CEN426 Introduction to Machine Learning

6 ECTS - 3-0 Duration (T+A)- 8. Semester- 3 National Credit

Information

Code CEN426
Name Introduction to Machine Learning
Term 2024-2025 Academic Year
Semester 8. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. UMUT ORHAN
Course Instructor
1 2
Prof. Dr. UMUT ORHAN (A Group) (Ins. in Charge)


Course Goal / Objective

In this course, the theoretical and practical foundations of machine learning methods are examined and by these methods, a solution finding to pattern recognition problems is aimed.

Course Content

Instance-Based Learning; supervised and unsupervised learning; Decision Tree Learning; Bayesian Learning; Artificial Neural Networks: feed-forward and feedback paradigms; Assesing, Comparing and Combining Learning Algorithms; Feature Extraction and Dimension Reduction; Principal Component Analysis; Linear Discriminant Analysis.

Course Precondition

none

Resources

T. Mitchell, Machine Learning, McGraw-Hill, 1997. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007. S. Haykin, Neural Networks and Learning Machines, Prentice Hall, 2008. R. O. Duda, Pattern Classification, Wiley-Interscience, 2000.

Notes

papers


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Knows classification and prediction
LO02 Knows to use a data in computer based study
LO03 Knows the computation of machine learning methods
LO04 Applies machine learning methods to problems


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. 4
PLO02 Bilgi - Kuramsal, Olgusal Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. 3
PLO03 Bilgi - Kuramsal, Olgusal Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. 3
PLO04 Bilgi - Kuramsal, Olgusal Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. 4
PLO05 Bilgi - Kuramsal, Olgusal Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. 3
PLO06 Bilgi - Kuramsal, Olgusal Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. 2
PLO07 Bilgi - Kuramsal, Olgusal Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. 3
PLO08 Bilgi - Kuramsal, Olgusal Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself. 3
PLO09 Bilgi - Kuramsal, Olgusal Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice.
PLO10 Bilgi - Kuramsal, Olgusal Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development. 4
PLO11 Bilgi - Kuramsal, Olgusal Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. 3


Week Plan

Week Topic Preparation Methods
1 Introduction to Course Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
2 A Fast Matlab Review Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
3 Instance based Learning, Supervised and Unsupervised Learning Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
4 K-Means Clustering, Classification by K-Nearest Neighbor Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
5 Entropy, Decision Tree Learning, ID3 and C4.5 algorithms, Classification and Regression Trees Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
6 Probability and Conditional Probability, Bayesian Theorem, Naive Bayes Classifier, Categorical and Numerical Data Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
7 Linear Regression, Multiple Linear Regression, Least Squares Method, Thresholding and Competitive Classification Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
8 Midterm Exam Study to lecture notes and apllications Ölçme Yöntemleri:
Yazılı Sınav
9 Introduction to Artificial Neural Networks, Perceptron, Adaline, Least Mean Squares Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
10 Back-propagation Algorithm, Multi-Layer Perceptron Network Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
11 Reinforcement Learning, Q-Learning, TD-Learning, Learning Vector Quantization Network, LVQ2, LVQ-X Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
12 Mapping and Kernel Functions, Radial Basis Function (RBF), RBF Network Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
13 Lagrange Method, Optimization by Lagrange Coefficient, Support Vector Machine, Quadratic Programming Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
14 Feature Extraction and Selection, Dimension Reduction, Principal Component Analysis, Linear Discriminant Analysis Reading related chapter in lecture notes Öğretim Yöntemleri:
Anlatım
15 Review for Final Exam Reading related chapter in lecture notes Öğretim Yöntemleri:
Soru-Cevap
16 Final Exam Study to lecture notes and apllications Ölçme Yöntemleri:
Yazılı Sınav
17 Final Exam Study to lecture notes and apllications Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 11.05.2024 09:03