1. CS855 Overview
Dr. Charles Tappert

2. Pattern Recognition and Machine Learning
Immensely broad subject
Applications in many fields
Scene analysis, document searching, handwriting and gesture recognition, speech recognition and understanding, geological analysis, recognition of bubble chamber tracks, and biometrics
Central to human-machine interface problems
Siri – speech recognition and speech understanding

3. Pattern Classification by Duda, Hart, and Stork
Definition: Pattern recognition is the act of taking in raw data and taking an action based on the “category” of the pattern
Provides unified presentation of classification theory
Parametric and nonparametric methods
Supervised and unsupervised learning
Discusses relative strengths and weaknesses of the various classification techniques

4. Deep Learning by Goodfellow, Bengio, and Courville
Deep learning involves a hierarchy of concepts that allows the computer to learn complicated concepts by building them out of simpler ones
Graphically the concepts are built on top of each other with many layers
Deep learning solves the representation problem by introducing representations expressed in terms of simpler representations
The quintessential example of a deep learning model is the feedforward deep network or MLP

5. Deep Learning: Practical Approach by Patterson and Gibson
Deep learning systems are neural networks with a large number of parameters and layers in one of four fundamental architectures
Unsupervised pretrained, convolutional, recurrent, and recursive networks
First four chapters cover the theory and fundamentals
Last five chapters cover a series of practical paths for building deep learning systems

6. This Course
Each textbook has enough material for a two-semester course
This course will cover
Duda: most chapters – the important ones for the course focus in more depth than others
Goodfellow: important chapters, with focus on the convolutional layers
Patterson: important chapters, with focus on the practical aspects