Overview

Class Central Tips

In this program, you will complete a real module from the online Master of Computer Science program that will help you understand artificial intelligence through a combination of both theory and practice. Through a series of interactive lectures and team-based projects, you will explore how machines learn in the form of learning paradigms, how to create autonomous agents that can reason, learn, and act on their own, and how to train and optimize deep neural networks.

You’ll also strengthen foundational skills in mathematics that will underpin your work in the field of artificial intelligence. Upon completion of these courses, you will have a strong understanding of the techniques used by practitioners in the field of AI, allowing you to advance your career in AI, work more effectively on machine learning projects, and identify opportunities for how you can apply AI to your current role or company.

By committing to online study for 6-9 months, you can earn the Artificial Intelligence and Machine Learning MasterTrack Certificate that will be a pathway to the online Master of Computer Science degree at Arizona State University.

Syllabus

Course 1: Statistical Machine Learning
- This course investigates the data mining and statistical pattern recognition that support artificial intelligence. Main topics covered include supervised learning; unsupervised learning; and deep learning, including major components of machine learning and the data analytics that enable it.

Course 2: Artificial Intelligence
- This course addresses the core concepts in designing autonomous agents that can reason, learn, and act to achieve user-given objectives and prepares students to address emerging technical and ethical challenges using a principled approach to the field. Main topics include principles and algorithms that empower modern applications and future technology development for self-driving vehicles, personal digital assistants, decision support systems, speech recognition and natural language processing, autonomous game playing agents and household robots.

Course 3: Knowledge Representation and Reasoning
- Knowledge representation and reasoning (KRR) is one of the fundamental areas in Artificial Intelligence. It is concerned with how knowledge can be represented in formal languages and manipulated in an automated way so that computers can make intelligent decisions based on the encoded knowledge. KRR techniques are key drivers of innovation in computer science, and they have led to significant advances in practical applications in a wide range of areas from Artificial Intelligence to Software Engineering. In recent years, KRR has also derived challenges from new and emerging fields including the semantic web, computational biology, and the development of software agents. This is a graduate-level course that introduces fundamental concepts as well as surveys recent research and developments in the field of knowledge representation and reasoning.

Course 4: Intro to Deep Learning in Visual Computing
- In this course, you will learn the basic principles of designing and training deep neural networks with a focus on computer vision. In this course, you will learn the founding principles for training deep neural networks along with techniques to train and optimize them. You will learn the principles of CNNs, generative modeling for unsupervised learning and much more.