Course Description
Computational intelligence, a.k.a. soft computing, is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems. This course provides a comprehensive introduction to computational intelligence techniques and their applications in solving optimization problems and complex real-world 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, meta-heuristics 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 real-world 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
- Knowledge-Based 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 [Mid-term 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'
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