AI Weekend Technical Workshops Instructors
Omar is a Computer Science graduate from the American University of Beirut with over two years of work experience in the software industry, including IT, Media, E-commerce, Finance, and Banking.
Introduction to Computer Vision
This workshop will introduce you to how to employ computer vision in your various projects. Images and videos are more present now than ever. As a result, computer vision was made to utilize those images and videos. Computer vision is used in various ventures from special effects to self driving cars!
During the session, you will be taken through several numerous project where you will use computer vision to blur images, draw shapes, and recognize faces from images!
You will learn:
• What is computer vision.
• The difference between Image processing and computer vision
• How to develop code using computer vision.
Prerequisite: Basic programming background.
Anis Ismail is a senior Computer Engineering student and the president of AI Club at LAU. He is also an undergraduate researcher at the Electrical and Computer Engineering department at LAU, and his research interests include Deep Reinforcement Learning and Generative Adverserial Networks. He is also an incoming Machine Learning intern at BMW Group in Germany.
Introduction to Deep Learning with PyTorch
This workshop will introduce you to how to build and train Deep Neural Networks with Pytorch, the popular library for developing flexible amd powerful Deep Learning solutions. By the end of this workshop, you will: • Develop an intuitive understanding of the theory behind Deep Learning. • Understand the components needed to train a Neural Network. • Develop code using PyTorch to build and train a Neural Network. This workshop assumes a prerequisite of basic programming background in Python.
Reem Mahmoud is a cofounder and Education Lead at Zaka, a community driven Artificial Intelligence (AI). Company with a mission of democratizing AI in the MENA region. Reem is pursuing her Ph.D at the American University of Beirut, Lebanon in Electrical & Computer Engineering where her research focuses on personalized Machine Intelligence with a focus on learning from limited labeled data. She is also the Vice President of the Beirut AI community, a Lebanese NGO bringing applied AI education and adoption to Lebanon.
Machine Learning for Time-series Prediction
This workshop will introduce you to how to build prediction models for time-series sensing data. Time-series data is abundantly collected from smart phones, wearable devices, and other edge devices. Applications of interest for time-series prediction include efficient resource planning, personalized context aware sensing, smart homes and cities, and much more!
During the session, you will be taken through a step-by-step hands-on project where you will use smart devices to solve a real-world problem. You will be introduced to how to treat time-series data types and build your own Machine Learning prediction model!
You will learn how to:
• Structure a time-series prediction project.
• Read, prepare, and clean data collected from smart devices/sensors.
• Build, train, and evaluate machine learning models that can predict future events from past data.
This workshop assumes a prerequisite of basic programming background and fundamental understanding of Machine Learning concepts.