ML4032
Machine Learning
Course Materials
Grading:
Projects/Programming Assignments: Quizzes, Class Participation and Attendance:
Midterms: Midterm Date: Final:
Text Books:
1- Introduction to Machine Learning, Ethem Alpaydin, 2014.
2- Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
3- Machine Learning A Probabilistic Perspective, Kevin P. Murphy, 2012.
4- Introduction to Machine Learning withPython, Andreas C. Müller & Sarah Guido, 2016.
5- Mathematics for machine learning. Deisenroth, M. P., Faisal, A. A., & Ong, C. S., Cambridge University Press. 2020.
6- Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques ... , Géron, A. 2022.
7- An Introduction to Machine Learning, Miroslav Kubat, Second Edition, 2017.
- Supervised Learning
- Linear Regression codes
- Classification:
- Logistic Regression
- SVM (Support Vector Machine)
- Decision Tree
- Naive Bayes Probability
- Performance Metrics, LearningCurve, ValidationCurve
- Unsupervised Learning
- Clustering:
Clustering1,
Clustering2,
DBSCAN, (link: chapter 9)
GMM, EM Algorithm (link: chapter 11) (link: chapter 9) - PCA
- Ensemble Methods: PPT, chapter 7 of ref. [6] , link2
- Cross validation cross
- Anomaly detection link1, link2
- Recommender System: Matrix Factorization
- Reinforcement Learning
Final Project
Course Information
Lecture Notes
