Syllabus
CS 457/557:
Computational Intelligence
Spring 2016
Meeting Times 
Lect: 1:00  1:50 M, Tu, Wed, HB
116 Labs: 1:00  1:50 Th, HB 206 
Instructor 
Dr. Razvan Andonie, HB 214E, Office hours 
Textbook 
Stephen Marsland, Machine Learning: An Algorithmic Perspective, CRC Press, Second Edition, 2015 
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 look at 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 Python
code and the datasets from the textbook can be found here. Here
is a nice neural
network simulation website. It lets you build, simulate and visualize a basic
neural network during training.
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
Keras: Deep Learning library for Theano
and TensorFlow
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.
Goodfellow,
I, Bengio, Y., and Courville,
A. Deep Learning, MIT Press.
Date 
Topics 
Reading 
Assignments 
3/29 
General Presentation 
Syllabus 

3/30 
Introduction, Python for machine learning 
Ch
1 & Appendix A 

4/4 
Weka Explorer 

4/5 
Weka: Experimenter and KnowledgeFlow Preliminaries in Machine Learning 
Ch
2 

4/6 
Overview of Neural Networks 
Ch
2 & 3 

4/11 
Learning in NN: unsupervised/supervised,
classification/regression, learning rules (Hebb, perceptron, delta,
winnertakesall), examples 
Ch
3 
Start HW 1 (graded) 
4/12 
Perceptron Convergence Theorem 
Ch
3 

4/13 
MultiLayer Perceptron, Backpropagation 
Ch
4 
HW 1 due Start HW 2 (graded) 
4/18 
Universal Approximation Theorem, TimeSeries Prediction 
Ch
4 

4/19 
Data PreProcessing 
Ch. 4 

4/20 
Training, Testing, and Validation 
Ch. 4 
HW 2 due Start HW 3 (graded) 
4/25 
Evolutionary Learning, Genetic Algorithms 
Ch. 10 

4/26 
Genetic Algorithms, The Fundamental Theorem of Genetic
Algorithms 
Ch. 10 

4/27 
Unsupervised Learning: kMeans,
LVQ 
Ch. 14 
HW 3 due Start HW 4 (graded) 
5/2 
Unsupervised Learning: SOM 
Ch. 14 

5/3 
Dimensionality Reduction & Feature Selection 
Ch. 6 

5/4 
Dimensionality Reduction: PCA 
Ch. 6 

5/9 
Radial Basis Function Network 
Ch. 5 

5/10 
Support Vector Machines (SVM) 
Ch. 8 

5/11 
Swarm Intelligence & Ant Colony Optimization 
Dorigo
and Parpinelli papers 
HW 4 due Start HW 5 (graded) 
5/16 
Optimization and Search, LevenbergMarquardt,
Simulated Annealing 
Ch. 9 

5/17 
Optimization and Search, LevenbergMarquardt,
Simulated Annealing 
Ch. 9 

5/18 
SOURCE presentations (12:40 – 2:00) – no class 


5/23 
Probability and Learning: Naïve Bayes, EM Algorithm, kNearest Neighbor 
Ch. 7 

5/24 
Convolutional Neural Networks 
Ch. 9 from Deep
Learning 

5/25 
Symmetric Weights and Deep Belief Networks: Hopfield NN
& Boltzmann Machine 
Ch. 17 Meyder
& Kiderlen paper 
HW 5 due 
5/30 
Memorial Day 
No classes 
Memorial Day 
5/31 
Symmetric Weights and Deep Belief Networks: Deep Learning 
Ch. 17 

6/1 
Deep Learning Implementations 

6/8 
Presentations of Final Projects (noon – 2:00) 


Date 
Topics 
Assignments 
3/31 
WinPython 
Use WinPython for Practice
Questions (not graded) 
4/7 
Weka 
Run Weka examples (not graded) 
4/14 
Character Recognition 
HW 2 
4/21 
Regression 
HW 3 
4/28 
Regression 
HW 4 
5/5 
GA Optimization 
HW 4 
5/12 
GA for NN and Games 
HW 5 
5/19 
Final Project 
HW 5 
5/26 
Final Project 

6/2 
Final Project 
