Introduction to Machine Learning and Deep Learning
Machine learning has become a rapidly evolving and expanding field, combining ideas from computer science, statistics, and optimization. Its primary focus is to develop algorithms that can identify patterns and regularities in data, allowing for accurate predictions on new observations. This exciting discipline has seen great success in various fields, including computer vision, natural language processing, and data science. Now, the techniques used in machine learning are making their way into the analysis of High Energy Physics data, enabling scientists to tackle more complex problems.
In a series of lectures, Michael Kagan, a Staff Scientist at SLAC National Accelerator Laboratory, will delve into the framework behind machine learning and discuss recent advancements in neural networks and deep learning. With a particular focus on the ATLAS Experiment and the study of the properties of the Higgs Boson at the LHC, Kagan combines his expertise in physics with his passion for developing and applying machine learning methods in high energy physics.
Machine learning, as a field, has evolved significantly. It builds upon concepts from various disciplines and leverages algorithms to uncover hidden patterns within data. By utilizing these patterns, scientists can make predictions on new data sets, opening doors to new discoveries and advancements in numerous fields. Recently, machine learning techniques have found success in computer vision, natural language processing, and data science, and they have even made their mark in particle physics.
Kagan’s lectures will provide a comprehensive understanding of the principles behind machine learning and delve into the recent developments in neural networks and deep learning. Neural networks, in particular, have gained attention due to their ability to effectively model complex relationships within data sets. Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers of interconnected nodes, allowing for more sophisticated analysis and prediction capabilities.
Michael Kagan brings a wealth of experience to these lectures. As a Staff Scientist at SLAC National Accelerator Laboratory, he has dedicated his research to the study of the properties of the Higgs Boson on the ATLAS Experiment at the LHC. His expertise in both high energy physics and machine learning enables him to apply advanced techniques to solve complex problems in his field. With a Ph.D. in physics from Harvard University and a B.S. in physics and mathematics from the University of Michigan, Kagan is well-equipped to present the latest advancements in machine learning and their applications in high energy physics.
In summary, machine learning has become a crucial tool for identifying patterns and making predictions on new observations. Kagan’s upcoming lectures will provide a comprehensive overview of machine learning principles and explore the latest advancements in neural networks and deep learning. By combining his expertise in high energy physics and machine learning, Kagan is poised to shed light on the exciting ways in which these fields intersect and open new possibilities for scientific discovery.
