Workshops

:: Workshops

Workshop	Presenter	Abstract	Goals	Duration (Hours)
Deep Learning	Pooya Mohammadi Kazaj	Deep learning is one of the most popular and widely used fields of artificial intelligence, which currently has the most occupations and fields of research. This field is composed of extensive sections, all of which are difficult to recognize and follow not only for newcomers, but also for experts at the same time. In this workshop, our goal is to cover different sections, review the advantages and disadvantages of different models and reasons for each model during It's time. Finally, the research and industrial areas of each section will be introduced and their libraries and implementation frameworks will be reviewed accordingly.	1. Introduction of artificial intelligence and the place of deep learning 2. Review and introducing of various areas of deep learning, including machine vision, natural language and sound processing, GAN models and deep reinforcement learning systems 3. Study of the evolution of models, libraries and frameworks 4. Investigating the future of the mentioned fields in industry and research sectors	12
Analyzing Fnancial Markets with the Help of Machine Learning	Abolfazl Tazari	Analysis of complex systems, such as financial markets, is a field of science that, due to its extreme complexity, cannot be analyzed with conventional tools, and for this reason, scientists have given attention to data science and machine learning. Reputable universities in the world conduct research in this field during their doctoral studies. In this workshop, we want to develop a machine learning-based system that analyzes financial markets. Reviewing the data mining process, feature extraction, preprocessing, feature selection, modeling, model evaluation, model aggregation and supermodel creation, etc. are among the features of this theoretical / practical workshop.	1. Using classification algorithms such as Artificial Neural Network, SVM, XGBOOST, Random Forest, etc. in Python language and scikit-learn package 2. Using regression algorithms in Python language and scikit-learn package 3. Practical acquaintance with how to assemble(Ensemble) the algorithms with Bagging, Boosting and Stacking methods 4. Familiarity with data analysis in global financial markets 5. Familiarity with algorithmic traiding and how to implement it	4
Ethereum Network and DApp Programming	Omid Boushehrian	Ethereum Network, As one of the most extensive peer-to-peer networks is becoming the most important platform for providing decentralized services due to the use of blockchain technology. This network because of the possibility of the setting up systems without the need for a central organization, data transparency, scalability, and the use of agreement mechanisms that make it almost impossible for an organization to manipulate the data is soon to be replaced the current web and introducing a new web called Web 3. This platform is currently used in various fields including: Virtual organizations, financial systems, the Internet of things, distributed economy and energy. In this workshop, the aim is to get acquainted with this network and learn Web 3 programming and its practical use.	1. Familiarity with the basics of the Ethereum network and the concept of the global computer 2. Familiarity with its application areas 3. Introduce network components 4. Training on how to join the network and use it 5. Training on DApp engineering structure	5

Important Dates

Submission Deadline: ~~November 30, 2020~~ December 20, 2020 (Anywhere on Earth)
Notification: February 3, 2021
Final Papers Due: February 13, 2021
Paper Registration: February 18, 2021
Conference: March 3-4, 2021