AlexisPortfolio

Academic Skills

Here you will find a list of several chosen topics amongst others that I acquired during my studies and/or by myself

Machine Learning

With courses such as Machine Learning 1 & 2, I have acquired valuable knowledge and practice in the field of ML covering topics such as Statistical learning, Linear regression & Classification, Neural networks, Kernel methods, Dimensionality reduction, Clustering methods, Ensemble methods, Graphical Models, Variational Inference ...

Deep Learning

This course consists in the study of multi-layered neural networks trained on big data. Advanced optimisation techniques (SGD, Nestorov’s Momentum, …) were covered as well as Convolutional and Reccurent Neural Networks, Generative models, Bayesian NN, Advanced temporal modelling, Deep Reinforcement Learning.

Natural Language Processing

In this topic, I’ve studied statistical approaches for NLP with tasks that involve hierarchical structures and/or hidden structures using supervised and unsupervised statistical learning algorithms. Morphological processing, Part-of-speech tagging, Neural language models and Machine translation were part of the topics we’ve covered

Mathematics

Several courses were covered in both my BSc and MSc such as Calculus, Linear Algebra, Discrete mathematics, Chaos and fractals, Mathematical modelling, Mathematical Simulation, Numerical Mathematics, Graph Theory and Linear Programming.

Software Engineering

In this topic, I’ve learned the basics of software design and project management concepts. I learned how to produce professional, reliable, and cost-efficient software that can be developed in a team, reused, maintained, further evolved. Covered concepts include design principles, design patterns, and requirement engineering.

Computer Vision

During this master, I’ve completed two courses on that topic including interest point detectors, descriptors, SIFT, HoG, visual information representation (vector space, statistical models, bag-of-words), dimension reduction (PCA, LDA and SVD), object detection and classification, object tracking (mean-shift, Kalman), and user interaction (active learning).