CSCE 768 Pattern Recognition Fall 2017
Why pattern recognition? Pattern recognition is the key technique for understanding the data and for making intelligent decisions. A major focus of pattern recognition research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.
search engines, financial investment and trading, Amazon recommendation systems, spam filter, network intrusion detection systems, bioinformatics, computational biology, machine translation, robotics, medical diagnosis, speech and image recognition, biometrics, finance.
Course Meeting Time & Location
Meeting Time: TTH 2:50 am - 4:05 pm
Classroom: 2A05 Swearinger Engineering Center
Pattern Classification (2nd Edition) by Duda and Hart (2001)
ISBN-10: 2nd edition 0471056693 Wiley-Interscience;
Course Description This course will cover the techniques and topics that are widely used in real-world pattern recognition. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Pattern recognition ( along with Machine learning or data mining) is at the center of information revolution.
Course Highlights Many interesting topics will be covered:
2 Bayesian Decision Theory
3 Maximum Likelihood and Bayesian Parameter estimation
4 nonParametric Methods
5 Linear Discrimination 85
6 Multilayer neural network 105
7 Stochastic methods: search boltzman machines, Genetic algorithms
8 Nonmetric Methods
9 unsupervised learning and clustering
10 Dimensionality Reduction
13 Hidden Markov Models
TBD Deep Learning
Prerequisite You should be able to program using high-level language C/C++ or java.
If you have problem to enroll, pls contact/call CSE secretary
Randi 803 777-7849 (phone)
Swearingen Bldg., Room 3A01L
Department of Computer Science and Engineering
College of Engineering and Computing
University of South Carolina
301 Main Street, Columbia, SC, 29201