_{Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University, Ph.D. - Ohio State University.}

_{Endowed Chair in Wireless Telecommunications, and McKnight Presidential Chair in ECE Digital Technology Center, Director University of Minnesota, Minneapolis. Ph.D. - University of Southern California.}

Pattern recognition is a field of computer science and engineering that deals with the automated recognition of patterns and regularities in data. It is a subfield of artificial intelligence and machine learning, and has a wide range of applications in different fields such as computer vision, speech recognition, bioinformatics, and image processing.

There are several types of pattern recognition techniques, including supervised learning, unsupervised learning, and semi-supervised learning.

Supervised learning is a technique where the system is trained with labeled data, where the desired output is already known. This type of learning is used in applications such as image classification, speech recognition, and natural language processing.

Unsupervised learning is a technique where the system is trained with unlabeled data. The system then finds patterns and regularities in the data on its own, which can be used for applications such as anomaly detection and clustering.

Semi-supervised learning is a technique that combines aspects of both supervised and unsupervised learning. It is used in cases where there is a limited amount of labeled data available, and the system is required to find patterns and regularities in the data on its own.

In computer vision, pattern recognition is used to recognize and classify objects in images and videos. This is useful in applications such as image search, object tracking, and autonomous vehicles.

In speech recognition, pattern recognition is used to convert speech into text. This is useful in applications such as voice-controlled assistants and dictation software.

In bioinformatics, pattern recognition is used to analyze large amounts of biological data, such as DNA sequences. This is useful in applications such as gene prediction and drug discovery.

In image processing, pattern recognition is used to analyze and process images, and it is used in applications such as image compression, image segmentation, and object recognition.

Overall, pattern recognition is a key area in computer science and engineering, with wide range of applications in diverse fields. With the increasing amount of data being generated, the field of pattern recognition is expected to continue to grow in importance and impact.