Graduate / Advanced Course

Special Topics in Artificial Intelligence

An advanced, applied course on modern artificial intelligence. It covers advanced predictive modeling, sequential data analysis, anomaly detection, generative approaches, optimization, and data-efficient learning. The course combines conceptual understanding with practical applications, case analysis, and project-based work.

📣 Course Status

The course website is live. Lecture materials will be released week by week during the semester. Check the Schedule page for the current status of each week.

Course Overview

Artificial intelligence is increasingly used to support prediction, automation, interpretation, and decision-making across engineering and industrial domains. This course helps students move beyond introductory machine learning: you will learn to understand advanced AI concepts and to evaluate, apply, and communicate AI methods in real-world problem-solving contexts.

The course integrates lectures, hands-on practice, technical discussion, and a semester-long project. Each major topic is connected to practical applications and case studies.

Semester at a Glance

Week Topic
1 Course Introduction and Overview of Applied Artificial Intelligence Lecture
2 Machine Learning Strategy and Best Practices Lecture
3 Deep Learning for Structured and Engineering Data Lecture
4 Time-Series Forecasting and Anomaly Detection Lecture
5 Practical Applications of Deep Learning Practical
6 Practical Applications of Time-Series and Anomaly Detection Practical
7 AI Project Proposal and Planning Project
8 Midterm Examination Exam
9 Hands-on Deep Learning Lab
10 Hands-on Deep Learning II Lab
11 Active Learning and Data-Efficient Artificial Intelligence Lecture
12 Practical Applications of Generative Modeling Practical
13 Practical Applications of Data-Efficient Artificial Intelligence Practical
14 Final Project Presentation and Demonstration Project
15 Final Examination Exam