Courses
- Seminar In Machine Learning (10355) תקציר הקורס:
- Computer Vision (65011) תקציר הקורס:
- Seminar on Generative AI (65025) תקציר הקורס:
- Foundations of Machine Learning (65028) תקציר הקורס:
- Advanced Deep Learning (65030) תקציר הקורס:
- Seminar on Smart Cities and IOT (65034) תקציר הקורס:
Abstract:
This seminar is designed for students with basic knowledge of machine learning who seek to explore and apply ML techniques to solve real-world challenges. The course emphasizes a research-oriented, project-based approach, guiding students from problem definition to solution implementation, including analysis, model training, and evaluation. The course will culminate in student presentations and research papers documenting their projects, reflecting on the practical and theoretical contributions of their work.Abstract:
During the course, methods for image processing and computer vision will be presented, with an emphasis on techniques currently used in academia and industry. Topics will include basic image processing, feature detection of objects in images, image quality enhancement, object tracking and classification, color identification, depth, lighting, and shading, scene understanding in images, and more.Abstract:
This project-based seminar explores the fundamentals and applications of Generative AI across multiple data domains, including text, images, audio, video, structured, and multimodal data. Through a combination of theory and practical experience, students will develop hands-on projects that demonstrate their understanding of generative AI techniques and their applicability to real-world scenarios. The course emphasizes the end-to-end project lifecycle, from literature review and project planning to the implementation, evaluation, and reporting of generative AI systems. Students will present their work twice, providing a platform for peer feedback and iterative improvement, and will submit final project reports.Abstract:
This course introduces students to the fundamental principles of machine learning, focusing on understanding and applying various algorithmic models. Students will learn how to analyze and interpret complex datasets, build regression and classification models, and apply unsupervised learning techniques. Throughout the course, students will be assigned homework exercises to practice the concepts learned, and a final exam will be heldAbstract:
This PBL course focuses on cutting-edge techniques and real-world applications. Students will explore generative models (GANs, VAEs), transfer learning, self-supervised learning, and attention-based architectures. The course also covers advanced optimization, adversarial robustness, anomaly detection, and explainability in deep learning. Through hands-on projects, students will apply these techniques in fields like computer vision, natural language processing, and anomaly detection.Abstract:
This seminar offers a profound and innovative exploration of smart cities, the captivating and challenging focal point of urban systems in the advanced technological era. Within the seminar framework, participants will be introduced to the challenges and issues pertaining to the development of data-driven systems in the context of smart cities.
Through lectures, both theoretical and practical aspects will be examined, with the aim of identifying, comprehending, and resolving the challenges that lead to the cities of the future. Furthermore, the seminar will feature guest lectures showcasing existing data-driven systems.
The seminar's cornerstone lies in hands-on project execution, where students will delve into specific challenges within smart cities and create unique computational models to solve particular issues.