Hours of lectures: 39.

Featured courses:

• Deep Generative Models by Aaron Courville, University of Montréal, Canada.

• Dive into Deep Learning by Alex Smola, Amazon, USA.

• Mathematics of Deep Learning, Rene Vidal, Johns Hopkins University, USA.

Accomplishment: 19 French courses from Newcomer to Advanced Course based on level B2-C1 of the Common European Framework of Reference for Languages (CEFR).

Description: Learn French online help you to strengthen your French skills, whatever your motivation to learn. Select a guided learning path based on your skill level, or choose a work-, travel- or culture-focused course.

Description: The Research School on Statistics and Data Science 2019 (RSSDS2019) provides a series of lectures and posters delivered by experienced academic and industry members on the cutting edge statistics and data science applications and theory. Aside from being considered among the most desirable jobs of the 21st century, the real world value of data science and skilled data scientists cannot be understated. The most successful data science professionals are those who can hone the power of statistics, programming, domain knowledge and data visualisation. The workshops at RSSDS2019 will empower current and upcoming data scientists (and enthusiasts) with new knowledge, understanding and exploration into many varied and interesting data domains, alongside the opportunity to network with many others involved at various levels in the exciting data science scene.

Niveau acquis: A1+ selon le Cadre européen commun de référence pour les langues (CECR).

Description: Le Soutien linguistique en français est un programme semestriel de cours du soir d’apprentissage de la langue française et de soutien linguistique en français sur objectifs universitaires. Il est principalement proposé aux étudiants et enseignants-chercheurs inscrits à l’université de Caen Normandie en complément de leurs formations disciplinaires et ou de leurs recherches en laboratoire. Le programme est également ouvert aux particuliers dont les activités professionnelles et ou la situation familiale ne seraient pas compatibles avec les emplois du temps de nos formations intensives de type DUEF. Les stagiaires doivent être titulaires d’un diplôme ou titre donnant accès à l’enseignement supérieur en France.

Advisor: Professor Faïcel Chamroukhi (Université de Caen Normandie).

Co-advisor: Senior Lecturer Hien Duy Nguyen (University of Queensland).

Thesis title: Model selection and approximation in high-dimensional mixtures of experts models$:$ from theory to practice.

Manuscript: PDF.

Slide: PDF.

Video: Youtube.

Code: GitHub.

Instructor: Andrew Ng. (Stanford University)

There are 5 Courses in this Specialization:

• Neural Networks and Deep Learning (Grade: 100%).

• Improving Deep Neural Networks: Hyperparameter tuning (Grade: 100%).

• Regularization and Optimization, Structuring Machine Learning Projects (Grade: 98.3%).

• Convolutional Neural Networks (Grade: 98.9%).

• Sequence Models (Grade: 100%).

Description: The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Master 2 Program of Mathematics. Major in Applied Mathematics.

GPA: $18.0/20.0$.

Mention: Très Bien.

Overall Band Score: $7.0/9.0$.

CEFR Level: C1.

Description: IELTS – the International English Language Testing System – is the world’s most popular English language test. It is developed by some of the world’s leading experts in language assessment and evaluates all of your English skills — reading, writing, listening and speaking. The test reflects how you’ll use English to study, work and live in an English speaking environment. You can take the test at any of our official test centres across the world.

Total Score: $795/990$ (Listening: $410/495$ + Reading: $385/495$).

Description: The TOEIC Listening and Reading Test measures listening and reading skills for beginner to advanced levels of English.

Instructor: Andrew Ng. (Stanford University).

Description: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Topics include:

(i) Supervised learning (parametric or non-parametric algorithms, support vector machines, kernels, neural networks).

(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Honors Program in Mathematics and Computer Science. Major in Probability and Statistics, minor in Numerical Analysis.

GPA: $9.17/10.0$.

Rank: $2/1557$. Summa Cum Laude.