Artificial Intelligence: The Application of Machine Learning in Decision Systems, Network Security and Computer Clusters

Course 6: Artificial Intelligence: The Application of Machine Learning in Decision Systems, Network Security and Computer Clusters

I. Course Description

The course explores in depth cutting-edge technologies in machine learning, online learning, reinforcement learning and the Internet of Things, and aims to provide students with comprehensive skills from theory to practice. The content covers core issues such as maximum likelihood estimation (MLE) and maximum posterior posterior estimation (MAP), hypothesis testing and classification, online learning and multi-arm gambling machines, reinforcement learning, and the management of large-scale computing clusters. Students will also learn how to apply machine learning to cybersecurity, improve intrusion detection and defense capabilities, as well as how to design smart devices and optimize decisions in an AI-driven Internet of Things environment.

The course goal is to help students master key machine learning techniques and algorithms, including estimation methods, classification, and online learning strategies, to develop their ability to make optimal decisions in a dynamic environment. Through a deep understanding of reinforcement learning and computing cluster management, students will have the ability to process large-scale data and complex systems, and can apply machine learning to innovative practices to improve network security and IoT applications.

II. Professor Introduction

Osman Yagan – Professor at Carnegie Mellon University

Professor Osman Yagan is a professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, and is a core member of the CyLab Institute for Security and Privacy. He also acted for the Department of Software and Social Systems at the School of Computer Sciences. He has made important contributions to the fields of network science, wireless communications and Internet of Things security, focusing to optimizing the design and security of complex network systems.

Professor Yagans research excellence has been recognized in early career fellowships from the Dean of the Carnegie Mellon School of Engineering. He is also a senior member of the Institute of Electrical and Electronics Engineers (IEEE), demonstrating his remarkable achievements and international influence in academic and engineering research.

III. Syllabus

1. MLE / MAP, the estimator
2. hypothesis testing / classification
3. online learning /Bandits1
4. online learning /Bandits2
5. reinforcement learning 1
6. reinforcement learning 2
7. Manage the server group / computing cluster 1
8. Manage the server group / computing cluster 2
9. Machine learning in network security
10. The Internet of Things in the era of artificial intelligence