COGS 400W  / CISC/CMPE 452W / CISC 874W

Neural and Genetic Cognitive Models

Winter 2019

Prerequisites:  CISC 235/3.0 or ELEC 278/3.0, and programming experience.

Recommended knowledge: Any of the COGS courses (100, 201, or 300) or PSYC 100 or PSYC  221 or PHIL 270.

Instructor:

Farhana H. Zulkernine, PhD, PEng

Assistant Professor

Coordinator, Cognitive Science Program
756 Goodwin Hall, School of Computing, Queen's University
Kingston, Ontario, Canada K7L 2N8

E-mail: Farhana at cs dot queensu dot ca (farhana@cs.queensu.ca)

Website: http://research.cs.queensu.ca/home/farhana/
Tel: (613)533-6426


 

Humans continuously learn from the time they are born using a continuous feedback process. Various mechanisms are used for collecting information, analyzing it and producing output. If the output is not satisfactory, corrections are made next time to produce a better output. Artificial Neural Networks (ANN) are computational networked structures that are based on the above concept and the structure of brain cells called neurons. ANNs are composed of artificial neurons which work like the brain neurons, each performing a very simple task but when connected as a network, are able to learn gradually as humans enabling machine intelligence. Genetic Algorithms (GA) present another type of machine learning technique that is based on the concepts of evolution where better solutions are derived by using many iterations or generations of trial and errors. The process starts with a random set of solutions which gradually gets refined by creating new solutions from the old ones until no further significant improvements can be achieved. These machine learning techniques are showing promising results for certain problem domains where all the data are not known in advance, and where machines need to continuously learn to adapt to the changing stimuli or situation. The brain-like ANNs have been used to model human cognition, perception, and memory. Recently these techniques are also being applied to train and refine the knowledge of robots which are used in many critical applications such as fire-fighting and medical surgery. You will learn different ANNs and GAs in this course, models of ANNs for human cognition, and how these techniques are used in problem solving such as classification, clustering, optimization and data reduction. You will need to develop software programs (using any programming language such as C, C++, Matlab, Java) that apply some of these algorithms to solve real world problems. By the end of the course, you will know the significant ANNs and GA techniques, have experience in writing software programs that implement some of these algorithms to solve practical problems, and get an idea of the ongoing research in this area in various application domains.

 

Learning Outcomes

Course Learning Outcomes: http://www.cs.queensu.ca/students/undergraduate/CLO.php#COGS400

Syllabus

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 reductio
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. Brain-state-in-a-Box (BSB), Hopfield and Self Organizing Map (SoM) networks.
Unit 5: Optimization algorithms for ANN such as Simulated Annealing and Genetic Algorithms.

 

Class time and location
Days & Times Room Meeting Dates
Tue 9:30am - 10:30pm KINES & HLTH  RM100 Jan 7 - Apr 5
Thu 8:30am - 9:30am KINES & HLTH  RM100 Jan 7 - Apr 5
Fri 10:30am - 11:30am KINES & HLTH  RM100 Jan 7 - Apr 5

 

Course Details

Details about the course content can be found at the OnQ website.

 

Office Hour

Tuesdays 10:30pm - 11:30pm. Contact the TAs first to setup flexible meeting times.

 

Grade Distribution

Online quizzes, assignments, project, midterm, and final exam. See OnQ for grade distribution.

Grade Categories

% of Final Grade

Online Quizzes

10%

Assignments

35%

R&D, Group Project + Report + Presentation

15%

Midterm

20%

Final Exam

20%

Grade Categories (CISC874)

% of Final Grade

Online Quizzes 10%
Assignments 35%
Individual Survey + Group Project + Report +  Presentation 150%
Midterm 20%
Final Exam 20%

Text Book

Elements of Artificial Neural Networks

By Mehrotra, Mohan, and Ranka (ebook from MIT press https://mitpress.mit.edu/books/elements-artificial-neural-networks )

2nd Edition

Available at the Campus Book Store

http://www.campusbookstore.com/Textbooks/Course/17202-COGS400-WINTER2018

 

Teaching Assistants

***Note: The students are advised to use the discussion forum on the OnQ site for general inquiries before making an appointment to see the TA.