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.

This course will teach the principles and practice of distributed systems as applied in today's cloud computing environments. The course will cover fundamental concepts in distributed computing including distributed clocks, consistency, fault tolerance, and consensus. The course will also cover popular cloud computing service models, related programming models, datacenter architectures, software-defined networking, and security and privacy issues in public clouds. The course will expose students to public cloud platforms such as Amazon EC2, Google Cloud Engine, Microsoft Azure etc.

This course will teach the principles and practice of distributed systems as applied in today's cloud computing environments. The course will cover fundamental concepts in distributed computing including distributed clocks, consistency, fault tolerance, and consensus. The course will also cover popular cloud computing service models, related programming models, datacenter architectures, software-defined networking, and security and privacy issues in public clouds. The course will expose students to public cloud platforms such as Amazon EC2, Google Cloud Engine, Microsoft Azure etc.

This course is meant for those looking for practical legal knowledge for use in Internet related endeavors. This course provides basic legal knowledge for Internet related legal issues with a focus on practical information for use by computer professionals. Topics covered are basics of the internet, basics of law and contract law, substantive laws, intellectual property law, basic ethical dealings, dealing with third parties, and policy issues.

This course is meant for those looking for practical legal knowledge for use in Internet related endeavors. This course provides basic legal knowledge for Internet related legal issues with a focus on practical information for use by computer professionals. Topics covered are basics of the internet, basics of law and contract law, substantive laws, intellectual property law, basic ethical dealings, dealing with third parties, and policy issues.

This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamental principles and practical applications through hands-on approach. Many of the principles are taught through examples. The key topics of this course are a brief introduction to computer networking; applied cryptography; protecting users, data, and services; network security, and common threats and defense strategies. Students will complete a number of practical lab assignments as well as auto-graded quizzes/assignments.

This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamental principles and practical applications through hands-on approach. Many of the principles are taught through examples. The key topics of this course are a brief introduction to computer networking; applied cryptography; protecting users, data, and services; network security, and common threats and defense strategies. Students will complete a number of practical lab assignments as well as auto-graded quizzes/assignments.

This course aims to instruct key components related to Clinical Health Informatics and practical data science tools that can be applied to analyze health data. More specifically, this course aims to teach students 1) how to understand and process complex real-world health data, 2) theoretical fundamentals of quantitative analysis of clinical health data, and 3) how to apply the theory and tools to analyze health data.

This course aims to instruct key components related to Clinical Health Informatics and practical data science tools that can be applied to analyze health data. More specifically, this course aims to teach students 1) how to understand and process complex real-world health data, 2) theoretical fundamentals of quantitative analysis of clinical health data, and 3) how to apply the theory and tools to analyze health data.