William M.

This specialization allows you to learn how to apply design principles, patterns, and architectures to create reusable and flexible software applications and systems. Learn how to express and document the design and architecture of a software system using a visual notation known as Unified Modelling Language (UML). Also review architectures for web applications, then explore the basics of Service-Oriented Architecture (SOA) in two approaches: Web Services (WS*) and Representational State Transfer (REST) architecture. Introduce the most common architectures, their qualities, and tradeoffs. Talk about how architectures are evaluated, what makes a good architecture, and an architecture can be improved. Finally talk about how the architecture touches on the process of software development.

This specialization provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: - Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). - Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). - Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.