درس‌ها

درس یادگیری ماشینی، پاییز 1401

Machine Learning course, Sharif University of Technology, Fall 2022

Instructor: Reza Rezazadegan

TAs: Ali Baghri, Qazal Farahani, Javad Sajadi, Melika Nasirian

Githubgithub.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

Student presentation topics

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

Selected student presentations

author-avatar

About Reza Rezazadegan

استاد ریاضیات کاربردی و علوم کامپیوتر دانشگاه شیراز پستداک هوش مصنوعی دانشگاه شریف پژوهشگر سابق موسسه بایوکامپلکسیتی دانشگاه ویرجینیا

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