Summer School on Deep Learning and Bayesian Methods
Мероприятие завершено
At Deep|Bayes summer school, we will discuss how Bayesian Methods can be combined with Deep Learning and lead to better results in machine learning applications. Recent research has proven that the use of Bayesian approach can be beneficial in various ways. School participants will learn methods and techniques that are crucial for understanding current research in machine learning. They will also have an opportunity to draw connections between Bayesian Methods and Reinforcements Learning, learn modern stochastic optimization methods and regularization techniques for neural networks. Lectures will be followed by practical sessions.
Our summer school is intended for:
- Undergraduate students (preferably those who completed at least 2 years of study) and graduate students with strong mathematical background and decent knowledge of machine learning including deep learning
- Researchers and industry professionals working in machine learning or related areas, willing to expand their knowledge and skills
Participation prerequisites
- Strong knowledge of machine learning and familiarity with deep learning.
- Mathematics: proficiency in linear algebra and probability theory is highly desirable.
- Programming: Python and NumPy.
- English will be the language of Deep|Bayes 2018 summer school, so participants are expected to be comfortable with technical English.
Дата
1 августа
13:25
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