The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.
This course is part of the Advanced Machine Learning Specialization
Offered By
Advanced Machine Learning SpecializationNational Research University Higher School of EconomicsAbout this Course
Offered by
National Research University Higher School of Economics
National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.
Syllabus - What you will learn from this course
Introduction into particle physics for data scientists
This module starts with a mild introduction into particle physics, and it explains basic notions, so you will understand the structure and the principal terms that physicists are using to describe the forces and particles that comprise the fundamental level of our universe. Also, we'll describe main stages of data collection and analysis that happens at LHC experiment. Each step is associated with specific machine learning challenges and some of which we are going to cover later. The final part of the module describes a very high-level example of data analysis that shows how simple data analysis techniques can be used for discovery of an elementary particle.
Particle identification
This module is about detectors in high energy physics. It describes several detector designs, different detector systems, how they work and what particle parameters they measure. Several cases in high energy physics where machine learning can be successfully applied are demonstrated.
Search for New Physics in Rare Decays
In this module, we explain how new physics search can be mediated through a search for rare processes. We describe the main steps physicists have to follow to find rare decay. At first search for such phenomena may look like a perfect task for machine learning algorithms. However, there are several constraints that one have to keep in mind during training and application of a classifier.
Search for Dark Matter Hints with Machine Learning at new CERN experiment
We start this module with explanation what Dark Matter phenomenon is about and what are the general strategies for Dark Matter search. Then we boil down the topic towards one of the CERN proposed experiments - SHiP. Given the design of the experiment, we consider the signatures that Dark Matter particles may produce. Of course, Machine Learning algorithms can be applied to discriminate such signatures from the background. We'll see how clustering algorithms can improve the signal visibility even further.
Reviews
TOP REVIEWS FROM ADDRESSING LARGE HADRON COLLIDER CHALLENGES BY MACHINE LEARNING
A challenging ML course for practitioners and researchers to put their abilities to the test. Could have enjoyed a bit more (possibly optional) explanation about the underlying physics.
nice starting point for graduate students or senior undergraduate students who want to dig deeper in this direction
About the Advanced Machine Learning Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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