The ability to process large quantities of data and to efficiently conclude from it is critical for coping with the “big” data generated by the information revolution. Machines that learns patterns and predict from data, proved useful in Business Intelligence (BI), Computerized vision (e.g. autonomous cars), Speech recognition, Image, video and text generation, Fraud Detection and Cyber security. Deep learning using such networks is probably one of the most successful machine learning technologies in recent years with applications that span across. many domains. In this course students will learn the principles of using such brain inspired networks for machine learning and will practice programming small projects.
Machine learning (or Computational learning) is a sub-field in computer science and in Artificial Intelligence and intersects Statistics and Optimization theory. The field deals with algorithms, which allow computer to learn from examples, and to operate in a variety of computational tasks, where classical programming is impossible or not economical. Machine learning is relatively new area and is responsible to recent breakthroughs in Artificial intelligence, Data mining and automatic knowledge discovery in big data. In its core lies the ability to the specific tasks. This is done by analyzing large quantities of data, pattern recognition and forecasting future behavior.
A theoretical framework for measuring work and improving methods will be presented in the course. Additionally, various aspects of efficient execution of activities will be discussed, such as planning how the work will be performed, planning the workstation, and analyzing the environment.
Computer communication is a rapidly evolving field in recent years. Every action in our lives is made over the internet: sending an email, cell phone, browsing a browser, wireless internet, and more. In this course we will review the common communication model OSI and network architectures, we will get acquainted with common communication protocols (HTTP, TCP / IP, UDP) and we will examine different routing algorithms for communication networks. The course combines both a theoretical part and a practical part for the implementation of various protocols / communication models.