Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn and gradually improve accuracy. Interestingly, several tools of machine learning are closely related to adaptive control and system identification (e.g., parameter estimation, recursive estimation, online minimization of performance errors). Based on these connections and common concepts, new intersections and opportunities to improve machine learning algorithms naturally arise.

Representative Topics

• Deep learning
• Extreme learning and broad learning
• Recursive learning
• Lifelong learning and continual learning

Representative Applications

• Computer vision, image processing
• Autonomous driving
• Traffic prediction, energy consumption prediction
• Human behavior prediction (for human-in-the-loop applications)
• Simultaneous Localization and Mapping (SLAM)

Representative Publications

• Liu D., Baldi S., Yu W., and Chen C. L. P., “A hybrid recursive implementation of broad learning with incremental features”, IEEE Transactions on Neural Networks and Learning Systems, 2021 (co-first author) doi:10.1109/TNNLS.2020.3043110
• Liu D., Baldi S., Yu W., Cao J., and Huang W., “On training traffic predictors via broad learning structures: a benchmark study”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 52(2), pp. 749-758, 2022. doi:10.1109/TSMC.2020.3006124
• Moerland T. M., Deichler A., Baldi S., Broekens J., and Jonker C. M., “Think neither too fast nor too slow: the computational trade-off between planning and reinforcement learning”, The International Conference on Automated Planning and Scheduling (ICAPS 2020), October 26th-30th, Nancy, France, 2020.