MACHINE LEARNING

AIM

To be able to apply and evaluate machine learning techniques.

CONTENT

This course contains; Elements of machine learning,Regression,Basics of classification,Bayesian classifier,Logistic regression,Support vector machines,Neural networks,Convolutional neural networks,Decision trees,Ensemble methods,Feature selection,Principal component analysis,Clustering,Model evaluation.

1. 1

   Understands regression techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

2. 2

   Applies regression techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

3. 3

   Evaluates regression techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

4. 4

   Understands classification techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

5. 5

   Applies classification techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

6. 6

   Evaluates classification techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

7. 7

   Understands unsupervised machine learning techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

8. 8

   It applies unsupervised machine learning techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

9. 9

   Understands feature selection / analysis techniques.

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

10. 10

    Applies feature selection/analysis techniques.

    Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

WEEKLY PLAN

1. WEEK 1

   Elements of machine learning

2. WEEK 2

   Regression

3. WEEK 3

   Basics of classification

4. WEEK 4

   Bayesian classifier

5. WEEK 5

   Logistic regression

6. WEEK 6

   Support vector machines

7. WEEK 7

   Neural networks

8. WEEK 8

   Convolutional neural networks

9. WEEK 9

   Decision trees

10. WEEK 10

    Ensemble methods

11. WEEK 11

    Feature selection

12. WEEK 12

    Principal component analysis

13. WEEK 13

    Clustering

14. WEEK 14

    Model evaluation

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Bishop, “Pattern Recognition and Machine Learning,” Springer, (1st edition) Duda, Hart, and Stork, “Pattern Classification,” Wiley-Interscience, (2nd edition)

TEACHING STAFF

• Prof.Dr. Bahadır Kürşat GÜNTÜRKCOORDINATOR
• Prof.Dr. Bahadır Kürşat GÜNTÜRK