Course Description
Computational intelligence, a.k.a. soft computing, is the use of inexact solutions to computationally hard tasks such as the solution of NPcomplete problems. This course provides a comprehensive introduction to computational intelligence techniques and their applications in solving optimization problems and complex realworld problems. The techniques may include but are not limited to: fuzzy logic and fuzzy systems, neural networks and deep neural networks, support vector machines, genetic algorithms, metaheuristics and swarm intelligence, and Bayesian network.
Learning Objectives
Upon completion of the course the students will be able to:  Understand basic concepts and principles of computational intelligence;
 Know how to apply computational intelligence to decision making in forecasting and optimization problems;
 Know how to apply computational intelligence to complex realworld problems.
Text Book
Reference Books  James M. Keller and Derong Liu (2016), Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation, IEEE Press.
 Amit Konar (2005) Computational Intelligence: Principles, Techniques and Applications. Springer.
 Andries P. Engelbrecht (2007) Computational Intelligence: An Introduction. Wiley.
 Trevor J. Hastie, Robert J. Tibshirani, & Jerome H. Friedman (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.
 Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Other Resources (scholarly journals)  IEEE Transactions on Neural Networks and Learning Systems
 IEEE Transactions on Fuzzy Systems
 IEEE Transactions on Evolutionary Computation
 IEEE Transactions on Emerging Topics in Computational Intelligence
 Expert Systems with Applications
 The Journal of Machine Learning Research
 Applied Soft Computing
 KnowledgeBased Systems
 Neurocomputing
 Neural Networks
 Fuzzy Sets and Systems
 Soft Computing
 Fuzzy Optimization and Decision Making
Course Contents  Topics/Activities  1  Introduction to computational intelligence  2  Mathematical programming and statistical forecasting [HW1] Forecasting Leo's jump rope performance  3  Fuzzy set and fuzzy logic  4  Fuzzy regression and fuzzy clustering [HW2] Statistical and fuzzy forecasting & clustering  5  Support vector machine and support vector regression  6  Neural networks  7  Neural fuzzy systems  8  Recurrent neural networks deep learning [Midterm Exam] Comparing linear regression, fuzzy regression, support vector regression and neural networks.  9  Metaheuristic search algorithms [HW3] A*, simulated annealing, and tabu search  10  Artificial immune systems  11  Genetic algorithms  12  Swarm intelligence [HW4] Variable selection using GA, PSO and ACO.  13  Bayesian network [Optional Project] Strategies in Texas Hold'em.  14  Emerging topics [Final Exam]  Project  Phase 1. Literature review Phase 2. Reproducing results from a recent journal paper Phase 3. Improving the method proposed by the journal paper reproduced in Phase 2. 
Grading  Homework: 10' x 4 = 40'
 Exam: 15' x 2 = 30'
 Project: 10' x 3 = 30'
 Bonus (Canvas discussion): 10'
