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Tutorials

The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

Tutorial proposals are accepted until:

May 26, 2020


If you wish to propose a new Tutorial please kindly fill out and submit this Expression of Interest form.



Tutorial on
AI and Machine Learning for Computer Vision Applications


Instructor

Sos S. Agaian
College of Staten Island, CUNY
United States
 
Abstract

Artificial intelligence (AI) is a research field that studies how to realize intelligent human behaviors on a computer. The fundamental goal of AI is to make a computer that can learn, plan, and solve problems independently. This course aims to give an overview of some basic AI algorithms and an understanding of the possibilities and limitations of AI. This is an introductory course on artificial intelligence. It emphasizes fast and smart search heuristics, thoughtful ways to represent knowledge, and incisive techniques that support rational decision-making. It will also summarize various development resources that can enable researchers and practitioners to quickly get started on deep learning design. Application areas will include computer vision and robotics.

Keywords

AI and Machine Learning, Computer Vision, Robotics.

Aims and Learning Objectives

The main purpose of this course is to provide the most fundamental knowledge to the students so that they can understand what the AI is and how to use it in image processing applications.
• To become familiar with Artificial Intelligence concepts and applications.
• To examine how to fit data to models.
• To understand how machine learning and describe the specifics of several prominent machine-learning methods (e.g., SVMs, Regression, and others).
• To understand the basics of deep learning, how it is applied to various applications.
• To discuss various research solutions for improving current AI algorithms.
• To gain hands-on experience in building, and improving the performance of AI and Machine Learning methods tailoring robotics and vision application.
• To explore new emerging solutions that are opening up new research and commercial opportunities in current and future applications.

Target Audience

Engineers, scientists, students, and managers interested in acquiring a broad understanding of image processing.

Prerequisite Knowledge of Audience

Engineers, scientists, students, startups, investors/venture,and managers interested in acquiring a broad understanding of Artificial Intelligence.

Detailed Outline

• Artificial Intelligence concepts and applications.
• Fit data to models machine learning and describe the specifics of several prominent machine-learning methods (e.g., SVMs, Regression, and others).
• The basics of deep learning, how it is applied to various applications, various research solutions for improving current AI algorithms.
• Experience in building, and AI and Machine Learning methods tailoring robotics and vision application;
• New emerging solutions that are opening up new research and commercial opportunities in current and future applications.






















Secretariat Contacts
e-mail: delta.secretariat@insticc.org

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