| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| EEE5572 | Machine Learning for Bioinformatics II | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| 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. |
|
The students who have succeeded in this course; 1. Understand fundamental concepts in machine learning 2. Understand supervised learning and classification methods 3. Understand how machine learning concepts are applied to various bioinformatics problems 4. Describe essential bioinformatics applications and tools 5. Learn a machine learning software |
| 1. Neural networks 2. Vector machines 3. Hidden Markov models 4. Dynamic Bayesian networks 5. Protein structure and function prediction 6. Mass spectrometry 7. Gene networks |
| Week | Subject | Related Preparation | |
| 1) | Neural networks, multi-layer perceptrons, parameterization, learning rules | ||
| 2) | Neural networks (cont'd), learning algorithms, regression, classification | ||
| 3) | Neural networks (cont'd), applications in bioinformatics | ||
| 4) | Vector machines, basis function approach, radial basis function neural network, bio-basis function neural network | ||
| 5) | Vector machines (cont'd), support vector machines, relevance vector machines | ||
| 6) | Vector machines (cont'd), applications in bioinformatics | ||
| 7) | Hidden Markov models, learning, decoding | ||
| 8) | Dynamic Bayesian networks | ||
| 9) | Applications of hidden Markov models and dynamic Bayesian networks in bioinformatics | ||
| 10) | Gene networks, discrete Bayesian networks, inference with discrete Bayesian networks, learning a discrete Bayesian network | ||
| 11) | Gene networks (cont'd), causal networks, graphs, applications in bioinformatics, gene regulatory networks | ||
| 12) | Protein structure and function prediction | ||
| 13) | Mass spectrometry | ||
| 14) | Project presentations | ||
| 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. |
| 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 | |
| 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 | ||
| Midterms | 1 | 3 | 3 |
| Paper Submission | 0 | ||
| Jury | 0 | ||
| Final | 1 | 3 | 3 |
| Total Workload | 190 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |