CSE 455/555 Introduction to Pattern Recognition
Instructor Wen Dong
Time & Location 8:00am – 9:20am on Tuesdays and Thursdays @ Cooke 121
Office Hours 10am – noon on Tuesdays @ Davis 306
TA Office Hours noon – 4pm on Fridays @ Davis 302
Pattern recognition is a research area on labeling objects from noisy signals using computer algorithms and visualizing patterns. While pattern recognition, machine learning and data mining are all about learning to label objects,
Pattern recognition workflow involves iterating between data acquisition, preprocessing, feature extraction, feature selection, model selection and training, evaluation until delivering a solution. While traditional pattern recognition mostly concerns feature selection and model training, the availability of big data and neural network frameworks such as Tensorflow and Torch also makes automatic feature extraction as a pattern recognition topic.
As an introductory course at the senior level and first year graduate level, we will be covering the concepts of traditional and modern pattern recognition, train students to wield probability theory tools to solve pattern recognition problems, and prepare students for advanced research/industrial projects in this field.
After taking this course, students should be able to design pattern recognition algorithms to solve real-world problems. In order to design such algorithms, students should feel comfortable with
formulating a pattern recognition problem using Bayesian decision theory, estimate the probability distributions of features using both parametric methods and nonparametric methods, understand the design principles, algorithms and applications of well-developed tools such as
cope with real-world problems with Python, R or MATLAB.
Assessment will be based on five problem sets, mid-term and final exams and class participation. Late submissions are worth only half credits.
(subject to change based on the learning progress)