Applied Soft Computing

Catalog description

Covers relatively new approaches to machine intelligence known collectively as “soft computing”. Introduces various types of neural networks, including word embeddings, recurrent neural networks, transformers, vision neural networks, and graph neural networks, along with several synergistic approaches for combining them as hybrid intelligent systems. Emphasis is on applications, including modeling, prediction, design, control, databases and data mining. Prerequisites: Basic knowledge of calculus and discrete mathematics and competence in Python, or permission of instructor. Crosslisted with ISE 419. Offered in the Fall semester.