CIS Program Introduces First Artificial Intelligence Concentration Cohort

This fall, GMercyU’s Computer Information Science program will introduce the first cohort of students for its new Artificial Intelligence and Machine Learning concentration.

This new concentration opens many doors for the CIS Program and its students. With artificial intelligence used increasingly in everyday technology, this concentration was launched to meet industry demands. Students graduating from the program will become the future developers of new and exciting artificial intelligence technologies. Graduates will be presented with above average job availability and salaries. One such job is a Machine Learning Engineer in which the average pay is about $166,000 per year.

“Our students are not just learning how to use intelligent technologies – they will become the innovators who develop the artificial intelligence and automation of the future,” Cindy Casey, Assistant Professor of Practice and Program Coordinator for Computer Information Science said.

The program is designed to be hands-on, with a newly created lab space where classes will take place and students can work on projects. Some of the new equipment for the program includes:
• Raspberry Pi Robotic Arms
• Jetson nano Robotic Development Kits
• Facial and Voice Recognition Hardware
• Robotic Dog - https://www.youtube.com/watch?v=a1Jo2MXVUOg&feature=youtu.be
• GoPiGo Cars

The program will also introduce five new courses, including the following:

• (CIS115) Introduction to Artificial Intelligence This course introduces students to the field of artificial intelligence (AI) and data mining. By examining how datasets from mined information are harnessed for machine learning, students will gain a deeper understanding of how artificial intelligence can be utilized. AI is presently being used in everyday applications such as Gmail, Siri, and Netflix. From self-powered automobiles and military drones to marketing research and fraud marketing, this course will examine the history, present day use, and future of artificial intelligence. Topics discussed include autonomous machines, privacy, ethics, data analytics, neural networks, singularity, and quantum computing. Open to all majors. Prerequisites: None

• (CIS205) Machine Learning In this course students learn how algorithms are used in machine learning. Algorithmic Information Theory will be examined as a catalyst for making machines smarter. Learn how computers are capable of acting without being explicitly programmed. Supervised, unsupervised, semi-supervised and reinforcement algorithms will be examined. Students will learn how to apply machine learning algorithms to real-world problems and evaluate their effectiveness. Machine bias will also be discussed. Prerequisite: CIS 116 (Introduction to Python)

• (CIS311) Application and Embedded Machine Learning This course introduces students to the fundamentals of artificial intelligence and robotics. Using current open-source technologies students will create a program that will be executed by a small-scale robot. Students will evaluate and apply various algorithms in order to gain a better understanding of the complexity surrounding autonomous applications. Perquisites: CIS 116 (Introduction to Python, CIS 205 (Machine Learning)

• (CIS312) Data Analytics In this course, students will learn how to extract, cleanse, wrangle, transform, reshape, and analyze data. Common data types, where data is derived from, and the challenges practitioners face in the modern era of big data will be discussed. Using tools such as Welka, students will extract previously unknown and potentially useful information from large datasets. Taking a multidisciplinary approach, examples from business, social sciences, public health and laboratory sciences will be utilized to explore predictive analytic techniques. Data privacy and ethics will be explored. Perquisite: CIS 116 (Introduction to Python)

• (CIS401) Deep Learning This course is designed to provide students with an opportunity to research a selected topic or topics in order to integrate and deepen the student's comprehension and application of Artificial Neural Networks (ANNs) and Deep Machine Learning. Perquisites: CIS 205 (Machine Learning) and CIS 311 (Application and Embedded Machine Learning)

Interested in the program? Learn more here.