CENG0050 Advanced Machine Learning

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

Information

Code CENG0050
Name Advanced Machine Learning
Term 2022-2023 Academic Year
Semester . Semester
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 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 Öğretim Yöntemleri:
Anlatım
2 Introduction to machine learning Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
3 Distance-based Clustering and Classification: K-Means and K-NN Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
4 Entropy-based Decision Trees: ID3 and C4.5 Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
5 Probability, Bayesian Theorem, Naive Bayes Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
6 Least squares optimization and linear regression Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
7 Introduction to Artificial Neural Networks: Perceptron and Adaline Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Study to all lecture notes Ölçme Yöntemleri:
Yazılı Sınav
9 Multi-layered artificial neural networks and Backpropagation Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
10 Reinforcement Learning: Q and TD Learning, LVQ Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
11 Mapping and Kernel Functions: RBF Networks Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
12 Optimization by Lagrange Method: Support Vector Machine Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
13 Dimension Reduction: PCA and LDA Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Anlatım
14 Fuzzy Logic and Fuzzy Inference Systems Reading the related chapter of the lecture notes Öğretim Yöntemleri:
Soru-Cevap
15 Project Presentations Preparing a presentation and an application about project subject Ölçme Yöntemleri:
Proje / Tasarım
16 Term Exams Study to all lecture notes Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Study to all lecture notes 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: 21.11.2022 07:34