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- Machine Learning The Art and Science of Algorithms that Make Sense of Data, Peter Flach, 2012.
6- 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
- Unsupervised Learning
- Clustering Clustering1 Clustering2
- PCA
- Cross validation cross
- Anomaly detection
- Reinforcement Learning
Final Project
Course Information
Lecture Notes
