Neural networks and Computer Vision
Faculty Profiles
Sergey Nikolenko
Chief Research Officer, Neuromation Head of AI Lab, PDMI RAS
Alexey Davydov
Researcher at Steklov Math Institute, Researcher at Synthesis AI
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
Deep learning, i.e., training multilayered neural architectures, was one of the oldest tools in machine learning but has revolutionized the industry over the last decade. In this course, we begin with the fundamentals of deep learning and then proceed to modern architectures related to basic computer vision problems: image classification, object detection, segmentation, and others.
Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures, while at the same time staying focused and not going into too wide a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localization and mapping, and scene understanding, which are all key problems for creating industrial and home robots.
The course is supported by Neuromation and features practical assignments done over the Neuromation platform.
Learning highlights
- Learn to apply Deep Learning techniques in practice
- Understand the theory behind the Deep Learning from basics to state-of-the-art approaches
- Learn how to train various deep neural architectures
- Understand a wide variety of neural architectures suited for real-life computer vision problems
- Gain essential experience with main Deep Learning frameworks
Course outline
6 classes
Session 1
Neural network basics.
Session 2
Feedforward neural networks
Session 3
Optimization in neural networks
Session 4
Regularisation in neural networks
Session 5
Weight initialisation and batchnorm
Session 6
Convolutional neural networks I
Prerequisites
This course is one of three in a wholistic series.
Students that have already taken MSL-111 and those with prior experience with HTML, CSS, and Javascript building simple web pages will be good candidates for this module.
Sergey Nikolenko is a computer scientist with vast experience in machine learning and data analysis, algorithms design and analysis, theoretical computer science, and algebra. He graduated from St. Petersburg State University in 2005, majoring in algebra (Chevalley groups), and earned his Ph.D at the Steklov Mathematical Institute at St. Petersburg in 2009 in theoretical computer science (circuit complexity and theoretical cryptography). Since then, Sergey has been interested in machine learning and probabilistic modeling, producing theoretical results and working on practical projects for the industry.
Sergey Nikolenko is currently serving as the Chief Research Officer at Neuromation, leading the Artificial Intelligence Lab at the Steklov Mathematical Institute at St. Petersburg, and teaching at the St. Petersburg State University and Higher School of Economics. Dr. Nikolenko has published more than 170 research papers on machine learning (ICML, CVPR, ACL, SIGIR, WSDM...), analysis of algorithms (SIGCOMM, INFOCOM, ICNP…), and other fields, several books, including a bestselling “Deep Learning” book (in Russian), lecture courses in ML, DL, other fields of computer science (St. Petersburg State University, NRU Higher School of Economics...) and much more. He has extensive experience in managing research and industrial AI/ML projects.
See full profileAlexey Davydov is a computer scientist experienced with algorithm design and machine learning. He received his bachelor degree in physics at Moscow Institute of Physics and Technology and his master degree at St. Petersburg Academic University. His main research interests are developing of competitive scheduling algorithms and usage of synthetic data in deep learning.
He has been teaching at St. Petersburg Academic University, Computer Science Center and St. Petersburg State University since 2012. Alex Davydov currently is a researcher at Steklov Math Institute where he works on theoretical research and at Neuromation where he can apply it to practice.
See full profileApply for this course
Neural networks and Computer Vision
by Sergey Nikolenko, Alexey Davydov
Total hours
45 Hours
Dates
May 18 - Jun 05, 2020
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.