Fundamental concept and knowledge of machine learning will be covered in this course using Python and MATLAB. This includes supervised and unsupervised learning (e.g. support vector machine, clustering, neural network), mathematical and probative approaches. Students will have an opportunity to experiment with machine teaming solutions on various datasets.

This course offers the student both theory and lab work that examines modern cloud technologies and everything-as-a-service (EAAS) and explores the machine learning and AI workbench in cloud. The students will learn installation, networking, support and administration of cloud technologies that can serve the needs of the businesses of today for AI, data science analytics and engineering. Also, security and disaster recovery strategies will be studied and applied.

Data governance is the management of the availability, usability, integrity and security of data and information. Legal, ethical and organizational frameworks all must be considered whenever working with or presenting information from data. Reviewing studies and applicable legislation, students will learn to exercise ethical judgement in the use of data for an organization while also protecting the rights of groups and individuals.

The focus of this course is to explore the analytical and statistical methods and tools used to process and visualize data. In this course, students will learn the essential concept of data analysis using Python programming. Students will work with Python tools and libraries for AI algorithms in a lab environment.

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. Students will learn the fundamentals of working with data in vector and matrix form and formulate machine learning tasks.

This is a multi-disciplinary course designed to help students develop their skills in managing technical project management methods. Students will learn how to plan a project and work toward achieving their project goals.

This course allows students to work through a guided project from design to development to implementation. This team-based project will provide students the opportunity to demonstrate their combined knowledge in AI and machine learning. Students will be challenged to assign responsibilities, create, and maintain satisfactory working relationships with the client, accept feedback, meet project deadlines, manage the production of deliverables to industry standards and formally present their findings.

This deep learning course is based in the Python programming language and will provide students with experience in pandas, matplot, numpy and TensorFlow. Students will have the opportunity to implement different types of deep learning algorithms, such as convolution neural networks, recurrent networks, generative adversarial networks and autoencoders. Students will train neural networks and create neural network architectures in TensorFlow.

This course introduces basic knowledge and concepts of machine vision, including image processing, pattern recognition and object tracking. To gain practical experience in this field, students will use the industry standard OpenCV library for developing machine vision applications. Students will create real-time applications, such as games or simulations, using image- and video-processing techniques.

By the end of this course, students should have a broad understanding of the field of natural language processing. They should have a sense of the capabilities and limitations of current natural language technologies and some of the algorithms and techniques that underlie these technologies. They should also understand the theoretical underpinnings of natural language processing in linguistics and formal language theory.

This course introduces the core concepts of social media analytics: an introduction to social media, the foundations of collecting and storing social media data and how to use AI and ML tools to analyze social media data. Also, the course provides students with hands-on practices and the opportunity to build, train and apply models that analyze social media data.