“Deep Learning” is an advanced computational learning strategy concerned with techniques and methodologies inspired by the function of the human brain. Deep Learning is a type of neural network whose usage has taken off over the past few years. Now, many scientific and industrial disciplines use this approach to discover hidden patterns and facts from massive and diverse data sources. Deep Learning is very promising in a wide variety of applications ranging from object tagging and speech recognition to disease diagnosis and treatment.
This workshop presents deep learning from a computational perspective. The currently available state-of-the-art deep learning components are introduced, deep learning strategies (both what they do and how they work) are elaborated and some real world deep learning applications will be given. This 2-days jump start workshop is designed to introduce you to the skills needed to start your journey as a deep learning developer. This workshop is designed in a series of hands-on and cutting-edge development in this field, and it is targeted for developers and data scientists who are looking to build AI powered solutions in real-world applications.
All engineers in different disciplines, especially computer, electrical and mechanical engineers can easily follow the workshop. Senior engineering student at bachelor’s level can also follow the content. Masters and Ph.D. students and graduates who are willing to further expand their knowledge in applied robotics, and deep learning methods can mostly benefit from this jump start workshop. It is intended that the potential developers and data scientists who are looking to build AI powered solutions in robotics application get the most out of this workshop.
|Robotics Research at K. N. Toosi University of Technology|
|1||Advanced Robotics and Automated Systems||15||Prof. Taghirad||ARAS Research themes|
|2||Surgical Robotics||40||Prof. Taghirad|
|3||Parallel and Cable Robotics||40||Prof. Taghirad|
|4||Dynamical Systems and Control||30||Prof. Taghirad|
|5||Autonomous Robotics||60||Dr. Norouzzadeh|
|Deep Learning on Autonomous Vehicles: Introduction and Preliminaries|
|1||Introduction to Self-Driving Cars||15||Dr.Norouzzadeh||Introduction
to Theory and Preliminar-ies
|2||Introduction to Computer Vision||15||Dr.Norouzzadeh|
|3||Introduction to Deep Learning||15||Dr.Norouzzadeh|
|4||Lane Line Detection||45||Dr.Norouzzadeh|
|5||Advanced Lane Line Detection||60||Dr.Norouzzadeh|
|6||Algorithm Test in Challenging Environment||30||Dr.Norouzzadeh|
|Deep Learning on Autonomous Vehicles: Methods and Applications|
|1||Traffic Sign Classification||45||Dr.Norouzzadeh||Hand-on Techniques|
|2||Autonomous Car Driving Simulator||40||Dr.Norouzzadeh|
|5||Vehicle and Pedestrian Tracking||60||Dr.Norouzzadeh|
|6||Quadrotors & UGV: From Design to implementation||45||Prof. Taghirad||Developed Robots at ARAS|
Prof. Hamid D. Taghirad has received his B.Sc. degree in mechanical engineering from Sharif University of Technology, Tehran, Iran, in 1989, his M.Sc. in mechanical engineering in 1993, and his Ph.D. in electrical engineering in 1997, both from McGill University, Montreal, Canada. He is currently the University Vice-Chancellor for Global strategies and International Affairs, Professor and the Director of the Advanced Robotics and Automated System (ARAS), Department of Systems and Control, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran. He is a senior member of IEEE, and Editorial board of International Journal of Robotics: Theory and Application, and International Journal of Advanced Robotic Systems. His research interest is robust and nonlinear control applied to robotic systems. His publications include five books, and more than 250 papers in international Journals and conference proceedings.
Dr. Alireza Norouzzadh Ravari received the B.Sc. degree in electrical engineering from Shahid Bahonar University of Kerman, Kerman, Iran, his M.Sc. in electrical engineering from K. N. Toosi University of Technology, Tehran, Iran, and his Ph.D. degree in control engineering at K. N. Toosi University. He is currently a member of the Advanced Robotics and Automated System at K. N. Toosi University of Technology. His current research interests include machine vision, mobile robotics and deep learning theory.
Period: 28-31 August
Location: K. N. Toosi University of Technology
Eligibility: Graduated, Professors, …
Course Language: English
Organized by: Faculty of Electrical Engineering
- N. Toosi University of Technology
- Office of vice-chancellor for Global Strategies and International affairs
In case of Further Question please contact:
The fee includes:
- educational courses
- accommodation in a hotel
- flight costs
- Trip to Qom
- Airport pick-up
- several social activities