This is a project based course, focusing on the science connecting the field of neural network with human brain mechanism, as well as advancements which are at the front of the field. We start by introducing a few neural network architectures with their learning paradigms, including deep feed-forward and recurrent networks, Hopfield's memory, and Kohonen's self organizing maps.

This is a project based course, focusing on the science connecting the field of neural network with human brain mechanism, as well as advancements which are at the front of the field. We start by introducing a few neural network architectures with their learning paradigms, including deep feed-forward and recurrent networks, Hopfield's memory, and Kohonen's self organizing maps.

This course covers a broad range of topics related to cyber security and operations. Our focus is on real world studies of reverse engineering, exploit analysis, and capability development within the context of computer network operations and attack. The course has an emphasis on hands-on exercises and projects.

This course covers a broad range of topics related to cyber security and operations. Our focus is on real world studies of reverse engineering, exploit analysis, and capability development within the context of computer network operations and attack. The course has an emphasis on hands-on exercises and projects.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering the most commonly used image and shape descriptors. It will proceed with statistical models for representing 2D images, textures, 3D shapes and scenes. The course will then provide an in-depth background on topics of shape and image analysis and co-analysis. Particular emphasis will be given on topics of automatically inferring function from shapes, as well as their contextual relationships with other shapes in scenes and human poses.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering the most commonly used image and shape descriptors. It will proceed with statistical models for representing 2D images, textures, 3D shapes and scenes. The course will then provide an in-depth background on topics of shape and image analysis and co-analysis. Particular emphasis will be given on topics of automatically inferring function from shapes, as well as their contextual relationships with other shapes in scenes and human poses.

Students will learn a systematic approach for visualization analysis and design to explore complex data in this course. The first part of the course focuses on teaching data visualization principles, including human perception, different visual encoding channels, and data and task abstraction techniques. The second part of the course will cover multiple aspects of data presentation for exploring patterns in data, including a wide range of statistical graphics and information visualization techniques.

Students will learn a systematic approach for visualization analysis and design to explore complex data in this course. The first part of the course focuses on teaching data visualization principles, including human perception, different visual encoding channels, and data and task abstraction techniques. The second part of the course will cover multiple aspects of data presentation for exploring patterns in data, including a wide range of statistical graphics and information visualization techniques.