INTERNATIONAL BURCH UNIVERSITY
Department of Information Technologies
COURSE
Code
CEN 559
Status
Name
Machine Learning
Number of ECTS Credits
Level
Graduate
Class Hours Per Week
Semester
Fall
Total Hours Per Semester
Elective
7.5
3
45
Instructor
Assoc. Prof. Dr. Abdülhamit Subaşı
Working Time:
Class Schedule: Wed 16:00-18:45
Office Hour: Open Door Policy
asubasi@ibu.edu.ba
1.
LEARNING
OBJECTIVES AND
GOALS
2.
STUDENT
ASSESSMENT
METHODS
Assistant


Present the key algorithms and theory that form the core of machine learning.
Draw on concepts and results from many fields, including statistics, artifical
intelligence, philosophy, information theory, biology, cognitive science,
computational complexity, and control theory.



Midterm Examination
Research
Final Examination
1.
Du and Swamy, Neural Networks in a Softcomputing Framework, SpringerVerlag London Limited, 2006.
Sebe, Cohen, Garg and Huang, Machine Learning in Computer
Vision, Springer, 2005.
Chow and Cho, Neural Networks and Computing, Imperial College Press,
2007.
Mitchell T., Machine Learning, McGraw Hill, 1997.
T. Hastie,R. Tibshirani, J. Friedman, The Elements of Statistical Learning,
Second Edition, Springer, 2008.
2.
3.
TEXTBOOK(S)
3.
4.
5.
4.
LANGUAGE OF
INSTRUCTION
6.
25%
25%
50%
English

5.
Year
Examination dates and times set forth are firm. Students are requested to check
their timetable and report possible conflicts with other courses.
Absence in Mid Term and Final examination, late delivery of research subject and
project reports will be automatically marked as zero, unless the student presents a
properly documented valid reason.
EVALUATION
POLICIES

COURSE
DESCRIPTION
Machine learning techniques and statistical pattern recognition, supervised learning
(generative/discriminative learning, parametric/non-parametric learning, neural networks,
support vector machines); unsupervised learning (clustering, dimensionality reduction,
kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);
reinforcement learning and adaptive control, applications areas (robotic control, data
mining, autonomous navigation, bioinformatics, speech recognition, and text and web
data processing).
 Demonstrate a systematic and critical understanding of the theories, principles
and practices of computing;
 Creatively apply contemporary theories, processes and tools in the
development and evaluation of solutions to problems in machine learning;
 Actively participate in, reflect upon, and take responsibility for, personal
learning and development, within a framework of lifelong learning and
continued professional development;
 Present issues and solutions in appropriate form to communicate effectively
with peers and clients from specialist and non-specialist backgrounds;
 Work with minimum supervision, both individually and as a part of a team,
demonstrating the interpersonal, organisation and problem-solving skills
supported by related attitudes necessary to undertake employment.
7.
INTENDED
LEARNING
OUTCOMES
8.
LEARNING
STRATEGY
9.
SCHEDULE OF LECTURES AND READINGS
3
Week 6
3
Week 7
3
Week 8
3
Week 9
Week 10
3
3
Week 11
3
Week 12
Week 13
Week 14
Week 15
3
3
3
3
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
Lectures
5.
Week 5
Concept of Learning
Bayesian Learning,
Computational Learning Theory
Machine learning techniques and statistical
pattern recognition
supervised learning (generative/discriminative
learning, parametric/non-parametric learning,
neural networks)
supervised learning (support vector
machines)
unsupervised learning (clustering,
dimensionality reduction, kernel methods)
learning theory (bias/variance tradeoffs; VC
theory; large margins)
Midterm
reinforcement learning and adaptive control
applications areas (robotic control, data
mining, autonomous navigation,
bioinformatics, speech recognition, and text
and web data processing).
Evaluation Hypotheses
Decision Tree Learning
Presentation
Presentation
Reading
3.
4.
3
Teaching Methods
2.
Week 4
Topic
Du and Swamy, Neural Networks in a Softcomputing
Framework, Springer-Verlag London Limited, 2006.
Sebe, Cohen, Garg and Huang, Machine Learning in
Computer
Vision, Springer, 2005.
Chow and Cho, Neural Networks and Computing, Imperial
College Press, 2007.
Mitchell T., Machine Learning, McGraw Hill, 1997.
Week 1
Week 2
Week 3
Class
Hours
3
3
3
1.
Date
1. Interactive lectures and communications with students
2. Discussions and group works
3. Presentations
Plagiarism Notice: Plagiarism is a serious academic offense. Plagiarism is a form of cheating in which a student tries to
pass off someone else's work or part of it as his or her own. It usually takes the form of presenting thoughts, terms, phrases,
passages from the work of others as one's own. When it occurs it is usually found in essays, research papers or term
papers. Typically, passages or ideas are 'lifted' from a source without proper credit being given to the source and its author.
To avoid suspicion of plagiarism you should use appropriate references and footnotes. If you have any doubt as to what
constitutes plagiarism you should consult your instructor. You should be aware that there are now internet tools that allow
each submitted paper to be checked for plagiarism. Remember plagiarism is serious and may result in a reduced or failing
grade or other disciplinary actions.
Cheating: Cheating in any form whatsoever is unacceptable and will subject you to IBU disciplinary procedures. Cheating
includes signing in others for attendance, exams or anything else; using prohibited electronic and paper aides; having others
do your work; having others do your work, copying from others or allowing others to copy from you etc. Please do not cheat
in any way! Please consult me if you have any questions.
Presentation
Research Topics:
1. Linear Methods for Classification
Linear Regression
Logistic Regression
Linear Discriminat Analysis
Perceptron
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
Adnan Hodzic
Kernel Smoothing Methods Ref5
Rijad Halilovic
Kernel Density Estimation and Classification (Naive Bayes)
Mixture Models for Density Estimation and Classification
Radial Basis Function Networks - Ref1
Sabehata Dogic
Basis Function Networks for Classification – Ref3
Advanced Radial Basis Function Networks– Ref3
Fundamentals of Machine Learning and Softcomputing –Ref1
Neural Networks Ref5
Zerina Masetic
Multilayer Perceptrons- Ref1
Hopfield Networks and Boltzmann Machines - Ref1
SVM Ref5
Vahidin
KNN Ref5
Jasmin Kurti
Competitive Learning and Clustering - Ref1
Hacer Konakli
Unsupervised Learning k means Ref5
Self-organizing Maps– Ref3
Sevde
Principal Component Analysis Networks (PCA, ICA)- Ref1 Mihret Sarac
Fuzzy Logic and Neurofuzzy Systems - Ref1
Samed Jukic
Evolutionary Algorithms and Evolving Neural Networks (PSO) - Ref1 Samila
Discussion and Outlook (SVM, CNN, WNN) - Ref1
Decision Tree Learning Duda&Hart
Zeynep Kara
Random Forest Ref5
Nafia
PROBABILISTIC CLASSIFIERS-REF2
Suleyman
SEMI-SUPERVISED LEARNING-REF2
MAXIMUM LIKELIHOOD MINIMUM ENTROPY HMM-REF2
MARGIN DISTRIBUTION OPTIMIZATION-REF2
LEARNING THE STRUCTURE OF BAYESIAN NETWORK CLASSIFIERS-REF2
OFFICE ACTIVITY RECOGNITION-REF2
Model Assessment and Selection REF5
Armin Spahic
Cross-Validation
Bootstrap Methods
Performance ROC, statistic
Olcay
WEKA Machine Learning Tool
TANGARA Machine Learning Tool
ORANGE Machine Learning Tool
NETICA Machine Learning Tool
RAPID MINER Machine Learning Tool
Minimum 15 pages word document, related PPT and presentation