The education sector was among the hardest hit by the COVID-19 pandemic. Schools across the globe were forced to shutter their campuses in the spring of 2020 and rapidly shift to online instruction. For many higher education institutions, this meant delivering standard courses and the "traditional" classroom experience through videoconferencing and various connectivity tools. The approach worked to support students through a period of acute crisis but stands in contrast to the offerings of online education pioneers. These institutions use AI and advanced analytics to provide personalized learning and on-demand student support, and to accommodate student preferences for varying digital formats.

Machine learning is a branch of data science which involves using "data science programs that can adapt based on experience," said Ben Tasker, technical program facilitator of data science and data analytics at Southern New Hampshire University. As the fields of science and engineering continue to advance, artificial intelligence is becoming "a lot less artificial and a lot more intelligent," Tasker said. Because so much about the field of data science in general and AI in particular is new, there are many opportunities to "make your own niche, especially now that many companies have started to invest in the idea of artificial intelligence," Tasker said. AI Engineer: In this role, one may be involved in the different facets of designing, developing and building artificial intelligence models using machine learning algorithms. Big Data Engineer: Overlapping with the role of a data scientist, the person in this role analyzes a company's volume of data known as "big data," and then uses the analyses to mine useful information in support of the company and its business model.

Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by the security researchers and practitioners. The literature covers different adversarial security attacks and perturbations on ML and DL methods and those have their own presentation styles and merits. A need to review and consolidate knowledge that is comprehending of this increasingly focused and growing topic of research; however, is the current demand of the research communities. In this review paper, we specifically aim to target new researchers in the cybersecurity domain who may seek to acquire some basic knowledge on the machine learning and deep learning models and algorithms, as well as some of the relevant adversarial security attacks and perturbations.

Dr. Daniel "Brian" Letort is a Fellow and Chief Data Scientist at Northrop Grumman Corporation. He has held various roles in his 18 year tenure, which have spanned software engineering, systems engineering, systems architecture, and chief architect. Throughout the roles, his interest have surrounded the strategic and forward-thinking use of data. Additionally, Brian serves as an adjunct instructor at both Colorado Tech and Southern New Hampshire University. Additionally, he serves as a lead faculty at Southern New Hampshire University.