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Korea Robotics Society

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AI & Robotics Summer School 2018

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Çà»ç¸í Á¦ 3ȸ ÀΰøÁö´É ¹× ·Îº¸Æ½½º ¿©¸§Çб³ (AI & Robotics Summer School 2018)
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ÃÊ·Ï º» °­ÀÇ¿¡¼­´Â ±â°èÇнÀÀÇ ±âº»°³³äÀ» ÀÌÇØÇÏ°í Multilayer Perceptron (MLP)¿¡¼­ Support Vector Machine (SVM)À» °ÅÃÄ µö·¯´×À¸·ÎÀÇ ¹ßÀü°úÁ¤À» °£´ÜÇÑ ¿¹Á¦¸¦ Áß½ÉÀ¸·Î ÇнÀÇÑ´Ù. °­Àdz»¿ëÀº ´ÙÀ½°ú °°´Ù.
  1. Multilayer Perceptron (MLP)
  2. Support Vector Machine (SVM)
  3. Deep Neural Network (DNN)
  4. NumPy¿Í Tensorflow¸¦ ÀÌ¿ëÇÑ ±¸Çö
¾à·Â 1992 B.S. in Electronic Engr., Yonsei Univ.
1994 M.S. in Electronic Engr., Yonsei Univ.
1999 Ph.D. in Electronic Engr., Yonsei Univ.
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ÃÊ·Ï º» °­ÀÇ¿¡¼­´Â Convolutional Neural Network (CNN)À» ÀÌ¿ëÇÑ ÀÀ¿ëÀÇ ´ëÇ¥ÀûÀÎ ¿¹·Î detectorÀÇ ±¸Á¶¿¡ ´ëÇÏ¿© °£´ÜÇÑ ¿¹Á¦¸¦ Áß½ÉÀ¸·Î ÇнÀÀ» ÇÑ´Ù. °­Àdz»¿ëÀº ´ÙÀ½°ú °°´Ù.
  1. Faster R-CNN
  2. SSD (Single Shot MultiBox Detector)
  3. YOLO (You Look Only Once)
¾à·Â 1992 B.S. in Electronic Engr., Yonsei Univ.
1994 M.S. in Electronic Engr., Yonsei Univ.
1999 Ph.D. in Electronic Engr., Yonsei Univ.
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Á¦¸ñ Time Series and Seq-to-Seq Modeling
ÃÊ·Ï º» °­ÀÇ¿¡¼­´Â ½Ã°è¿­ (time series) µ¥ÀÌÅ͸¦ ó¸®Çϱâ À§ÇÑ µö·¯´× ±¸Á¶¿Í Sequence-to-sequence ¸ðµ¨¸µ ±â¹ý¿¡ ´ëÇÑ ÀÌ·Ð ¹× ÀÀ¿ë ºÐ¾ß¿¡ ´ëÇØ ¼³¸íÇÏ°í, °£´ÜÇÑ ¿¹Á¦¸¦ ÅëÇØ ÀÌÇصµ¸¦ ³ôÀδÙ. °­ÀÇ ³»¿ëÀº ´ÙÀ½°ú °°´Ù.
  1. RNN, LSTM, GRU
  2. Sequence-to-sequence model
  3. ÀÀ¿ë ºÐ¾ß ¹× ½Ç½À
¾à·Â 1989 B.S. in Electronic Engr., Yonsei Univ.
1991 M.S. in Electronic Engr., Yonsei Univ.
1995 Ph.D. in Electronic Engr., Yonsei Univ.
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Á¦¸ñ µö·¯´× ±â¹Ý ¿µ»ó Á¤ÇÕ
ÃÊ·Ï º» °­ÀÇ¿¡¼­´Â ½ºÅ×·¹¿À Á¤ÇÕ, Àǹ̷РÁ¤ÇÕÀ» Æ÷ÇÔÇÑ Á¤ÇÕ ºÐ¾ßÀÇ ¿¬±¸ È帧¿¡ ´ëÇØ ¾Ë¾Æº»´Ù. µö·¯´× Àû¿ë ÀÌÀüÀÇ ¿µ»ó Á¤ÇÕÀÇ ¹ßÀü »çÇ×À» »ìÆ캻 ÈÄ, ÃÖ±Ù µö·¯´× ±â¹ÝÀ¸·Î Á¤ÇÕÀÚ ÇнÀ, ¸ÞÆ®¸¯ ÇнÀ µîÀÌ ¿µ»ó Á¤ÇÕ¿¡ ÀÀ¿ëµÇ°í ÀÖ´Â »ç·Ê¸¦ »ìÆ캻´Ù.
¾à·Â 2008 B.S. in Electronic Engr., Yonsei Univ.
2013 Ph.D. in Electronic Engr., Yonsei Univ.
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ÃÊ·Ï º» °­ÀÇ¿¡¼­´Â °­È­ÇнÀÀÇ ±âÃÊ ÀÌ·Ð ¹× ±íÀº °­È­ÇнÀ °ü·Ã ÃֽŠ¿¬±¸ ¹× ÀÀ¿ëÀ» ¼Ò°³ÇÑ´Ù. °­È­ÇнÀ ±âÃÊ ÀÌ·Ð ¹× ±íÀº °­È­ÇнÀ ÀÀ¿ëÀ» ½Ç½ÀÀ» ÅëÇؼ­ ÀÍÈù´Ù. ¶ÇÇÑ, °­È­ÇнÀÀÌ ·Îº¿¿¡ Àû¿ëµÈ ¿¹Á¦¸¦ ±¸Ã¼ÀûÀ¸·Î ¼Ò°³Çϸç, °­È­ÇнÀÀ» ¼³¸í°¡´ÉÇÏ°Ô ÇÏ´Â ÃֽŠ¿¬±¸¸¦ ¼Ò°³ÇÑ´Ù.

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  1. Basics of Reinforcement Learning(RL)
    • Value iteration & Policy iteration
    • Q-Learning
  2. Model-free & Model-based RL
    • Temporal Difference Learning
    • Linear Quadratic Regulator
  3. Q-Learning ½Ç½À

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  1. Deep RL
    • Deep Q-Learning
    • Policy Gradients
  2. Policy Gradients ½Ç½À
  3. RL for Robots and Explainable RL
¾à·Â 2003 B.S. in Computer Engr. Seoul National Univ.
2012 Ph.D. in Computer Sci., Univ. of Illinois at Urbana-Champaign