taught by Paul Boniol and Roberto Stanzione.
How large collections of data are modelled, stored and queried: the relational model, relational algebra and SQL, schema design and normalization, and what happens inside the system (indexing, query optimization, transactions), with an eye on the datasets machine learning is trained on. It closes with a short introduction to putting a database behind a web page: the elements of HTML, CSS, and JavaScript, and a small application in Flask.
Taught by Lucas Gnecco Heredia.
An introduction to machine learning that puts method first: framing a problem, baselines, train, validation and test sets, cross-validation, overfitting and regularization, studied at length on regression; then classification with logistic regression, nearest neighbours, decision trees and random forests, and dimensionality reduction with PCA. Hands-on mini-projects throughout, and a final project.
Taught by Zichuan Li and Edgar Jimenez Bedolla.
An introduction to macroeconomics: how output, employment, and inflation are measured and determined, business cycles and economic shocks, fiscal and monetary policy, growth, crises, and inequality.
Taught by Ayşe Gizem Yaşar.
The legal framework technology operates under: personal data and privacy, intellectual property, platform and competition regulation, and the European rules that now apply to artificial intelligence.
A second semester of scientific communication, built around reading and presenting research articles, largely in the students’ own time.
An optional language course taken at the language centre of PSL: French as a foreign language, or another language. It is not compulsory and carries no credit.
Course descriptions are written and maintained by the teaching team. Contact hours are hours of class per student; credits follow the European ECTS scale, 60 of them per year.
The bachelor is part of the PR[AI]RIE – PSAI artificial intelligence cluster, supported by the French State under France 2030 through the Agence nationale de la recherche (ANR-23-IACL-0008).