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๋ชจ๋ฐ”์ผ ๋ฉ”๋‰ด ์—ด๊ธฐ
 

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์•ัซฃผ๋Œถฤว๊™๊ต ์†Œํ”„ํŠธ์›จนฎด์œตว๊ฉ๋Œ€ว๊™

๋Œถฤว๊™์›

๊ต์ˆ˜์š”๋ชฉ

AI ๊ธฐ์ดˆ

๊ณ ๊ธ‰์•Œ๊ณ ๋ฆฌ์ฆ˜ (Advanced Algorithm)
  • ํ•™๋ถ€์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ณผ๋ชฉ์— ์ด์–ด์„œ ํšจ์œจ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ค๊ณ„์™€ ๋ถ„์„์— ๋Œ€ํ•˜์—ฌ ๊นŠ์ด ์žˆ๊ฒŒ ๊ณต๋ถ€ํ•œ๋‹ค. ๋‹ค๋ฃจ๋Š” ์ฃผ์ œ๋Š” ๊ทธ๋ž˜ํ”„ ์•Œ๊ณ ๋ฆฌ์ฆ˜, ๋Œ€์ˆ˜์  ์•Œ๊ณ ๋ฆฌ์ฆ˜, ์ŠคํŠธ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜, ๊ธฐํ•˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜, ๊ทผ์‚ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋“ฑ์ด๋‹ค.
๋ฐ์ดํ„ฐํ†ต๊ณ„ (DataStatistics)
  • ์‹ค์ œ์ž๋ฃŒ๋ถ„์„ ๋ฐ ๋ฌธ์ œํ•ด๊ฒฐ์„ ์œ„ํ•œ ๊ธฐ์ดˆ์  ํ†ต๊ณ„๊ธฐ๋ฒ•์„ ๊ฐœ๋… ์œ„์ฃผ๋กœ ์ตํžŒ๋‹ค. ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๊ณ ๊ธ‰ํ†ต๊ณ„, ์ฆ‰ ํšŒ๊ท€๋ถ„์„, ๋‹ค๋ณ€ ๋Ÿ‰์ž๋ฃŒ๋ถ„์„, ์‹คํ—˜๊ณ„ํš๋ฒ• ๋“ฑ์„ ์—ฐ๊ตฌํ•˜๋Š” ๋ฐ ํ•„์š”ํ•œ ์ง€์‹์„ ์ตํžŒ๋‹ค.
์ˆ˜ํ•™์  ๋ชจ๋ธ๋งI (Mathematical Modeling I)
  • ๋ฌผ๋ฆฌ๊ณผํ•™(Physical Sciences), ๊ณตํ•™ ๋“ฑ์— ๋“ฑ์žฅํ•˜๋Š” ๊ณผํ•™์  ํ˜„์ƒ์„ ์ˆ˜๋ฆฌ ๋ชจ๋ธ๋ง ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋ฐฐ์šด๋‹ค, ๊ตฌ์ฒด์ ์œผ๋กœ, ์‹ค์ œ ๋ฌธ์ œ์—์„œ ๋“ฑ์žฅํ•˜๋Š” ๋ฏธ๋ถ„๋ฐฉ์ •์‹, ์„ ํ˜•์‹œ์Šคํ…œ, ๋น„์„ ํ˜•์‹œ์Šคํ…œ, ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋“ฑ์„ ํ•™์Šตํ•œ๋‹ค
์ธ๊ฐ„์ค‘์‹ฌ์ธ๊ณต์ง€๋Šฅ๊ฐœ๋ก  (HumanCenteredArtificialIntelligence)
  • ์ธ๊ฐ„์ค‘์‹ ์ธ๊ณต์ง€๋Šฅ ์ˆ˜์—…์€ ๊ธฐ์ดˆ์ธ๊ณต์ง€๋Šฅ ์†Œ๊ฐœ์™€ ํ•จ๊ป˜ ํ•ด์„๊ฐ€๋Šฅํ•œ ์ธ๊ณต์ง€๋Šฅ ๋ฐฉ๋ฒ•๋“ค์„ ์†Œ๊ฐœํ•œ๋‹ค. ๊ธฐ์ดˆ์ธ๊ณต์ง€๋Šฅ์—์„œ๋Š” ์ธ๊ณต์ง€๋Šฅ ๋ฐฉ๋ฒ•๋“ค์˜ ์ „์ฒด์  ๊ด€๊ณ„์™€ ํ˜•ํƒœ์™€ Decision Making process - State Search ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•, Constraint ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ• Probabilistic Reasoning, ๋“ฑ โ€“์„ ๋‹ค๋ฃฌ๋‹ค. Data๊ธฐ๋ฐ˜ Optimization ๋ฐฉ๋ฒ•๋“ค์€ ํ•ด์„๊ฐ€๋Šฅ์„ฑ์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฌ๋Ÿฌ Tree๊ธฐ๋ฐ˜ Classification/Regression solver๋“ค๊ณผ rule๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์„ ์†Œ๊ฐœํ•œ๋‹ค.
์ธ๊ณต์ง€๋Šฅยท๋ฐ์ดํ„ฐ์ฒ˜๋ฆฌ์–ธ์–ด (Computer Programming for AI&Data Processing)
  • ์ปดํ“จํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ธฐ ์ˆ ์„ ํ•™์Šตํ•œ๋‹ค. ํŠนํžˆ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ๋ณ€์ˆ˜์™€ ํ˜•, ์กฐ๊ฑด, ๋ฐ˜๋ณต, ํ•จ์ˆ˜ ๋“ฑ์— ๋Œ€ํ•œ ๊ธฐ๋ณธ ๊ฐœ๋…๊ณผ ๋”๋ถˆ์–ด ์ด๋Ÿฌํ•œ ๊ฐœ ๋…์„ Python, C++, JavaScript ๋“ฑ ๋‹ค์–‘ํ•œ ์ปดํ“จํ„ฐ ํ”„๋กœ๊ทธ ๋ž˜๋ฐ ์–ธ์–ด์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ํ•™์Šตํ•œ๋‹ค. ํ•™์Šต ๊ฒฐ๊ณผ๋กœ ํ•™์ƒ๋“ค์€ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์— ๊ด€ํ•œ ๊ธฐ๋ณธ ๊ฐœ๋…๊ณผ ๊ฐ„๋‹จํ•œ ํ”„๋กœ๊ทธ๋ž˜ ๋ฐ ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•, ํ”„๋กœ๊ทธ๋žจ์„ ์ž‘์„ฑํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ํ„ฐ๋ฆฌํ•˜๊ธฐ ์œ„ ํ•œ ๋ฐฉ๋ฒ•์„ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค.

AI ํ•ต์‹ฌ

๊ณ ๊ธ‰๊ธฐ๊ณ„ํ•™์Šต (Advanced machine learning)
  • ๋ณธ ๊ณผ๋ชฉ์€ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹๊ณผ ๊ธฐ๊ณ„ ํ•™์Šต ๋ถ„์•ผ์˜ ๊ณ ๊ธ‰ ์ˆ˜์ค€ ๊ฐ• ์ขŒ๋กœ, ๊ณ ์ „์ ์ธ ๋ฐฉ๋ฒ•๋ก ์—์„œ๋ถ€ํ„ฐ ์ตœ์‹  ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์‹ค์ œ ์‘์šฉ์—์„œ ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ๋ฒ•๋“ค์„ ์†Œ๊ฐœํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ๋ถ„๋ฅ˜ (classification) ๊ธฐ๋ฒ•, ๊ณ  ์ฐจ์› ํšŒ๊ท€๋ถ„์„ (regression) ๋ชจ๋ธ, ๊ตฐ์ง‘ํ™” (clustering), bagging and boosting, ์š”์ธ ๋ถ„์„ (factor analysis), ์€ ๋‹‰๋งˆ๋ฅด์ฝ”ํ”„ ๋ชจ๋ธ (hidden markov model), ๊ทธ๋ฆฌ๊ณ  ํ™•๋ฅ  ๊ทธ ๋ž˜ํ”„ ๋ชจ๋ธ (probabilistic graphical model) ๋“ฑ์„ ๋‹ค๋ฃฌ๋‹ค.
๊ณ ๊ธ‰์ธ๊ณต์ง€๋Šฅ (Advanced Artificial Intelligence)
  • ์ง€์‹ํ‘œํ˜„๊ณผ ์ถ”๋ก ์„ ์ง‘์ค‘์ ์œผ๋กœ ๊ณต๋ถ€ํ•œ๋‹ค. ํŠนํžˆ Ontology Engineering์„ ์œ„ํ•œ ์ง€์‹ ํ‘œํ˜„ ๋ฐ ์ถ”๋ก ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ณต๋ถ€ํ•˜ ๋ฉฐ, ์ด์— ๋Œ€ํ•œ ์‚ฌ๋ก€์—ฐ๊ตฌ ์ค‘์‹ฌ์œผ๋กœ ์‹ฌ๋„ ์žˆ๊ฒŒ ๋‹ค๋ฃฌ๋‹ค.
๊ณ ๊ธ‰์ธ๊ฐ„์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ (Advanced Human-Computer Interaction)
  • ๋ณธ ๊ต๊ณผ๋ชฉ์—์„œ๋Š” HCI๋ถ„์•ผ ์—ฐ๊ตฌ ์ˆ˜ํ–‰์— ์žˆ์–ด์„œ ํ•„์ˆ˜์ ์ธ HCI ๋ชจ๋ธ, ์ด๋ก , ํ”„๋ ˆ์ž„์›Œํฌ์— ๋Œ€ํ•ด์„œ ์†Œ๊ฐœํ•˜๊ณ , HCI ์ตœ์‹  ์—ฐ๊ตฌ ๋™ํ–ฅ์„ ์‚ดํŽด๋ณธ๋‹ค. ๋˜ํ•œ HCI์˜ ๋‹ค์–‘ํ•œ ์‘์šฉ๋ถ„์•ผ(e.g., Social Computing, Human Computation, Machine Learning, Visualization, Mobile Interaction)์—์„œ ์‹ค์ œ ๋ฌธ์ œ ํ•ด๊ฒฐ์— ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก  ๋ฐ ๊ธฐ์ˆ ์„ ์ˆ™์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ ํšŒ๋ฅผ ์ œ๊ณตํ•œ๋‹ค.
๊ณ ๊ธ‰์ปดํ“จํ„ฐ๋น„์ ผ (Advanced Computer Vision)
  • Humans perceive the three-dimensional structure of the world with apparent ease. The goal of a computer vision is to achieve the dream of having a computer interpret an image at the same level. In this course, we will explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level task such as image editing and stitching, which students can apply to their own personal photos and videos. Moreover, we will study the deep learning based computer vision methods from common CNN-based object recognition to RNN-based sequential image processing. To handle this latest method, we will study the deep learning tools such as caffe, torch and tensor flow and from AlexNet to ResNet from the viewpoint of computer vision application.
๊ณ ๋“ฑ๋ฐ์ดํ„ฐ๋งˆ์ด๋‹ (Advanced Data Mining)
  • Data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, intelligent machines, and the amount has been increasing at an incredible rate due to technological advances. โ€œData miningโ€ refers to a collection of techniques for extracting โ€œinterestingโ€ relationships and knowledge hidden in a mountain of data in order to assist managers or analysts to make intelligent use of them. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. In this course, we will examine a variety of data mining techniques evolved from the disciplines of statistics and artificial intelligence (or machine learning), and practice them in recognizing patterns and making predictions from an applications perspective. Application (or case) surveys and hands-on experimentations with easy-to-use software will be provided.
๊ณ ๊ธ‰์˜์ƒ์‹ ํ˜ธ์ฒ˜๋ฆฌ (Advanced Image Signal Processing)
  • ์ด ๊ต๊ณผ๋ชฉ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ์˜์ƒ์‹ ํ˜ธ์ฒ˜๋ฆฌ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์„ ํ˜•์ฒ˜๋ฆฌ(ํ™”์งˆ๊ฐœ์„  ๋ฐ ์˜์ƒ์žฌ์ƒ), ๋น„์„ ํ˜•์ฒ˜๋ฆฌ(๋ถ„์ˆ˜๊ณ„ ๋ณ€ํ™˜, ํ˜•ํƒœ๋ก ), ์ปฌ๋Ÿฌ์˜์ƒ์ฒ˜๋ฆฌ(์ปฌ๋Ÿฌ ๊ธฐ์šธ๊ธฐ์— ์˜ํ•œ ์—์ง€๊ฒ€์ถœ), ๋‹ค์ฐจ์›์˜์ƒ์ฒ˜๋ฆฌ ๋“ฑ์„ ๋‹ค๋ฃฌ๋‹ค. ๊ธฐ์กด์˜ ์ฃผ์š” ์˜์ƒ์ฒ˜๋ฆฌ๊ธฐ๋ฒ•(์˜์ƒ๋ถ„ํ• , ๋‹ค์ฐจ์› ์˜์ƒ ๋ถ„๋ฅ˜, ๋™์˜์ƒ๋ฌผ์ฒด์ถ”์ )์„ ๋‹ค๋ฃจ์ง€๋งŒ ์ด๋ก ๋ณด๋‹ค ์‹คํ—˜ ์‹ค์Šต์  ์ปดํ“จํ„ฐ ๊ณ„์‚ฐ์— ์ค‘์ ์„ ๋‘”๋‹ค.
๊ณ ๊ธ‰์†Œํ”„ํŠธ์›จ์–ด๊ณตํ•™ (AdvancedSoftwareEngineering)
  • ๋ณธ ๊ฐ•์ขŒ๋Š” ์†Œํ”„ํŠธ์›จ์–ด ๊ณตํ•™์— ๋Œ€ํ•œ ๊ณ ๊ธ‰ ์ˆ˜์ค€์˜ ๊ฐ•์ขŒ๋กœ ์„œ ๊ธฐ์กด์˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ณตํ•™์˜ ๊ฐœ๋…, ๋ฐฉ๋ฒ•๋ก , ๊ธฐ๋ฒ• ๋“ฑ์„ ๋ถ„ ์„ ํ‰๊ฐ€ํ•˜๊ณ  ๊ทธ๊ฒƒ์˜ ํ•œ๊ณ„์„ฑ ๋‚ด์ง€๋Š” ์ œ์•ฝ์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ ํ•ด์„œ ์ƒˆ๋กญ๊ฒŒ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋Š” ๊ฐ์ฒด์ง€ํ–ฅ์  ์†Œํ”„ํŠธ์›จ์–ด ๊ณตํ•™ (O.O.S.E.),์‹œ์Šคํ…œ ๊ณตํ•™, ์ปดํฌ๋„ŒํŠธ ๊ธฐ๋ฐ˜ ์†Œํ”„ํŠธ์›จ์–ด๊ณตํ•™ (Component Based S.E.) ๋ฐ ์•„ํ‚คํ…์ณ ๊ธฐ๋ฐ˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ณตํ•™ (Architecture Based S.E.)๋“ฑ์— ๊ด€ํ•ด์„œ ๊ทธ๊ฒƒ๋“ค์˜ ์ƒˆ ๋กœ์šด ๊ฐœ๋… ๊ทธ๋ฆฌ๊ณ  ๋ฐฉ๋ฒ•๋ก  ๊ธฐ๋ฒ• ๋“ฑ์— ๋Œ€ํ•ด์„œ ํฌ๊ด„์ ์œผ๋กœ ๊ณ  ์ฐฐํ•˜๊ณ  ํ˜„์‹ค ์ ์šฉํ™˜๊ฒฝ์„ ๋ถ„์„ ํ‰๊ฐ€ํ•ด ๋ด„์œผ๋กœ์„œ ํ–ฅํ›„ ์ด๋ถ„์•ผ ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ฐœ์ „ํ•ด ๊ฐˆ ๊ฒƒ์ธ๊ฐ€์— ๋Œ€ํ•œ ๊ฐ๊ฐ์„ ๊ฐ€์ง€๋„๋ก ํ•˜๋Š” ๋ฐ ๋ชฉ์ ์„ ๋‘”๋‹ค.
๊ณ ๊ธ‰๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค (AdvancedDatabase)
  • ๋ณธ ๊ต๊ณผ์—์„œ๋Š” ํ•™์ƒ๋“ค์ด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋ถ„์•ผ์˜ ์ตœ์‹  ์—ฐ๊ตฌ ์ด ์Šˆ๋ฅผ ํ•™์ƒ๋“ค์ด ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ์ฆ‰, ๊ฐ์ฒด ์ง€ํ–ฅ ๋ฐ์ด ํ„ฐ๋ฒ ์ด์Šค, ๊ฐ์ฒด ๊ด€๊ณ„ํ˜• ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค, XML ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค, ๋ฉ€ํ‹ฐ๋ฏธ๋””์–ด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค, ์ฐจ์„ธ๋Œ€ ํ”Œ๋ž˜์‹œ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ฐ์ด ํ„ฐ๋ฒ ์ด์Šค ๋“ฑ์„ ๋‹ค๋ฃฌ๋‹ค.
๊ณ ๊ธ‰์šด์˜์ฒด์ œ (Advanced Operating System)
  • ์ด ๊ฐ•์˜์—์„œ๋Š” Liux ์šด์˜์ฒด์ œ์˜ ๊ตฌ์กฐ ๋ฐ ๊ตฌํ˜„์„ ์—ฐ๊ตฌํ•œ๋‹ค. ํŠนํžˆ ๋กœ๋”, ์‰˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋“ฑ์„ ํ•™์Šตํ•˜๊ณ , ์ฃผ์š” Linux source code๋ฅผ ์ค‘์‹ฌ์œผ๋กœ Linux์˜ ์ฃผ์š” ์ž๋ฃŒ๊ตฌ์กฐ, ๋ชจ๋“ˆ ๊ด€๋ฆฌ, VFS, ์žฅ์น˜๋“œ๋ผ์ด๋ฒ„, ๋„คํŠธ์›Œํฌ ๊ด€๋ จ ๋ชจ๋“ˆ, ์žฅ์น˜ ๋“œ๋ผ์ด๋ฒ„๋‚˜ ์ฃผ์š” ์‹œ์Šคํ…œ ํ˜ธ์ถœ์˜ ๊ตฌํ˜„ ๊ธฐ๋ฒ•์„ ์‚ดํŽด๋ณธ๋‹ค.
๊ณ ๊ธ‰์ปดํ“จํ„ฐ๊ตฌ์กฐ (Advanced Computer Architecture)
  • ์ตœ๊ทผ ๊ณ ์„ฑ๋Šฅ ํ”„๋กœ์„ธ์„œ ์„ค๊ณ„์—์„œ๋Š” ์„ฑ๋Šฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด, Instruction Level Parallelism (ILP) ๊ธฐ๋ฒ•, Thread Level Parallelism (TLP) ๊ธฐ๋ฒ•, ๋ฉ€ํ‹ฐ ์ฝ”์–ด ๊ธฐ๋ฒ•, ๋ณ‘๋ ฌ ์ปดํ“จํ„ฐ ๋“ฑ์„ ์ด์šฉ, ์„ฑ๋Šฅ์„ ๋†’์ด๊ณ  ์žˆ๋‹ค. ์ด๋Š” ์ฃผ๋กœ ๊ธฐ์กด ์ปดํ“จํ„ฐ์—์„œ ์‚ฌ์šฉํ•˜๋˜ ๊ธฐ์ˆ ์ด์—ˆ์œผ๋‚˜, ์ตœ๊ทผ์—๋Š” ์Šค๋งˆํŠธํฐ, ์Šค๋งˆํŠธ ํŒจ๋“œ ๋“ฑ์—์„œ ์ ๊ทน์ ์œผ๋กœ ์ฑ„ํƒํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์  ๋ณ€ํ™”, ์‹œ์žฅ์  ๋ณ€ํ™”๋Š” ๋ฏธ๋ž˜์˜ ๋งˆ์ดํฌ๋กœํ”„๋กœ์„ธ์„œ ๋””์ž์ธ์˜ ์ƒˆ๋กœ์šด ์˜์—ญ์„ ๊ฐœ์ฒ™ํ•  ๊ฒƒ์ด๋‹ค. ์ด ๊ต๊ณผ๋ชฉ์—์„œ๋Š” ๊ณ ๊ธ‰ ์ปดํ“จํ„ฐ ๊ตฌ์กฐ๋ผ๋Š” ์ฃผ์ œ๋กœ, ์ ์‘์  ๋™์  branch prediction, ๊ณ ๋Œ€์—ญํญ instruction fetch, ๋™์  instruction scheduling, Tomasulo ์•Œ๊ณ ๋ฆฌ์ฆ˜, superscalar, speculation, multi threading, symmetric multiprocessors, shared memory multiprocessors, cache and memory hierarchy ์„ค๊ณ„ ๋“ฑ์„ ์ฃผ๋กœ ํ•™์Šตํ•œ๋‹ค.
๋ถ„์‚ฐ๋ณ‘๋ ฌํ”„๋กœ๊ทธ๋ž˜๋ฐ (DistributedandParallelProgramming)
  • ๋ถ„์‚ฐ๋ณ‘๋ ฌํ”„๋กœ๊ทธ๋ž˜๋ฐ์€ ์—ฌ๋Ÿฌ ์—ฐ์‚ฐ(task or job)์„ ๋™์‹œ ์— ์ˆ˜ํ–‰ํ•˜์—ฌ ๋Œ€๊ทœ๋ชจ ์ปดํ“จํŒ… ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ณ ์„ฑ ๋Šฅ ์ปดํ“จํŒ… ํŒŒ์›Œ(High Performance Computing/ High Throughput Computing)๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์–ด ์™” ๋‹ค. ์ตœ๊ทผ์— ๋ฌธ์ œ์˜ ํฌ๊ธฐ๊ฐ€ ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ปค์ง€๋ฉฐ (๋น… ๋ฐ์ดํ„ฐ), multicore ๋ฐ manycore (GPGPU)์˜ ๋“ฑ์žฅ ๋ฐ MapReduce ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ชจ๋ธ์˜ ํ™•์‚ฐ์— ๋”ฐ๋ผ ๋ณ‘๋ ฌํ”„๋กœ๊ทธ๋ž˜ ๋ฐ์˜ ํ•„์š”์„ฑ์ด ๋‹ค์‹œ ๋Œ€๋‘๋˜๊ณ  ์žˆ์–ด ๋ณธ ๊ณผ๋ชฉ์„ ํ†ตํ•ด์„œ ๋ถ„์‚ฐ๋ณ‘๋ ฌํ”„๋กœ๊ทธ๋ž˜๋ฐ์˜ ์ด๋ก  ๋ฐ ์‘์šฉ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ํ•™์Šตํ•œ๋‹ค. ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ๋ณ‘๋ ฌํ”„๋กœ๊ทธ๋ž˜๋ฐ์˜ ๊ธฐ๋ฐ˜์ด ๋˜๋Š” ํ”Œ๋žซํผ, ๋ชจ๋ธ ๊ณผ ํ•จ๊ป˜ ์ „ํ†ต์ ์ธ ๊ณ ์„ฑ๋Šฅ ์ปดํ“จํ„ฐ/ํด๋Ÿฌ์Šคํ„ฐ ๊ธฐ๋ฐ˜์˜ Parallel Programming Tool์ธ MPI, ์ตœ๊ทผ ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ… ๋ฐ ๋น…๋ฐ ์ดํ„ฐ์™€ ๊ด€๋ จํ•˜์—ฌ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋Š” MapReduce (Hadoop) ๋ฐ CUDA (PyCUDA) ๋“ฑ์˜ GPGPU๋ฅผ ํ™œ์šฉํ•œ ๋ณ‘๋ ฌ์ฒ˜๋ฆฌ์— ๋Œ€ํ•ด ํ•™์Šตํ•œ๋‹ค.
์ปดํ“จํ„ฐ๋น„์ ผ (Theory and Applications of Reinforcement Learning)
  • ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ์ปดํ“จํ„ฐ ๋น„์ ผ์˜ ์ผ๋ฐ˜์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์— ๋Œ€ํ•ด์„œ ๊ณต๋ถ€ํ•œ๋‹ค. ์ปดํ“จํ„ฐ ๋น„์ ผ์ด๋ž€ ์ •์ง€์˜์ƒ์ด๋‚˜ ๋™์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ 3์ฐจ์› ํ™˜๊ฒฝ์„ ๋ถ„์„ํ•˜๊ณ  ๋ชจ๋ธ๋งํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด ๊ณผ์ •์—์„œ๋Š” ๋จผ์ € ์˜์ƒ์˜ ๊ธฐ๋ณธ์ ์ธ filtering, sampling ๋“ฑ์˜ ๊ฐœ๋…์„ ๋ฐฐ์šฐ๊ณ  edge detection, projection, image matching, motion estimation, image segmentation ๋“ฑ ์ปดํ“จํ„ฐ ๋น„์ ผ ๊ฐ ๋ถ„์•ผ์˜ ๋Œ€ํ‘œ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๊ณผ ์ด๋“ค์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋“ค์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ํ•™๊ธฐ๋ง์—๋Š” ๊ฐ์ž ์ปดํ“จํ„ฐ ๋น„์ ผ ์ตœ์‹  ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ตฌํ˜„ํ•˜๊ณ  ๊ฐœ์„ ํ•˜๋Š” ํ”„๋กœ์ ํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉด์„œ ์ปดํ“จํ„ฐ ๋น„์ ผ์˜ ๋…ธํ•˜์šฐ๋ฅผ ๊นจ์น˜๊ฒŒ ๋œ๋‹ค.

AI ์‹ฌํ™”

๊ฐ•ํ™”ํ•™์Šต์ด๋ก ๋ฐ์‘์šฉ (Theory and Applications of Reinforcement Learning)
  • ๊ฐ•ํ™”ํ•™์Šต์˜ ๊ธฐ์ดˆ์  ๋‚ด์šฉ์ธ Multi-Armed Bandit, Markov Decision Process๋กœ๋ถ€ํ„ฐ Monte-Carlo Method, Q-learning, Value Function Approximation, Policy Gradient, Deep Q-learning Network ๋“ฑ ์ด๋ก ์  ๋‚ด์šฉ์„ ๋‹ค๋ฃฌ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์˜ ์‘์šฉ ์‚ฌ๋ก€๋“ค์„ ์‚ดํŽด๋ณด๊ณ  ํ•™ ์ƒ๋“ค์˜ ์—ฐ๊ตฌ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ”„๋กœ์ ํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค.
๊ณ ๊ธ‰์ •๋ณด๋ณดํ˜ธ (Advanced Information Security)
  • ๋ณธ ๊ณผ๋ชฉ์€ ์ •๋ณด ๋ณดํ˜ธ์— ๋Œ€ํ•œ ๊ณ ๊ธ‰ ์ด๋ก ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ ํ‘œ๋กœ ํ•œ๋‹ค. ๋จผ์ € ์ •๋ณด๋ณดํ˜ธ์˜ ์˜๋ฏธ, ์ค‘์š”์„ฑ, ๊ทธ๋ฆฌ๊ณ  ๋ชฉํ‘œ๋ฅผ ์ดํ•ดํ•˜๊ณ , ์ดํ›„ ์ •๋ณด๋ณดํ˜ธ์— ๊ด€๋ จ๋œ ์•”ํ˜ธํ•™, ๋ณด์•ˆ ๋ชจ๋ธ ๋ฐ ์ •์ฑ…, ์šด์˜์ฒด์ œ ๋ณด์•ˆ, ํ”„๋กœ๊ทธ๋žจ ๋ณด์•ˆ, ์•…์„ฑ ์ฝ”๋“œ, ๋ณด์•ˆ ํ‰๊ฐ€ ์™€ ๊ด€๋ฆฌ ๋“ฑ์˜ ๊ณ ๊ธ‰ ์ด๋ก ๋“ค์„ ์—ฐ๊ตฌํ•œ๋‹ค.
๊ธฐ๊ณ„ํ•™์Šต ์‹ฌํ™”์ด๋ก  (AdvancedMachineLearningTheory)
  • Machine learning is all about finding generalized patterns from data. The whole idea is to replace the โ€œhuman writing codeโ€ with a โ€œhuman supplying dataโ€ and then let the system figure out what it is that the person wants to do by looking at the examples. In recent years, many successful applications of machine learning have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. The goal of this class is to provide an overview of the state-of-art algorithms used in machine learning and different perspectives, and hopefully to gain some understanding of whatโ€™s going on the next. We will discuss both the theoretical properties of these algorithms and their practical applications.
๊ธฐ์ˆ ์ธํ…”๋ฆฌ์ „์Šค (Technologyintelligence)
  • ๊ธฐ์ˆ  ์ธํ…”๋ฆฌ์ „์Šค๋Š” ๋‹ค์–‘ํ•œ ์›์ฒœ์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ์ˆ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘, ํ†ตํ•ฉ, ๋ถ„์„, ์‹œ๊ฐํ™”ํ•˜์—ฌ ์กฐ์ง์˜ ๊ธฐํšŒ์™€ ์œ„ํ˜‘์„ ํŒŒ์•…ํ•˜์—ฌ ์˜์‚ฌ๊ฒฐ์ •์ž์—๊ฒŒ ์ œ๊ณตํ•˜๋Š” ํ”„๋กœ์„ธ์Šค๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๋ณธ ๊ณผ์ •์—์„œ๋Š” ๊ฐ€์žฅ ๋Œ€ํ‘œ์ ์ธ ๊ธฐ์ˆ ์ •๋ณด์˜ ์›์ฒœ์ธ ํŠนํ—ˆ, ์ƒํ‘œ๊ถŒ์— ๋Œ€ํ•ด ์ด๋ก ์  ๊ฐ•์˜๋ฅผ ์ง„ํ–‰ํ•˜๊ณ , ์ด๋ฅผ ๋‹จ๋…์œผ๋กœ ํ˜น์€ ๊ธฐ์—… ํ”„๋กœํŒŒ์ผ, ์›น ๋ฐ์ดํ„ฐ ๋“ฑ๊ณผ ํƒ€ ์ •๋ณด์›์ฒœ๊ณผ ํ†ตํ•ฉํ•˜์—ฌ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ธฐ์ˆ ๊ณผ ๊ฒฝ์Ÿ์‚ฌ์˜ ๋™ํ–ฅ์„ ํŒŒ์•…ํ•˜๊ณ  ๊ถ๊ทน์ ์œผ๋กœ ์กฐ์ง์˜ ์˜์‚ฌ ๊ฒฐ์ •์„ ์ง€์›ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ•™์Šตํ•œ๋‹ค. ํŠนํžˆ ๋ณ€๋ฆฌ์‚ฌ ๋“ฑ์˜ ์™ธ๋ถ€ ๊ฐ•์‚ฌ์ง„ ๋“ฑ์„ ์ดˆ๋น™ํ•˜์—ฌ ๊ธฐ์ˆ ์ •๋ณด๋ถ„์„์˜ ์‹ค๋ฌด์™€ ์ด๋ก ์— ๋Šฅํ•œ ์—ฐ๊ตฌ์ž๋ฅผ ์–‘์„ฑํ•˜๊ณ ์ž ํ•œ๋‹ค.
๊ธฐ์ˆ ์˜ˆ์ธก (Technologyforecasting)
  • ๊ธ‰์†๋„๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๊ณผํ•™๊ธฐ์ˆ  ํ™˜๊ฒฝ ์†์—์„œ ๊ธฐ์—…๋“ค์€ ํ˜„์กดํ•˜๋Š” ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์„ ๋ชจ๋‹ˆํ„ฐ๋ง ํ•จ๊ณผ ๋™์‹œ์— ์ƒˆ๋กญ๊ฒŒ ๋ถ€์ƒํ•˜๋Š” ์œ ๋ง๊ธฐ์ˆ ์„ ์ฐพ์•„์•ผ๋งŒ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค. ๊ตญ๊ฐ€ ์ˆ˜์ค€์—์„œ ๋˜ํ•œ ๋ฏธ๋ž˜์˜ ์‚ฌํšŒ์™€ ๊ธฐ์ˆ ์˜ ๋ณ€ํ™”๋ฐฉํ–ฅ์„ ์˜ˆ์ธกํ•˜๊ณ  ๋ฏธ๋ž˜๋ฅผ ์„ ๋„ํ•  ์œ ๋ง์—ฐ๊ตฌ ๋ฐ ๊ธฐ์ˆ ์„ ์ฐพ๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์„ ์ง€์†ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ๋ฏธ๋ž˜์˜ ๊ธฐ์ˆ ์„ ์˜ˆ์ธกํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒ€ํ† ํ•˜๊ณ  ๊ฐ ๋ฐฉ๋ฒ•๋ก ์˜ ์žฅ๋‹จ์ ๊ณผ ์ ์šฉ์‹ค๋ก€๋ฅผ ์—ฐ๊ตฌํ•œ๋‹ค.
์†Œ์…œ๋ฏธ๋””์–ด๋ถ„์„ (Social Media Analysis)
    • API ํ™œ์šฉ ๋ฐ ์›น ํฌ๋กค๋ง์„ ํ†ตํ•œ ์†Œ์…œ๋ฏธ๋””์–ด ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์ €์žฅ
    • ๋ฐ์ดํ„ฐ์˜ ์ „์ฒ˜๋ฆฌ, ์••์ถ• ๋ฐ correlation, regression, and classification์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„ ใ†์–ธ์–ดํ•™์  ํŠน์ง• ๋ถ„์„ ๋ฐ ๊ฐ์„ฑ ๋ถ„์„ ใ† ์—ฐ๊ตฌ ๋ชฉ์ ์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ํˆด์„ ํ™œ์šฉํ•œ ์†Œ์…œ๋ฏธ๋””์–ด ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐ ์‹œ๊ฐํ™”
์ž์—ฐ์–ด์ฒ˜๋ฆฌ (Natural Language Processing)
  • ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ์ž์—ฐ์–ด๋กœ ํ‘œํ˜„๋˜์–ด ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ •๋ณด๋“ค์„ ์ „ ์ฒ˜๋ฆฌ ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ํ™” ํ•˜๊ณ , ์ด๋ฅผ ๋‹ค์–‘ํ•œ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜ ์—ฌ ์˜๋ฏธ ์žˆ๋Š” ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ธฐ ์œ„ํ•œ ์ผ๋ จ์˜ ๊ณผ์ •์„ ํ•™์Šตํ•œ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ๊ด€๋ จ ๋ถ„์•ผ์˜ ์—ฐ๊ตฌ ๋™ํ–ฅ์„ ์‚ดํŽด๋ณด๊ณ  ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ์„ ํ•จ์–‘ํ•œ๋‹ค.
์ „์‚ฐ์ƒ๋ฌผํ•™ (Computational Biology)
  • BT์™€ IT ์‚ฐ์—…์˜ ์œตํ•ฉ์— ํ•ด๋‹นํ•˜๋Š” ์ „์‚ฐ์ƒ๋ฌผํ•™์˜ ๊ธฐ์ดˆ ์ง€์‹ ๋ฐ ๊ทธ ์‘์šฉ๊ณผ ์ „๋ง์— ๋Œ€ํ•ด ๋ฐฐ์šด๋‹ค. ๋ถ„์ž์ƒ๋ฌผํ•™์˜ ๊ฐ„๋žตํ•œ ๊ฐœ ์š” ๋ฐ R programming์„ ์†Œ๊ฐœํ•˜๊ณ , sequence analysis, disease association analysis, gene expression analysis, systems biology ๋“ฑ ์˜์ƒ๋ช…๊ณผํ•™ ๊ด€๋ จ ์•Œ๊ณ ๋ฆฌ ์ฆ˜์— ๋Œ€ํ•ด ๊ณต๋ถ€ํ•œ๋‹ค. Clustering, classification, timeseries data analysis, network mining ๋“ฑ์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ๋ฐ ์ดํ„ฐ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ๋‹ค๋ฃฌ๋‹ค.
์ •๋ณด๊ฒ€์ƒ‰ (Information Retrieval)
  • ์ •๋ณด ๊ฒ€์ƒ‰์˜ ๋ชจ๋ธ, ๋ธ”๋ฆฌ์–ธ ๋ชจ๋ธ, ๋ฒกํ„ฐ๊ณต๊ฐ„ ๋ชจ๋ธ, ์ธ์ง€๊ณผํ•™์  ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๊ฒ€์ƒ‰ ๋ชจํ˜• ๋“ฑ์„ ๋ฐฐ์šด๋‹ค. ๋˜ํ•œ ์ธํ„ฐ ๋„ท ๊ฒ€์ƒ‰์„ ์ค‘์‹ฌ์œผ๋กœ ํ•„์š”ํ•œ ๊ธฐ์ˆ , ์ธ๋ฑ์Šค ์ถ”์ถœ, ํ•„ํ„ฐ๋ง, ํด ๋Ÿฌ์Šคํ„ฐ๋ง, ๊ฐœ๋… ๊ธฐ๋ฐ˜ ๊ฒ€์ƒ‰ ๋“ฑ์— ๊ด€๋ จ๋œ ๊ธฐ์ˆ ์„ ๋…ผ๋ฌธ์„ ์ค‘์‹ฌ ์œผ๋กœ ๋ฐฐ์šด๋‹ค. ์‘์šฉ์„ ์œ„ํ•˜์—ฌ ์ธํ„ฐ๋„ท์ƒ์—์„œ ๊ฒ€์ƒ‰ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์„ ๊ฐ„๋‹จํžˆ ๋งŒ๋“ค๊ณ  ๊ธฐ์ˆ ์„ ํ”„๋กœ์ ํŠธ๋ณ„๋กœ ๊ตฌํ˜„ํ•˜๋Š” ๊ณผ ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค.
ํด๋ผ์šฐ๋“œ์ปดํ“จํŒ… (Cloud Computing)
  • ํ˜„์žฌ IT ํ™˜๊ฒฝ์—์„œ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ํŒจ๋Ÿฌ๋‹ค์ž„์€ ํด๋ผ์šฐ๋“œ ์ปดํ“จ ํŒ…์ด๋ฉฐ, ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์ด ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…์„ ํ†ตํ•ด ๋” ํšจ์œจ ์ ์ด๊ณ  ์„ฑ๋Šฅ์ด ๋†’์€ ์ž์› ์ œ๊ณต์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ ๋‚˜์•„๊ฐ€์„œ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์„œ๋น„์Šค ๋ฐ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜(์‘์šฉ์ฒด๊ณ„)์˜ ์ œ๊ณต์ด ๊ฐ€๋Šฅํ•˜ ๋‹ค๊ณ  ์˜ˆ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ด On-demand ๊ธฐ๋ฐ˜์˜ ์ปดํ“จํŒ… ํŒจ๋Ÿฌ ๋‹ค์ž„์—์„œ๋Š” ์—ฌ๋Ÿฌ ์ปดํ“จํŒ… ๊ธฐ์ˆ ๋“ค์„ ํ•„์š”๋กœ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ์ด์™€ ๊ฐ™์€ ๊ธฐ์ˆ ๋“ค๊ณผ ์ด ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ… ํŒจ๋Ÿฌ๋‹ค ์ž„์„ ์‘์šฉํ•œ ์‘์šฉ์ฒด๊ณ„๋“ค์— ๋Œ€ํ•ด ๊ณต๋ถ€ํ•œ๋‹ค. ์„ธ๋ถ€ ์ฃผ์ œ๋กœ๋Š” ํด ๋ผ์šฐ๋“œ์ปดํ“จํŒ…์˜ ๊ฐœ์š”์™€ ์‹œ์Šคํ…œ ๋ชจ๋ธ, ๊ฐ€์ƒํ™” ๊ธฐ์ˆ , ํด๋ผ์šฐ๋“œ ํ”Œ๋žซํผ, ํด๋ผ์šฐ๋“œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ํ™˜๊ฒฝ, SOA ๋“ฑ์„ ๋‹ค๋ฃฌ๋‹ค.
ํŒจํ„ด์ธ์‹๋ก  (Pattern Recognition Theory)
  • ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ํŒจํ„ด์ธ์‹ ๋ฐฉ๋ฒ•๋“ค์— ๋Œ€ํ•ด ๊ณต๋ถ€ํ•œ๋‹ค. ๋จผ์ € ๋น„์ง€๋„ํ•™์Šต๊ณผ ์ง€๋„ํ•™์Šต ๋“ฑ์˜ ๊ฐœ๋…๊ณผ ์ด๋“ค์˜ ์ฐจ์ด์ ์— ๋Œ€ํ•ด์„œ ๊ณต๋ถ€ํ•˜๊ณ , ์ง€๋„ํ•™์Šต ์ค‘์—์„œ๋„ ๋ถ„๋ฅ˜ ๋ฌธ์ œ์™€ ๋ฆฌ๊ทธ๋ ˆ์…˜ ๋ฌธ์ œ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋‹ค๋ฅธ์ง€์— ๋Œ€ํ•ด์„œ ๊ณต๋ถ€ํ•œ๋‹ค. ๊ฐ ๋ฐฉ๋ฒ•๋“ค์˜ ๋Œ€ํ‘œ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๊ณผ ์ด๋“ค์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋ง์— ๋Œ€ํ•ด์„œ ๋‹ค๋ฃฌ๋‹ค. ํ•™๊ธฐ๋ง์—๋Š” ์–ผ๊ตด์ธ์‹ ์‹œ์Šคํ…œ์˜ ๊ตฌํ˜„ ๋“ฑ ๊ธฐ๋ง ํ”„๋กœ์ ํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉด์„œ ํŒจํ„ด์ธ์‹์˜ ๋…ธํ•˜์šฐ๋ฅผ ๊นจ์น˜๊ฒŒ ๋œ๋‹ค.

AI ์œตํ•ฉ

๊ธ€๋กœ๋ฒŒ์ธํ„ด์‹ญ1/2/3 (Global Internship 1/2/3)
๊ธฐ๊ณ„ํ•™์ŠตํŠน๋ก 1 (Advanced Topics in Machine Learning 1)
  • ๊ธฐ๊ณ„ ํ•™์Šต๊ณผ ๊ด€๋ จ๋œ ์ตœ์‹ ์˜ ์ด๋ก  ๋ฐ ์‘์šฉ, ์ถ”์„ธ์— ๋Œ€ํ•˜์—ฌ ๋‹ค ๋ฃฌ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ๊ด€๋ จ ์—ฐ๊ตฌ ๋™ํ–ฅ์„ ์†Œ๊ฐœํ•˜๊ณ , ํ† ๋ก ํ•จ์œผ๋กœ์จ ํ•™์ƒ๋“ค์˜ ๋…ผ๋ฆฌ์  ์‚ฌ๊ณ ๋ ฅ๊ณผ ํ† ๋ก  ๋Šฅ๋ ฅ์„ ํ•จ์–‘ํ•œ๋‹ค.
๊ธฐ๊ณ„ํ•™์ŠตํŠน๋ก 2 (Advanced Topics in Machine Learning 2)
  • ๊ธฐ๊ณ„ ํ•™์Šต๊ณผ ๊ด€๋ จ๋œ ์ตœ์‹ ์˜ ์ด๋ก  ๋ฐ ์‘์šฉ, ์ถ”์„ธ์— ๋Œ€ํ•˜์—ฌ ๋‹ค ๋ฃฌ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ๊ด€๋ จ ์—ฐ๊ตฌ ๋™ํ–ฅ์„ ์†Œ๊ฐœํ•˜๊ณ , ํ† ๋ก ํ•จ์œผ๋กœ์จ ํ•™์ƒ๋“ค์˜ ๋…ผ๋ฆฌ์  ์‚ฌ๊ณ ๋ ฅ๊ณผ ํ† ๋ก  ๋Šฅ๋ ฅ์„ ํ•จ์–‘ํ•œ๋‹ค.
๊ฐœ๋ฐฉํ˜• ์ธ๊ณต์ง€๋ŠฅํŠน๊ฐ•1 (Open AI Special Lecture 1)
  • ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ๊ธฐ์กด์˜ ๊ธฐ๊ณ„ํ•™์Šต๊ณผ ์‹ฌํ™”ํ•™์Šต์„ ํ†ตํ•ด ์–ป์€ ์ด ๋ก ์ ์ธ ์ง€์‹์„ ๋น…๋ฐ์ดํ„ฐ์™€ ์‹ค์ œ ๋„คํŠธ์›Œํฌ๋ฌธ์ œ์— ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ๋“ค์„ ๋ฐฐ์šฐ๊ณ  ์ƒˆ๋กœ์šด ๋ถ„์•ผ์—์„œ์˜ ํ™œ์šฉ ๋“ฑ์— ๋Œ€ํ•œ ์ฐฝ์˜ ์  ์—ฐ๊ตฌ๋ฅผ ๋„๋ชจํ•œ๋‹ค.
๊ฐœ๋ฐฉํ˜• ์ธ๊ณต์ง€๋ŠฅํŠน๊ฐ•2 (Open AI Special Lecture 2)
  • ๋ณธ ๊ณผ๋ชฉ์—์„œ๋Š” ๊ธฐ์กด์˜ ๊ธฐ๊ณ„ํ•™์Šต๊ณผ ์‹ฌํ™”ํ•™์Šต์„ ํ†ตํ•ด ์–ป์€ ์ด ๋ก ์ ์ธ ์ง€์‹์„ ๋น…๋ฐ์ดํ„ฐ์™€ ์‹ค์ œ ๋„คํŠธ์›Œํฌ๋ฌธ์ œ์— ์ ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ๋“ค์„ ๋ฐฐ์šฐ๊ณ  ์ƒˆ๋กœ์šด ๋ถ„์•ผ์—์„œ์˜ ํ™œ์šฉ ๋“ฑ์— ๋Œ€ํ•œ ์ฐฝ์˜ ์  ์—ฐ๊ตฌ๋ฅผ ๋„๋ชจํ•œ๋‹ค.
์‚ฐ์—…์ˆ˜ํ•™ ํŠน๋ก  1 (Industrial Mathematics Survey 1)
  • ์ˆ˜ํ•™์ด ์š”๊ตฌ๋˜๋Š” ๊ตฌ์ฒด์ ์ธ ์‚ฐ์—… ๋ฌธ์ œ์™€ ๊ทธ ํ•ด๊ฒฐ์„ ์œ„ํ•ด ์ ์šฉ๊ฐ€๋Šฅํ•œ ์ˆ˜ํ•™์  ๋„๊ตฌ๋“ค์„ ์กฐ์‚ฌํ•˜๊ณ , ํŒ€ ํ”„๋กœ์ ํŠธ ์ˆ˜ํ–‰ ์‹œ ํ•„์š”ํ•œ ๋ณด๊ณ ์„œ ์ž‘์„ฑ๊ณผ ๊ตฌ๋‘ ๋ฐœํ‘œ๋ฅผ ์—ฐ์Šตํ•œ๋‹ค.
ํ˜„์žฅ์‹ค์Šต1/2/3 (internship 1/2/3)
  • ICT ๊ด€๋ จ ์‚ฐ์—…์ฒด ํ˜น์€ ์—ฐ๊ตฌ์†Œ์—์„œ ์‹ค์ œ ์—ฐ๊ตฌ๊ฐœ๋ฐœ ์—…๋ฌด์— ์ธํ„ด์œผ๋กœ ์ฐธ์—ฌํ•จ์œผ๋กœ์จ ํ˜„์žฅ ์‹ค๋ฌด ๋Šฅ๋ ฅ์„ ๋ฐฐ์–‘ํ•œ๋‹ค.
์‚ฐํ•™์—ฐ๊ตฌ 1 (Industrial Research 1)
์‚ฐํ•™์—ฐ๊ตฌ 2 (Industrial Research 2)