High Performance Computing and Big Data
【日期】 2018/8/14 (二)
【時間】 ■09:10~12:00 ■13:20~16:10
【地點】 計中 106 教室（階梯教室）
【費用】 臺灣大學及國立臺灣大學系統 500 元，其他 1000 元，名額40人
Yu-Chiang Frank Wang 王鈺強
Yu-Chiang Frank Wang received his PhD and MS degrees in ECE from Carnegie Mellon University in 2009 and 2004, respectively. He obtained his BS degree in EE from National Taiwan University in 2001. Before joining Dept. Electrical Engineering at National Taiwan University as an associate professor in 2017, Dr. Wang joined the Research Center for IT Innovation at Academia Sinica as an Assistant Research Fellow in 2009. With research focuses on computer vision, pattern recognition, and machine learning, he has been working in computer vision, in particular on the topics of robust face recognition and domain adaptation. Dr. Wang was promoted to an Associate Research Fellow in 2013, and also served as a Deputy Director of our center since 2015.
In Aug. 2017, Dr. Wang joins the Department of Electrical Engineering at National Taiwan University as an associate professor. Leading the Vision and Learning Lab at NTU, Dr. Wang focuses on a range of computer vision and deep learning topics, including image segmentation, object recognition, video analysis and summarization. Dr. Wang and his team received the First Place Award at Taiwan Tech Trek by the National Science Council (NSC) of Taiwan in 2011. In 2013 and 2017, he was twice selected as the Outstanding Young Researcher by NSC/MOST (Ministry of Science and Technology). In 2018, his team won the Second Place Award for the research poster presentation at NVIDIA GTC Taiwan.詳細資料
This one-day course aims at introducing fundamental techniques of machine learning and deep learning, with particular applications for computer vision. Preferred (but not necessarily required) background knowledge for taking this course would be probability, linear algebra, signals and systems, and machine learning. In the first part of this course, we will start from a number of unsupervised and supervised learning algorithms, using image or video data as examples. In the second part, we will talk about (deep) neural networks and convolutional neural networks, and explain why they have shown promising performances for computer vision tasks.
Finally, as the last part of this course, we will cover a range of state-of-the-art deep learning models (e.g., generative adversarial networks, recurrent neural networks, reinforcement learning, etc.), and demonstrate their successes on the applications of image classification, semantic segmentation, object detection, image/video analysis and synthesis.