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Ilyas WADJINNY

CERGY

Electionsmunicipales 2026

RETROUVEZ GRATUITEMENTRésultat municipales à Cergy
Résultat municipales dans le Val-d'Oise

En résumé

Je m'appelle Ilyas Wadjinny, étudiant à l'école CY Tech en 3ème année du cycle ingénieur en Mathématiques appliquées et Informatique - Spécialité Data Science.
J'ai des compétences solides en machine learning et développement python que j'ai acquis à travers des projets personnels et durant mes deux années d'alternance en data science chez EvidenceB dont le métier est de créer des outils e-learning renforcé par le machine-learning, afin d'offrir un suivi personnalisé aux élèves. Pendant ces deux années j'ai travaillé sur plusieurs projets, dont la création d'un outil pour simuler le comportement des élèves afin de tester le modèle machine learning et le développement d'un moteur d'intelligence artificielle pour classifier et estimer le niveau de chaque élève.
Ci-dessus quelques projets personnels de machine learning et de développement python/R:
https://github.com/Wadjinny/pygame_kmeans
https://github.com/Wadjinny/Hapiness_Score_Prediction_Linear_Model

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Formations

  • EISTI - CY TECH

    Cergy 2020 - maintenant M.Eng. Coursework:
    AI Ethics: Roboethics, Philosophy of AI, AI and Descrimination.
    Work Ethics: Transparency, Communication, Work Attitude, Cooperation, Organisation, Efficiency, Respect, Teamwork.
    Big Data & Cloud Computing: Database, Cloud, Storage, Compression, Memory Complexity, NoSQL, AWS.
    Data Science Frameworks: Matplotlib, Seaborn, Pandas, Sckit-learn, Scipy, Ploty, Spacy.
    Web Development: HTML, CSS, JavaScript, PHP, SQL.
    Data Gathering: Web Scraping, Scrapy, BeautifulSoup, Selenium.
    Time Series Forecasting: R Programming Language, Multiple Linear Regression, Classical Models for Time Series, Long Short-term Memory (LSTM), Efficiency Computing.
    Bioinformatics: Sequence Analysis, Gene and Protein Expression, Analysis of Cellular Organization, Structural Bioinformatics, Network and System Biology, Bio-inspired Algorithms.
    Deep Learning & Reinforcement Learning: CNNs, LSTMs, RNNs, GANs, RBFNs, SOMs, Autoencoders, Applications with Tensorflow.
    GPU-TPU Programming & Parallel Computing: OpenMP for C, CUDA.
    AI based Image Processing: Intorduction to Computer Vision, Signal Processing & Filters, OpenCV, Pillow, Introduction to PyTorch for Image processing.
    Quantum Computing: Cryptography, Quantum Supremacy, Quantum Algorithms.
    Natural Language Processing (NLP): Text and Speech Processing, Semantic Analysis, Morphological Analysis, Statistical Methods for NLP.
    Metaheuristic Optimization: Search Algorithms, Parallel Metaheuristics, Nature-inspired Metaheuristics.
    AI & Cybersecurity: Threat Exposure, Controls Effectiveness, Incidence Response, Fraud Detection, Breach Risk Prediction.
    Reactive Programming: TypeSafe Stack, Scala, Play, Akka, Responsive Reactive Programming, Elastic Reactive Programming, Resilient Reactive Programming, Message Driven Reactive Programming.
    Portfolio Management: Investment Anlaysis, Modern Portfolio Theory, Capital Asset Pricing, Investment Banking, Investment Model, Applications using R.
    Microeconomics: Microeconomic Theory, Microeconomic Models, Market Structure, Game Theory.
    Macroeconomics: Macroeconomic Models, Basic Macroeconomic Concepts, Macroeconomic Policy, Money Market.
    Entrepreneurship & Product Management: Entrepreneurial Behaviours, Ressources & Financing, Market & Customer Research, Competitive Intelligence, Industry Analysis, Trends.
    Intercultural Communication: Social Engineering, Verbal & Nonverbal Comuunication, Authentic Intercultural Communication, History of Assimilation, Cross-cultural Business Startegies, Globalization, Cultural Perceprtion.
    Corporate Law: Corporate Structure, Corporate Finance, Corporate Governance & Balance of Power, Litigation.
    Research Methodology: Data Gathering, Qualitative and Quatitative Analysis Methods, Bibliography, Reporting & Presenting.
    BSc. Coursework:
    Real and Complex Analysis: Complex-valued Functions, Analytic Functions, Holomorphic Functions, Cauchy-Riemann Equations, Fourier Analysis, Formal Power Series.
    Advanced Calculus: Infenitesimal Calculus, Limits & Derivatives, Differential Calculus, Integral Calculus, Smooth Infenitesimal Calculus, Advanced Measures.
    Optimization:Standard Form, Slack Form, Duality, Variations, Classic Algorithms, Solvers.
    Graph Theory: Enumeration, Subgraphs, Coloring, Route Problems, Network Flow, Visibility Problems, Covering Problems, Decomposition Problems, Graph Classes, Algorithms.
    Differential Equations: Ordinary Differential Equations, Partial Differential Equations, Non-linear Differencial Equations.
    Probability Theory: The Kolmogorov Axioms, Discrete Probability Distributions, Continuous Probability Distributions, Mesure-theoretic Probability Theory, Probability Application & Simulation.
    General Topology: Topological Spaces, Algebraic Structures, Topological Invariants, Topological Data Analysis.
    Linear Algebra: Vector Spaces, Matrices, Linear Systems, Endomorphisms & Square Matrices, Duality, Usage and Applications of Linear Algebra.
    Number Theory: Elementary Number Theory, Analytic Number Theory, Algebraic Number Theory, Arithmetic Combinatorics, Applications of Number Theory for Cryptography.
    Electromagnetism: Fundamental Forces, Classical Electromagnetism, Maxwell Equations, Wave Propagation, Nonlinear Phenomena.
    Thermodynamics: Classical Thermodynamics, Statistical Thermodynamics, Chemical Thermodynamics, Laws of Thermodynamics.
    Mechanics: Classical Mechanics, Quantum Mechanics, Introduction to Relativistic Mechanics.
    Optics: Optical Systems, Superposition & Interference, Diffraction & Optical Resolution, Dispertion, Polarization.
    Data Structures: Memory, Data Types, Usage & Implementation, Language Support.
    Numerical Methods: Direct & Iterative Methods, Discretization, Numerical Integration, Numerical Stability, Functions Values Computing, Interpolation & Extrapolation, Solving Equations and Systems of Equations.
    Monte Carlo Simulations: Integration, Stochastic Simulation, Inverse Problems, Markov Chains, Usage of Monte Carlo Methods.
    Object Oriented Programming: Features of OOP, Design Patterns, OOP Application with Java.

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