Overview
- This course will introduce essential concepts of Pattern Recognition that will include:
- Systems Overview
- Random variables
- Basic topics in probability and statistics, and linear algebra
- Discriminant Analysis and Bayes Decision Theory
- Supervised learning and parameter estimation
- Unsupervised learning
- Linear discriminant functions
- Learning and generalization
- Single and multi layer perceptrons
- Make pattern recognition and analysis accessible to computer scientists and engineers.
- Present numerous examples to illustrate the importance of pattern recognition in solving various engineering problems, especially in the domain of multi-dimensional with emphasis on image- and video-based signals.
- Create an interactive teaching environment where all participants can:
- Ask questions
- Comment on level of instruction and material
- Comment on speed of delivery
- Discuss relevant research
Student Responsibility
- Attendance in not mandatory, but highly recommended. If you miss a class, please try to obtain notes from your colleagues. We will have in-class discussion on various topics, so please keep up with your reading assignments.
- Late assignment submissions will only be accepted with a penalty of 33% per day. Please try to complete each assignment, as each will have a relevant exercise.
- You will make two presentations in class. We will discuss the details as the class progresses. Both presentations count towards your final grade. Each student should be present for the presentations, as an anonymous peer evaluation will be conducted at the end of the presentations.