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Information
| Code | CENG0050 |
| Name | Advanced Machine Learning |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
In this course, optimization basis of artificial intelligent algorithms like artificial neural networks and support vector machine and the applications on their solutions is aimed.
Course Content
K-Means, K-NN, Decision trees ID3, C4.5, Bayesian and Naive Bayes , Least squares and linear regression, Perceptron, Adaline, Least Mean Squares, Levenberg- Marquartd and artificial neural networks, Reinforcement Learning, Q-Learning, TD-Learning, Learning Vector Quantization Network, Radial Basis Function Network, Lagrange Method and Support Vector Machine, Principal Component Analysis, Linear Discriminant Analysis, Fuzzy Logic and Fuzzy Inference System.
Course Precondition
none
Resources
How to Solve It: Modern Heuristics, Z. Michalewicz, D. B. Fogel, Springer, 2004. Intelligent Optimization Techniques, D.T. Pham, D. Karaboga, Springer, 1999. Pattern Recognition and Machine Learning, C. M. Bishop, Springer, 2007. Neural Networks and Learning Machines, S. Haykin, Prentice Hall, 2008.
Notes
Papers
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Calculate distance based measures |
| LO02 | Analyse dataset by splitting it |
| LO03 | Labels a problem as regression or classification |
| LO04 | Selects the machine learning method suitable for the difficulty of the problem |
| LO05 | Reports results by analyzing data |
| LO06 | Compares method successes on data |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 5 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 2 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 3 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 3 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Python | Reading the related chapter of the lecture notes | |
| 2 | Introduction to machine learning | Reading the related chapter of the lecture notes | |
| 3 | Distance-based Clustering and Classification: K-Means and K-NN | Reading the related chapter of the lecture notes | |
| 4 | Entropy-based Decision Trees: ID3 and C4.5 | Reading the related chapter of the lecture notes | |
| 5 | Probability, Bayesian Theorem, Naive Bayes | Reading the related chapter of the lecture notes | |
| 6 | Least squares optimization and linear regression | Reading the related chapter of the lecture notes | |
| 7 | Introduction to Artificial Neural Networks: Perceptron and Adaline | Reading the related chapter of the lecture notes | |
| 8 | Mid-Term Exam | Study to all lecture notes | |
| 9 | Multi-layered artificial neural networks and Backpropagation | Reading the related chapter of the lecture notes | |
| 10 | Reinforcement Learning: Q and TD Learning, LVQ | Reading the related chapter of the lecture notes | |
| 11 | Mapping and Kernel Functions: RBF Networks | Reading the related chapter of the lecture notes | |
| 12 | Optimization by Lagrange Method: Support Vector Machine | Reading the related chapter of the lecture notes | |
| 13 | Dimension Reduction: PCA and LDA | Reading the related chapter of the lecture notes | |
| 14 | Fuzzy Logic and Fuzzy Inference Systems | Reading the related chapter of the lecture notes | |
| 15 | Project Presentations | Preparing a presentation and an application about project subject | |
| 16 | Term Exams | Study to all lecture notes | |
| 17 | Term Exams | Study to all lecture notes |
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 | ||