BIN785 - ADVANCED MACHINE LEARNING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ADVANCED MACHINE LEARNING | BIN785 | Any Semester/Year | 2 | 2 | 3 | 9 |
| Prequisites | - | |||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Preparing and/or Presenting Reports | |||||
| Instructor (s) | Prof. Erdem Karabulut | |||||
| Course objective | Basic concepts of Machine Learning (ML), identifying complex patterns /structures in data and developing automatic learning algorithms needed to make decisions based on the data will be discussed. Usage of supervised- unsupervised and bayesian learning algorithms and their applications in Bioinformatic studies will be discussed. | |||||
| Learning outcomes |
| |||||
| Course Content | Basic Concepts in Machine Learning Bootstrap and Cross-validation Bagging ve Boosting Algorithms Artificial Neural Networks Deep Learning Naive Bayes Method Genetic Algorithms Kohonen Map Basic Concepts of Image Mining Microarray Data and Machine Learning, Unsupervised Based Classification | |||||
| References | 1.Aidong Zhang, Advanced Analysıs Of Gene Expressıon Mıcroarray Data, World Scientific Publishing Co. Pte. Ltd, 2006 2.Mei-Lıng Tıng Lee ,Analysıs of Mıcroarray Gene Expressıon Data, Kluwer Academic Publishers, 2004 3.David Posada (Editor), Bioinformatics for DNA Sequence Analysis Human, A Press, Springer Science, Business Media, 2009 4.Ilya Shmulevich, Wei Zhang (Editors), Computatıonal And Statistical Approaches To Genomics, Second Edition, Springer Science+Business Media, Inc.,2006 5.Rongling Wu, George Casella, Chang-Xing Ma, Statistical Genetics of Quantitative Traits, Linkage, Maps, and QTL, Springer Science + Business Media, LLC, 2007 6.Peter Schattner, Genomes, Browsers, and Databases,Cambridge University Press, 2008 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basic Concepts in Machine Learning |
| Week 2 | Bootstrap and Cross-validation |
| Week 3 | Bagging ve Boosting Algorithms |
| Week 4 | Artificial Neural Network-1 |
| Week 5 | Artificial Neural Network-2 |
| Week 6 | Deep Learning |
| Week 7 | Naive Bayes Method |
| Week 8 | Midterm exam |
| Week 9 | Genetic Algorithms |
| Week 10 | Kohonen Map |
| Week 11 | Basic Concepts of Ä°mage Mining |
| Week 12 | Midterm exam |
| Week 13 | Microarray Data and Machine Learning |
| Week 14 | Unsupervised Based Classification |
| Week 15 | Preparation to final exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 2 | 10 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 40 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 4 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 2 | 28 |
| Laboratory | 0 | 0 | 0 |
| Application | 14 | 2 | 28 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 8 | 112 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 2 | 2 | 4 |
| Midterms (Study duration) | 2 | 20 | 40 |
| Final Exam (Study duration) | 1 | 58 | 58 |
| Total Workload | 47 | 92 | 270 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. He/She should use electronical databases; such as Science Direct, PubMed, ISI, published books and periodical publications properly. | X | ||||
| 2. He/She will know basic bioinformatics analysis methods and use them properly in research. | X | ||||
| 3. Since bioinformatics is an interdisciplinary branch of science, he/she will be able to a part of group work, have good communication skills, and will understand others' problems. | X | ||||
| 4. He/She should have internet utilization skills enough to follow the innovations in the field and access desired information, accessing library sources should be advanced. | X | ||||
| 5. He/She will prepare projects for his/her technical-scientific development, provide consultancy service for seminars and genetic analyses, attend article discussions, congresses and workshops. | X | ||||
| 6. /She will be able to follow recent developments in other research fields also. | X | ||||
| 7. He/She will use programming languages, such as R, Phyton and Linux and uses Bioinformatics tools like Bioconductor, BLAST, PLINK, GATK etc. and will know the logic of basic programming. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest