Machine Learning course, Sharif University of Technology, Fall 2022
Instructor: Reza Rezazadegan
TAs: Ali Baghri, Qazal Farahani, Javad Sajadi, Melika Nasirian
Github: github.com/rezareza007/MLcourse
Prerequisites: linear algebra, multivariable calculus, basics of probability theory, basic familiarity with Python
Texts:
Reza Rezazadegan, Applications of Artificial Intelligence and Big Data in Industry 4.0 Technologies, in Industry 4.0 Vision for Energy and Materials: Enabling Technologies and Case Studies, Wiley, 2022
Aurelien Geron, Hands-on Machine Learning with Scikit-Learn
Blum, et al, Foundations of Data Science
Course evaluation: student project and presentation
Syllabus
1-Introducing AI and Machine Learning: AI as function approximation (slides)
2-Regression (slides)
3-Introduction to classification (slides)
4-Classification methods: Support Vector Machines (slides)
5-Bayesian learning: the naive Bayes classifier (slides)
6-Classification methods: Decision trees (slides)
7-Unsupervised learning: clustering (k-means, Agglomerative, DBSCAN) (slides)
8-Unsupervised learning: dimensionality reduction (slides)
9-Ensemble Learning (slides)
10-Topological Data Analysis
11-Evolutionary optimization
12-Rule-based Machine Learning: Learning classifier systems
13-Time series forecasting