La Salle University
Why Study Artificial Intelligence at La Salle?
This program provides comprehensive coverage of AI concepts, beginning with a general ‘artificial intelligence’ course that introduces students to the subject, then moving on to methodologies, and tools, equipping students with a deeper understanding of the field. In these AI-focused courses, students develop skills in machine learning, deep learning, natural language processing, and data analytics. The program provides the chance to engage in emerging technologies linked to innovation and advancements in many areas.
Students in this program will understand specific architectures used to develop AI tools and models. They will be equipped with increasingly valuable AI skills that are transferable across many industries. The strong understanding of AI principles allows students to consider diverse career paths that incorporate problem-solving, critical thinking, and data analysis. Students will approach complex challenges with varying views and solutions.
Highlighted Courses
CIS 654: Artificial Intelligence
This course introduces students to the field of artificial intelligence (AI). Students will learn how big data and data mining techniques are utilized by machines to create the AI models used by autonomous aircraft and automobiles, personal assistants, IT security software, fraud investigations and credit bureaus. The course will review the history, present day use, and future of artificial intelligence. Through case studies and current events, students will examine the benefits and risks associated with AI. The course will cover issues related to AI and privacy, ethics, and machine bias. Neuromorphic computing, the Open Neural Network Exchange (ONNX), and data analytics will also be discussed.
AI 656: Deep Learning and Neural Networks
This course provides an in-depth exploration of deep learning, a subset of machine learning that focuses on neural networks with many layers (deep architectures). Students will learn the theory, methodologies, and practical implementations of deep learning model.
BSA 735: Machine Learning with Business Applications
This course covers the fundamentals of machine learning used to solve business problems and improve business decisions through supervised (predictive) and unsupervised (descriptive) methods and applications. The course starts with supervised learning methods, including Linear Regression and Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Gradient Boosting Algorithms, and K-Nearest Neighbors (KNN). The course will then focus on unsupervised methods, including K-Means Clustering, Hierarchical Clustering, and dimensionality reduction
Meet the Faculty
Yang Wang is an Associate Professor and Director of Graduate Programs in Artificial Intelligence, Cybersecurity, and Computer Information Science at La Salle University. He earned his Ph.D. in Computer Science from Georgia State University in 2012, where he received the Ph.D. Dissertation Grant Award and the Outstanding Graduate Teaching Award. Before joining La Salle, he held several industry positions, including senior engineering roles at Futurewei Technologies and Internap. Dr. Wang has authored over 50 peer-reviewed publications in areas such as optical networking, network virtualization, and cybersecurity. His recent scholarly work focuses on generative AI in education and security, as well as AI-driven research on network optimization. He has secured multiple REU grants to support undergraduate research and was recently awarded a community-based research grant to develop a gamified, AI-infused learning platform aimed at supporting youth mental health coping skills. He actively serves on technical committees for IEEE and ACM conferences and has been recognized as an Exemplary Reviewer by the IEEE Communications Society.
