I am Warith Harchaoui, passionate about artificial intelligence (AI) in both research and companies and I hold a Ph.D. in Applied Mathematics.
I began in 2008 my never-ending learning process with the best AI scientists thanks to the École Normale Supérieure de Cachan (MVA M.Sc.). After several experiences in the field of Computer Vision in startups and worldwide companies, I pursued my research endeavor at the École Normale Supérieure de Paris at the Willow / Sierra laboratories which are ranked among the best in the world.
Later, I reinforced the corporate component of his career in Data Science within the e-commerce world leader Oscaro.com for selling car parts from 2014 to 2020. While keeping my operational responsibilities, I accomplished my Ph.D. in Applied Mathematics from 2016 to 2020 with Charles Bouveyron at the MAP5 Lab of Université de Paris. Today, thanks to Jellysmack as Research Fellow, my expertise in image, sound, video and text processing encourages me to embrace my dreams: bringing ideas from artificial intelligence to the real world.
To the best of my efforts, I believe in the idea that Artificial Intelligence (AI) should be understood as a revolution comparable to agriculture 10,000 years before Christ, the invention of writing 3,000 years before Christ or even printing in the 15th century.
By AI, I mean the widest definition: Mathematics applied with computers known as Statistical Learning, Pattern Recognition, Machine Learning, Data Science and even Signal Processing for various media such as sound, image, video, text and even tabular data. The tangible impact of AI profoundly changes the relationship between our minds and the world.
For companies, almost all areas of our contemporary world are now impacted by this Science.
Philosophically, AI is the automated emergence of certain natural intelligence aspects such as learning, decision making, adaptation, prediction, imitation, content production etc. thanks to some Applied Mathematics with computers. Concretely, Artificial Intelligence is the science that allows a machine to execute tasks without exhaustively enumerating scenarios by human intervention. AI is the natural extension of automation from matter to information.
In practice, my ambition is translated into accomplishments towards this inevitable milestone for mankind by maintaining close relationships with academia such as MAP5, Université de Paris, INRIA Masaai, Executive MBA of Rennes School of Business, think tank 4th Revolution and companies like Jellysmack but also thanks to operational consulting as entrepreneur with Ircam Amplify for music analysis and monetization and VizioSense for embedded Computer Vision.
Throughout my professional experience, I have come to understand the importance of fostering a collaborative work environment where all voices and ideas are heard. A project with a team, colleagues and collaborators that do not get along well always ends up paying the price. This is why I collect feedbacks from my collaborators and colleagues to improve my work and my team's work.
In the rapidly advancing field of Artificial Intelligence, it is common to observe high volume and pace of both scientific and non-scientific publications. However, it is fortunate that experienced scientists do take the time to write comprehensive books that provide valuable insights and surveys of the field. In addition to the concise format of publications at top AI conferences, it is beneficial to delve deeper into the mathematical and algorithmic complexities of plain books in order to both understand the shorter works and effectively utilize the various toolkits available online. It is in this context that I present a list of books that I find particularly noteworthy, along with some comments, for those readers who are eager to engage in the exciting “AI adventure”.
I did not commented all the books I like so far yet. Indeed, it is difficult for me to comment books of people I admire in a way that is useful for the readers, so it takes me time.
Machine Learning is the science of learning from data, which is experience gained by analyzing data instead of explicit programming. This is done by using computer-based algorithms that analyze input data, identify patterns using statistical techniques and make decisions or predictions. As a result, the machine can adapt to new and unprecedented data and make more accurate predictions.
Pattern Recognition and Machine Learning
Christopher M. Bishop, 2006,
Machine Learning: A Probabilistic Perspective
Kevin Murphy, 2012,
Bayesian Reasoning and Machine Learning
David Barber, 2012,
Computer vision is an area of artificial intelligence that aims to replicate the capabilities of human vision by teaching computers to interpret and comprehend the visual environment in a similar way to humans. This field is applied to a variety of tasks, such as facial recognition, object detection, autonomous driving, and medical imaging.
Computer Vision: Algorithms and Applications, 2nd Edition
Richard Szeliski, 2022,
Computer Vision: A Modern Approach, 2nd Edition
David Forsyth and Jean Ponce, 2011,
Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman, 2004,
Natural Language Processing (NLP) allows computers to interpret and comprehend human language. This is achieved through the use of algorithms and software that analyze large amounts of data and extract the meaning of text, enabling computers to understand language in a similar way to humans. NLP is applied in various contexts, including search engine optimization, automatic summarization, sentiment analysis, and natural language generation.
Neural Network Methods in Natural Language Processing
Yoav Goldberg, 2017,
Foundations of Statistical Natural Language Processing
Chris Manning and Hinrich Schütze, 1999,
Signal processing deals with the manipulation, analysis, and transformation of signals. These signals may include sound waves, radio waves, images, and data from medical instruments. Signal processing is applied in a variety of contexts, including audio enhancement, data analysis in medical imaging, and more. Essentially, signal processing involves extracting useful information from signals such as frequency, amplitude, or color, or modifying to make it crisper.
A Wavelet Tour of Signal Processing, 3rd Edition
Stéphane Mallat, 2008,
Reinforcement Learning (RL) is about making machines learn from their environment and perform actions that maximize rewards. To do this, the computer is given a numerical goal or objective, and then given feedback after each action it performs in the form of punishment and reward, also numerical. The computer then adjusts its actions according to this feedback, learning from its mistakes and optimizing its behavior over time. Reinforcement learning is applied in a variety of fields, including games, robotics and autonomous vehicles. In general terms, reinforcement learning is the process of teaching a computer to perform the most efficient actions in a given environment in order to maximize the rewards
Reinforcement Learning, 2nd Edition
Richard S. Sutton and Andrew G. Barto, 2018,
Algorithms are sets of instructions for solving problems in a systematic way. Optimization is the process of identifying the most efficient way to solve a problem. In essence, algorithms and optimization can be viewed as sister techniques for improving efficiency. Algorithms and optimization are the cornerstones of artificial intelligence to adjust model parameters to fit the data. Mastering algorithms and optimization techniques provides the theoretical, but more importantly practical, tools to create new ones and adapt old ones to meet the specificity of your real-world problems.
Introduction to Algorithms
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, 2009,
Stephen Boyd and Lieven Vandenberghe, 2004,
Numerical Optimization, 2nd Edition
J. Frédéric Bonnans, J. Charles Gilbert, Claude Lemaréchal and Claudia A. Sagastizábal, 2006,
Numerical Recipes, 3rd Edition
William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery, 2007,
Computational Optimal Transport
Gabriel Peyré and Marco Cuturi, 2019,
Embracing both the corporate and academic worlds in the field of artificial intelligence is probably the best conscious choice in my career so far. Industry and academia are two very different worlds, but they are also complementary in my experience. Sometimes the line gets blurred in institutions like OpenAI or trillion dollar companies like Microsoft Research, Baidu Research, Amazon Research or Google Research (not an exhaustive list and in random order) that contribute with groundbreaking papers and OpenSource toolkits like excellent scholars in top-level universities would.
To be honest, starting from an industrial problem allows me to limit some scientific ramblings (which I like so much because they are a source of creativity in disguise!) in favor of a greater impact on the real world... which justifies the efforts. In the end, I am rewarded by the satisfaction of making someone else's life easier, relieved from her/his original problem. So, in my own way, I try to contribute with all my might to this millennium dream of artificial intelligence. Luckily, I have been fortunate to continue to work in both academic and corporate environments with publications, teachings and prototypes / engines in production.
Artificial Intelligence for Business — 2nd Edition
Warith Harchaoui, Laurent Pantanacce and Nicolas Renard, December 2022, Rennes School of Business
Generalised Mutual Information for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui, Arnaud Droit, Mickaël Leclercq and Frédéric Precioso, 2022, NEURIPS (ex-NIPS)
Artificial Intelligence for Business — 1st Edition
Warith Harchaoui, Laurent Pantanacce, March 2022, Rennes School of Business
Thoughts in 2021 about Hardware in Artificial Intelligence
Warith Harchaoui, November 2021,
True Story for a Rare Punctuation Mark
Warith Harchaoui, March 2021,
Learning Representations using Neural Networks and Optimal Transport (Ph.D.)
Warith Harchaoui, September 2016 to October 2020, MAP5 — Université Paris Descartes
My Ph.D. work was about artificial intelligence for:
Rencontre avec Luc Julia, l’IA n’existe pas !⎜ORLM-363
Luc Julia et Warith Harchaoui, February 2020, IUT de l'université de Paris
Wasserstein Adversarial Mixture Clustering (WAMiC) — Poster
Warith Harchaoui, Pierre-Alexandre Mattei, Andrés Almansa and Charles Bouveyron, Summer 2018, Data Science Summer School — École Polytechnique
Artificial Intelligence, Machine Learning, Computer Vision and Natural Language Processing with Python
Warith Harchaoui, Mohamed Chelali, Matias Tassano, Pierre-Louis Antonsanti and Azedine Mani, Last updated in December 2022 (since December 2018), MAP5 — Université Paris Descartes
Artificial Intelligence needs heavy computations. During the 2010s, the Deep Learning community paved the way of hardware acceleration by historically Graphics Processing Units (GPU) diverted from its original usage for the benefits of Applied Mathematics Research beyond Graphics.
The aim of this webpage is to present a cheat sheet for programming in Machine Learning
(i.e. Statistical Learning, Pattern Recognition, Artificial Intelligence, Data Science) for tremendous applications such as in Computer Vision, Sound Processing and Natural Language Processing.
This page has been extensively used at least in the MAP5 lab, Oscaro.com, Jellysmack for Applied Mathematics to conduct research in Machine Learning (ML), Computer Vision (CV) and Natural Language Processing (NLP) in Python. Please feel free to contact me (Warith Harchaoui, ) for improvements and suggestions.