ELECTRICAL AND ELECTRONICS ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5571 Machine Learning for Bioinformatics I Spring 3 0 3 6
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level: Bachelor
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi ZAFER İŞCAN
Course Objectives: This course surveys fundamental concepts in machine learning along with current applications in bioinformatics.

Learning Outputs

The students who have succeeded in this course;
1. Describe essential bioinformatics applications and tools
2. Understand fundamental concepts in machine learning
3. Understand unsupervised learning methods
4. Understand supervised learning and classification methods
5. Understand dimension reduction methods
6. Understand how machine learning concepts are applied to various bioinformatics problems
7. Learn a machine learning software

Course Content

1. Introduction to bioinformatics
2. Unsupervised learning methods: probability density estimation, clustering, self organizing maps
3. Supervised learning methods: Bayes classifier, discriminant analysis, k-nearest neighbors, regression, random forest
4. Dimension reduction methods: PCA, multi-dimensional scaling
5. Microarrays and gene expression

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to bioinformatics, brief history of bioinformatics, database applications in bioinformatics web tools and services, pattern analysis, contribution of information theory
2) Introduction to machine learning, supervised, unsupervised, reinforcement and semi-supervised learning, no free lunch theorem, ugly duckling theorem, Occam's razor, overfitting, cross-validation, bootstrap
3) Introduction to unsupervised learning, brief review of probability theory, probability density estimation, histogram approach, parametric approach, non-parametric approach: K-nearest neighbor, kernel approach
4) Dimension reduction, principal component analysis, independent component analysis, multi-dimensional scaling, application of the Sammon algorithm to the gene data
5) Cluster analysis, hierarchical clustering, K-means clustering, Fuzzy C-means, Gaussian mixture models, application of clustering algorithms to gene expression data
6) Self organising map, vector quantization, SOM structure, SOM learning algorithm, Using SOM for classification, bioinformatics applications of VQ and SOM
7) Introduction to supervised learning, general concepts and definition, model evaluation, data organization
8) Bayes rule for classification, minimum error rate classification, discriminant functions, Bayesian belief networks
9) Linear and quadratic discriminant analysis, generalized discriminant analysis, K-nearest neighbors and application to gene data analysis
10) Classification and regression trees, CART for compound pathway involvement prediction, random forest algorithm, analyzing gene expression profiles with random forest
11) Feature selection, built-in strategy, lasso regression, ridge regression, partial least squares algorithm, exhaustive strategy
12) Feature selection (cont'd), heuristic strategy: orthogonal least square approach, criteria for feature selection, correlation measure, Fisher ratio measure, mutual information measure
13) Feature extraction, biological data coding: molecular sequences, chemical compounds, sequence analysis
14) Project presentations

Sources

Course Notes: 1. Machine Learning Approaches to Bioinformatics, Zheng Rong Yang, World Scientific Publishing Company, 2011.
References: 1. Data Mining for Bioinformatics, Sumeet Dua, CRC Press, 2013. 2. Bioinformatics: The Machine Learning Approach, Pierre Baldi and Soren Brunak, 2nd edition, MIT Press, 2001. 3. Pattern Recognition and Machine Learning, Christopher M. Bishop, 2nd printing edition, Springer, 2011. 4. Pattern Recognition, Sergios Theodoridis, Konstantinos Koutroumbas, Academic Press, 4th edition, 2008. 5. Introduction to Pattern Recognition: A Matlab Approach, Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, Dionisis Cavouras, Academic Press, 2010. 6. Pattern Classification, Richard O. Duda, Peter E. Hart, David G. Stork, 2nd edition, Wiley-Interscience, 2000.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 15
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 1 % 30
Seminar % 0
Midterms 1 % 15
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 16 2 32
Presentations / Seminar 0 0 0
Project 8 10 80
Homework Assignments 5 6 30
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 3 3
Paper Submission 0 0 0
Jury 0 0 0
Final 1 3 3
Total Workload 190

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Adequate knowledge in mathematics, science and electric-electronic engineering subjects; ability to use theoretical and applied information in these areas to model and solve engineering problems.
2) Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4) Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively.
5) Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems.
6) Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing.
8) Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9) Awareness of professional and ethical responsibility.
10) Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11) Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.