Dr. Charles Tappert,
Goldstein Academic Center, Room 325, Pleasantville NY.
Graduate Assistant: Jigar Jadav Email
Class Meetings: Wednesdays 6:10-9:00pm, Goldstein Academic Center, Room 300, Pleasantville NY.
Textbooks and Additional Information:
Pattern Classification, 2nd Edition, Duda, Hart, and Stork. Wiley 2000. Errata for early printings
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. MIT Press, Dec 2016.
Additional technical material will be provided (journal articles, conference papers, book sections).
Other Textbooks Available Online (useful as aditional reading, not required):
Pattern Recognition and Machine Learning, Bishop. Springer 2006.
The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman. Springer 2011.
Data Mining (3rd Ed.), Witten, Frank, and Hall. Morgan Kaufmann 2011.
Pattern Recognition Resources (this Duda book not same as print version).
Neural Networks and Deep Learning, Michael Nielsen, online 2016.
Other Textbooks (useful as aditional reading, not required):
Artificial Intelligence for Humans Volume 3: Deep Learning and Neural Networks , Jeff Heaton, Heaton Research 2015.
Machine Learning with R (2nd Edition), Brett Lantz, Packt 2015.
Machine Learning, Jason Bell, Wiley 2015.
Machine Learning, Peter Flach, Cambridge 2012.
Machine Learning for Dummies, Mueller and Massaron, dummies 2016.
Survey and Review Articles on Deep Learning:
Deep learning, Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, 2015.
Deep Learning in Neural Networks: An Overview, Jurgen Schmidhuber, 2014.
A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning, Li Deng, 2014.
Deep Machine Learning -- A New Frontier in Artificial Intelligence Research, Itamar Arel, Derek Rose, and Thomas Karnowski, 2010.
Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, and Pascal Vincent, revised 2014.
Learning Deep Architectures for AI, Yoshua Bengio, 2009.
A world survey of artificial brain projects, Part I: Large-scale brain simulations, Hugo de Garis, Chen Shuo, Ben Goertzel, Lian Ruiting, 2010.
A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures, Ben Goertzel, Ruiting Lian, Itamar Arel, Hugo de Garis, Shuo Chen, 2010.
Articles on Deep Learning:
What is the Best Multi-Stage Architecture for Object Recognition?, Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun, Int. Conf. Computer Vision, 2009.
This course focuses on the fundamental concepts, theories, and algorithms for pattern recognition and machine learning. Diverse application areas such as optical character recognition, speech recognition, and biometrics are discussed. Topics covered include supervised and unsupervised (clustering) pattern classification algorithms, parametric and non-parametric supervised learning techniques, including Bayesian decision theory, discriminant functions, the nearest neighbor algorithm, and neural networks with emphasis on deep learning.
Learning Outcomes and Major Topics:
After taking this course, the students should be able to:
Evaluation for Course Grade: