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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS BST 6005 ARTIFICAL INTELLIGENCE FOR BIOLOGICAL APPLICATIONS ELECTIVE 3 0 0 12

Offered By

Biomedicine and Health Technologies

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSISTANT PROFESSOR EZGI KARACA EREK

Offered to

Biomedicine and Health Technologies

Course Objective

To gain a thorough understanding of the artificial intelligence (AI) methods and tools for manipulating, analyzing and interpreting biological data.

Learning Outcomes of the Course Unit

1   Familiarity with state-of-the AI methods 2   Familiarity with state-of-the AI applications in biology 3   Being able understand and discuss pattern recognition in biology 4   Being able understand and discuss neural network applications in biology 5   Usage of data mining tools to extract information from open source biological data 6   Incorporate different AI methods to provide with a unified treatment of biological problems 7   Knowing all available learning algorithms in biology 8   In silico coding of AI tools for biological applications 9   Being able to discuss the capabilities and limitations of current AI applications in biology 10   Presentation of research findings

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 What is Machine Learning/Artificial Intelligence 2 Supervised Learning 3 Bayesian Decision Theory 4 Parametric Methods 5 Multivariate Methods 6 Dimensionality Reduction 7 Clustering 8 Midterm 9 Nonparametric Methods 10 Decision Trees 11 Linear Discrimination 12 Multilayer Perceptron 13 Local and Hidden Markov Models 14 Assessing and Comparing Classification Algorithms 15 Assignment Presentations 16 Final Presentations

Recomended or Required Reading

Introduction to Machine Learning Ethem ALPAYDIN The MIT Press, October 2004, ISBN 0-262-01211-1

Planned Learning Activities and Teaching Methods

Oral presentation, literature search and discussion

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 MTE MIDTERM EXAM 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.30 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.30 + MTE * 0.30 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Theoretical knowledge will be evaluated by exam

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Ezgi.karaca@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Tutorials 0 0 0 Preparations before/after weekly lectures 14 8 112 Preparation for midterm exam 1 20 20 Preparation for final exam 1 25 25 Preparing assignments 2 35 70 Preparing presentations 1 30 30 Midterm 1 5 5 Final 1 5 5 TOTAL WORKLOAD (hours) 309

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 LO.1 5 5 5 LO.2 5 5 5 5 5 LO.3 5 5 5 LO.4 5 5 5 LO.5 5 5 5 5 5 LO.6 5 5 5 5 5 LO.7 5 5 5 LO.8 5 5 5 5 5 5 5 LO.9 5 5 5 5 5 LO.10 5 5 5 5 5