Lecture 2 – Metrics, distance measures, classification criteria
Independent component analysis · Metrikler · Mesafe ölçümü ve metrikler · Metrik · KL-Divergence · Jenson-Shannon div
Fundamentals of Machine Learning ELM472 Lecture Notes
Makine Öğrenmesinin Temelleri — ders notları, ödevler, projeler · Gebze Teknik Üniversitesi (GTÜ) Elektronik Mühendisliği
Supervised and unsupervised learning, regression and classification, Bayesian decision theory, dimensionality reduction, clustering and anomaly detection, and an introduction to neural networks.
Gözetimli ve gözetimsiz öğrenme, regresyon ve sınıflandırma, Bayesçi karar teorisi, boyut azaltma, kümeleme ve anomali tespiti, yapay sinir ağlarına giriş.
- Semester 7 · Fall 2024–25
- 11 lecture notes
- 11 digital notes
- 1 exam-prep file
- 5 assignments
- 1 project
On this page: General Lecture notes Digital notes (LaTeX) Exam preparation Homework Projects GitHub repositories Textbooks
Most files are in Turkish; file and folder names are shown as they appear in the repository.
General
Lecture notes
Digital notes (LaTeX)
Searchable digital versions of the handwritten notes, typeset in LaTeX (the notes are written in Turkish). Template and build: latex-sablon.
Notice: The digital notes were created from the handwritten notes with the help of Claude (Anthropic); they are meant as a supplementary resource and may contain errors. Always rely on the original notes and the official course materials.
Lecture 3 – Supervised learning, regression, MLE, MMSE and MAP
Supervised learning · Sınıflandırma · Regresyon · 3 adımımız var · Hastalık tespitinde · İlişki kuralları
Lecture 4 – Parametric estimation, MLE, Gaussian Mixtures, EM
Parametric kestirim · MLE · Yeterli istatistik (Sufficient Statistics) · Bernoulli Distrubution · Gauss Dağılımı · Gaussian Mixtures
Lecture 5 – Naive Bayes, kernel density and k-NN
Kitapta non parametrik kısımlara gireceğiz
Lecture 6 – Decision trees, regression trees, ensemble learning
Karar ağaçları , Regresyon ağaçları · Decision tree · Impurity measures · Entropy · Gini Sabiti · Regresyon ağaçları
Lecture 7 – Feature selection, PCA, SVD, LDA and clustering
1 Feature selection · Subset Selection · Principle Component analizi · Feature Embedding · Eigen faces · Singular value composition
Lecture 8 – Perceptron, backpropagation, activation functions and PyTorch
ROC curve · sigmoid · Back Propagation · Derin öğrenmede katmanlar artar
Lecture 9 – CNN, RNN/LSTM, encoding and TF-IDF
Transfer Learning · Explainable AI · Encoding
Lecture 10 – NLP: Tokenization, TF-IDF, Word2Vec
NLP (Natural language processing) · Tokenization · Word2Vec · CBOW · Skip gram / n-gram · Global vectors for word Representation (Glove)
Final exam study notes
Karar ağaçları (Decision tree) · Saflık ölçütleri (Impurity measures) · Regresyon ağaçları · Avantajları · Dezavantajları · Ensemble Learning
Homework
Projects
Anomaly detection in cities (trajectory clustering)
GitHub repositories
Textbooks
Sources used in the course (the books themselves are not published because of copyright):
- E. Alpaydın — Introduction to Machine Learning, 3rd ed., MIT Press, 2014
- R. O. Duda, P. E. Hart, D. G. Stork — Pattern Classification, 2nd ed., Wiley, 2001
- H. Brink, J. W. Richards, M. Fetherolf — Real-World Machine Learning, Manning
Other courses this semester
Image Processing · RF System Design for Communications
Open the folder on GitHub← Semester 7
Course materials belong to their instructors and GTU and are shared for educational purposes only.