Syllabus
CS 457/557:
Computational Intelligence
Spring 2014
Meeting Times 
Lect: 12:00  12:50 M, Wed, Th, HB
106 Labs: 12:00  12:50 Tu, HB 204 
Instructor 
Dr. Razvan Andonie, HB 219B, Office hours 
Textbook 
Stephen Marsland, Machine Learning: An Algorithmic Perspective, CRC Press,
2009 
Introducing
concepts, models, algorithms, and tools for development of intelligent systems.
Example topics include artificial neural networks, genetic algorithms, fuzzy
systems, swarm intelligence, ant colony optimization, artificial life, and
hybridizations of the above techniques. We will at all these techniques from a
machine learning perspective. This domain is called Computational Intelligence,
and is a numerical interpretation of biological intelligence.
Topics & Student Learning
Outcomes
Neural networks,
associative memories, vector quantization, selforganizing feature maps,
support vector machines, genetic algorithms, fuzzy neural networks, swarm
intelligence, ant colony optimization, decision trees, ensemble learning,
nearest neighbor method, Gaussian mixture methods, principal component
analysis, independent components analysis, hill climbing, reinforcement
learning, Markov, decision processes,
simulated annealing, hidden Markov models, Bayesian networks.
On the completion of this course, the student will have:
An understanding of
fundamental computational intelligence and machine learning models.
Implemented neural
networks, genetic algorithms, and other computational intelligence and machine
learning algorithms.
Applied computational
intelligence and machine learning techniques to classification, prediction,
pattern recognition, and optimization problems.
95 
100 A
90 
94 A
87 
89 B+
83 
86 B
80 
82 B
77 
79 C+
73 
76 C
70 
72 C
67 
69 D+
63 
66 D
60 
62 D
0  59 F
The
slides for lectures and additional materials can be found in the shared
directory. The Python code and the datasets from the textbook can be found here.
Numeric
& Scientific Computation with Python
scikitlearn: machine learning algorithms in Python
Witten, I. H., Frank, E., Hall, M. A. Data Mining; Practical Machine Learning
Tools and Techniques, 2011.
Kaluza, B. INSTANT Weka Howto, Packt Publishing, 2013.
Computational Intelligence Library (CIlib)
Java Object
Oriented Neural Engine (JOONE)
ECJ 20: A Java Based Evolutionary
Computation and Genetic Programming Research System
LIBSVM – A Library for Support Vector
Machines
UCI Machine Learning Repository
UCI
Weka datasets in arff
format
Rojas, P. Neural Networks – A Systematic
Introduction, Springer, 1996.
Hastie, T., Tibshirani,
R., and Friedman, J. The Elements of Statistical
Learning, Springer, 2009.
Date 
Topics 
Reading 
Assignments 
4/2 
Introduction 
Syllabus 

4/3 
Preliminaries in Computational Intelligence, WinPython, WEKA 
Ch
1 

4/7 
Overview of Neural Networks 
Ch
2 

4/9 
Learning in NN 
Ch
2 

4/10 
Learning in NN 
Ch
2 
Start HW 1 (graded) 
4/14 
The Perceptron Convergence Theorem 

4/16 
MultiLayer Perceptron 
Ch
3 
HW 1 due Start HW 2 (graded) 
4/17 
MultiLayer Perceptron 
Ch
3 

4/21 
Training, Testing, and Validation 
Ch. 3 

4/23 
Data PreProcessing, Regression, Universal Approximation,
Classification, TimeSeries Prediction 
Ch. 3 
HW 2 due Start HW 3 (graded) 
4/24 
Unsupervised Learning: kMeans 
Ch. 9 

4/28 
Unsupervised Learning
LVQ 
Ch. 9 

4/30 
Unsupervised Learning: SOM 
Ch. 9 

5/1 
Evolutionary Learning, Genetic Algorithms 
Ch. 12 

5/5 
Genetic Algorithms, The Fundamental Theorem of Genetic
Algorithms 
Ch. 12 
HW 3 due Start HW 4 (graded) 
5/7 
Dimensionality Reduction & Feature Selection 
Ch. 10 

5/8 
Dimensionality Reduction: PCA 
Ch. 10 

5/12 
Radial Basis Function Network 
Ch. 4 

5/14 
Support Vector Machines (SVM) 
Ch. 5 
HW 4 due Start HW 5 (graded) 
5/15 
Swarm Intelligence & Ant Colony Optimization 
Dorigo
and Parpinelli’s papers 

5/19 
Optimization and Search, LevenbergMarquardt,
Simulated Annealing 
Ch. 11 

5/21 
Class cancelled 


5/22 
Class cancelled 


5/26 
Memorial Day 
No classes 
Memorial Day 
5/28 
Probability and Learning: Naďve Bayes,, EM Algorithm, kNearest Neighbor 
Ch. 8 
HW 5 due 
5/29 
Fuzzy Systems 


6/2 
Hybrid Intelligent Systems, Expert Systems, NeuroFuzzy Systems 
Cai
& Kwan’s papers 

6/4 
Associative Memories: Hopfield NN & Boltzmann Machine 
Meyder
& Kiderlen’s paper 

6/5 
Project Presentations 

6/9 
Project Presentations: noon – 2:00 
Date 
Topics 
Reading 
Assignments 
4/8 
WinPython 
Ch. 16 
Use WinPython for Practice
Questions (not graded) 
4/15 
Weka 
Weka
manual 
Run Weka examples (not graded) 
4/22 
Character Recognition 

HW 2 
4/29 
Regression 

HW 3 
5/6 
Regression 

HW 3 
5/13 
GA Optimization 

HW 4 
5/20 
GA for NN and Games 

HW 5 
5/27 
Final Projects 


6/3 
Final Project 
