Prerequisites: Linear Algebra and programming experience.
Farhana H. Zulkernine, PhD, PEng
Coordinator, Cognitive Science Program
Bigdata Analytics and Management (BAM) Laboratory
637 Goodwin Hall, School of Computing, Queen's University
Kingston, Ontario, Canada K7L 2N8
E-mail: fhz at queensu dot ca (email@example.com)
Theoretical foundation and practical applications of Artificial Neural Networks (ANN) and Cognitive Computing (CC) models. Paradigms of neural computing algorithms using attention and context embedding models, applications in cognitive modeling, artificial intelligence, and machine learning with multi-stream data processing techniques.
Prerequisites: Knowledge of relational algebra
Students are expected to spend 120 hours per term in lecture and practice.
Course Learning Outcomes:
|Unit 1:||Biological neurons and the evolution of Artificial Neural Network (ANN) models. Example of an ANN model applied to human cognition. General architecture and concepts behind ANN and its application in machine learning. Learning algorithms for different machine learning applications such as classification, clustering, associations, predictions, image processing and compression, and dimensionality reduction.|
|Unit 2:||Supervised learning in ANNs. Learning algorithms primarily include error correction (back propagation) and feedback learning in multilayer feed-forward, recurrent, Radial Basis Function, and adaptive networks. Engineering ANNs, design techniques include network pruning algorithms for adaptive networks, momentum, and decay.|
|Unit 3:||Unsupervised learning in ANNs. Kohonen, Adaptive Resonance Theory, Principal Component Analysis networks. Application of competitive learning, and Hebbian or reinforcement learning.|
|Unit 4:||Associative networks, memory modeling, cognitive modeling and deep neural networks.|
|Unit 5:||Seminars on recent research|
Class time and location
|Days & Times||Room||Meeting Dates|
|Monday 10:30am - 12:00pm||Virtual Classroom||Jan 11 - Apr 9|
|Wednesday 10:00am - 11:30am||Virtual Classroom||Jan 11 - Apr 9|
Details about the course content can be found at the OnQ website.
% of Final Grade
Project and presentations
Elements of Artificial Neural Networks
By Mehrotra, Mohan, and Ranka
2nd Edition (ebook available to order using the following link or from Amazon.ca)
By Charu C. Aggarwal
e-Text book 2018, https://link.springer.com/book/10.1007/978-3-319-94463-0, Springer
Neural Networks and Deep Learning, By Michael Nielsen, e-book - Reference online study material, http://neuralnetworksanddeeplearning.com/