TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| COMPUTER ENGINEERING PR. (ENGLISH) | |
| Code | CEN426 |
| Name | Introduction to Machine Learning |
| Term | 2019-2020 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. UMUT ORHAN |
| Course Instructor |
Prof. Dr. UMUT ORHAN
(Bahar)
(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
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows classification and prediction |
| LO02 | Bilgisayar temelli bir çalışmada bir datayı kullanmayı bilir |
| 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 | - | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 5 |
| PLO02 | - | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 5 |
| PLO03 | - | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 4 |
| PLO04 | - | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 4 |
| PLO05 | - | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 5 |
| PLO06 | - | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 3 |
| PLO07 | - | Can access information,gains the ability to do resource research and uses information resources | 3 |
| PLO08 | - | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 3 |
| PLO09 | - | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 3 |
| PLO10 | - | Professional and ethical responsibility, | 0 |
| PLO11 | - | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | 0 |
| PLO12 | - | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues | 0 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Course | Reading related chapter in lecture notes | |
| 2 | A Fast Matlab Review | Reading related chapter in lecture notes | |
| 3 | Instance based Learning, Supervised and Unsupervised Learning | Reading related chapter in lecture notes | |
| 4 | K-Means Clustering, Classification by K-Nearest Neighbor | Reading related chapter in lecture notes | |
| 5 | Entropy, Decision Tree Learning, ID3 and C4.5 algorithms, Classification and Regression Trees | Reading related chapter in lecture notes | |
| 6 | Probability and Conditional Probability, Bayesian Theorem, Naive Bayes Classifier, Categorical and Numerical Data | Reading related chapter in lecture notes | |
| 7 | Linear Regression, Multiple Linear Regression, Least Squares Method, Thresholding and Competitive Classification | Reading related chapter in lecture notes | |
| 8 | Midterm Exam | Study to lecture notes and apllications | |
| 9 | Introduction to Artificial Neural Networks, Perceptron, Adaline, Least Mean Squares | Reading related chapter in lecture notes | |
| 10 | Back-propagation Algorithm, Multi-Layer Perceptron Network | Reading related chapter in lecture notes | |
| 11 | Reinforcement Learning, Q-Learning, TD-Learning, Learning Vector Quantization Network, LVQ2, LVQ-X | Reading related chapter in lecture notes | |
| 12 | Mapping and Kernel Functions, Radial Basis Function (RBF), RBF Network | Reading related chapter in lecture notes | |
| 13 | Lagrange Method, Optimization by Lagrange Coefficient, Support Vector Machine, Quadratic Programming | Reading related chapter in lecture notes | |
| 14 | Feature Extraction and Selection, Dimension Reduction, Principal Component Analysis, Linear Discriminant Analysis | Reading related chapter in lecture notes | |
| 15 | Review for Final Exam | Reading related chapter in lecture notes | |
| 16 | Final Exam | Study to lecture notes and apllications | |
| 17 | Final Exam | Study to lecture notes and apllications |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Project / Design | 100 | 20 |
| General Assessment | ||
| Midterm / Year Total | 100 | 20 |
| 1. Final Exam | - | 80 |
| Grand Total | - | 100 |
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 | ||