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課程名稱 |
感知運算
Cognitive Computing |
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開課學期 |
108-1 |
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授課對象 |
電機資訊學院 資訊網路與多媒體研究所 |
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授課教師 |
徐宏民 |
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課號 |
CSIE5420 |
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課程識別碼 |
922 U4460 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期二7,8,9(14:20~17:20) |
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上課地點 |
資107 |
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備註 |
總人數上限：50人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1081cognitive |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
Cognitive computing refers to systems that learn at scale, reason with purpose, and interact with humans naturally from numerous emerging sensors and signals. Cognitive computing systems are trained to sense, predict, infer, and in some ways, reason, using machine learning algorithms that are operated over large-scale, noisy, and unstructured data streams.
The topic will be essential for current and future industrial needs and academic research opportunities.
Please see the my webpage for more updated course information:
https://winstonhsu.info/2018f-cognitive-computing/
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課程目標 |
We aim to introduce the state-of-the-art and essential machine learning algorithms for numerous core problems in cognitive computing. We investigate methods for machine perception and the following action planning. We need to deal with the noisy, unstructured, high-dimensional data in rigorous and efficient manners.
We emphasize the hands-on experiences for conducting the course in terms of programming and experimental assignments, midterm, and final projects. We will organize the lecture content from the state-of-the-art and the reading materials will be mostly based on the literatures from top conferences. |
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課程要求 |
本課程的目標在於讓修課同學：
- Preliminarily understanding the design and implementation of cognitive learning algorithms for multimodal signals (including images, videos, audio, and text)
- Understanding basic machine learning tools for learning high-dimensional data for machine perception
- Evaluating the performance of numerous applications in cognitive computing
- Identifying current research problems in machine perception and cognitive computing |
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
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指定閱讀 |
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參考書目 |
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評量方式
(僅供參考) |
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