Addressing Large Hadron Collider Challenges by Machine Learning
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
Created by: Andrei Ustyuzhanin
Quality Score
Overall Score : 88 / 100
Course Description
Instructor Details
- 4.4 Rating
5 Reviews
Andrei Ustyuzhanin
Dr. Andrey Ustyuzhanin - the head of Yandex-CERN joint projects as well as the head of Laboratory of Methods for Big Data Analysis at NRU HSE. His team is the member of frontier research international collaborations: LHCb - collaboration at Large Hadron Collider, SHiP (Search for Hidden Particles) - experiment being designed for the New Physics discovery. His group is unique for both collaborations, since majority of the team members are coming from the Computer and Data Science worlds. The major priority of his research is the design of new Machine Learning methods and using them to solve tough scientific enigmas thus improving the fundamental understanding of our world. Discovering the deeper truth about the Universe by applying data analysis methods is the major source of inspiration in his lifelong journey. Andrey is co-author of the course on the Machine Learning applied to the High Energy Physics at Yandex School of Data Analysis and organizes annual international summer schools following the similar set of topics.
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