* µî·Ï ¸¶°¨ µÇ¾ú½À´Ï´Ù *
´ëÇѱâ°èÇÐȸ ÀΰøÁö´É¸Ó½Å¿¬±¸È¸ 2018³âµµ À©ÅͽºÄð °³ÃÖ ¾È³»
´ëÇѱâ°èÇÐȸ ȸ¿ø ¿©·¯ºÐ,
´ëÇѱâ°èÇÐȸ ÀΰøÁö´É¸Ó½Å¿¬±¸È¸(ȸÀå ¼ÛÀ纹)¿¡¼´Â ´ÙÀ½°ú °°Àº ÀÏÁ¤À¸·Î ±â°èÇнÀ, µö·¯´× ¹× ½ÉÃþ°ÈÇнÀ¿¡ ´ëÇÑ À©ÅͽºÄðÀ» °³ÃÖÇÕ´Ï´Ù. ±Ý¹ø ±³À°¿¡¼´Â ±â°è ºÐ¾ß¿¡ Æ¯ÈµÈ À̷аú ½Ç½ÀÀ» ÁøÇàÇϸç, ¼¼¼Çº° µî·Ïµµ °¡´ÉÇϹǷÎ, ȸ¿ø ¿©·¯ºÐÀÇ ¸¹Àº Âü¿©¸¦ ºÎŹµå¸³´Ï´Ù.
¢Â Çà »ç ¸í : ÀΰøÁö´É¸Ó½Å¿¬±¸È¸ 2018³âµµ À©ÅͽºÄð
¢Â °³ÃÖÀÏÀÚ : 2018³â 1¿ù 16ÀÏ(È) ~ 19ÀÏ(±Ý) 4ÀÏ°£
¢Â °³ÃÖÀå¼Ò : °í·Á´ëÇб³ ÀÚ¿¬°èÄ·ÆÛ½º(¼¿ï½Ã ¼ººÏ±¸ ¾È¾Ïµ¿) âÀÇ°ü
¢Â ÇÁ·Î±×·¥ :
ÀÏÀÚ
|
½Ã°£
|
³»¿ë
|
°»ç
|
1. 16.(È)
|
10:00~18:00
|
±â°èÇнÀ
|
¿ÀÇö¼® ±³¼ö(GIST)
|
1. 17.(¼ö)
|
09:00~18:00
|
µö·¯´×
|
À̽Âö ±³¼ö(UNIST)
|
1. 18.(¸ñ)
|
09:00~12:30
|
µö·¯´×
|
À̽Âö ±³¼ö(UNIST)
|
14:00~18:00
|
½ÉÃþ°ÈÇнÀ
|
ÀÌ´öÁø ±³¼ö(±º»ê´ë)
|
1. 19.(±Ý)
|
09:00~17:00
|
½ÉÃþ°ÈÇнÀ
|
ÀÌ´öÁø ±³¼ö(±º»ê´ë)
|
¢Â °ÀÇ ³»¿ë ¼Ò°³
Á¦¸ñ
|
³»¿ë
|
<¼¼¼Ç1> ±â°èÇнÀ (Machine Learning)
|
- ÀÌ·Ð: Linear Model, Support Vector Machine (SVM), Decision Tree, K-means Clustering, Principal Component Analysis (PCA), Hidden Markov Model (HMM) - µ¥¸ð : MNIST Data, Áøµ¿µ¥ÀÌÅÍ - µ¥¸ð S/W : Python with scikit-learn
|
<¼¼¼Ç2> µö·¯´× (Deep Learning)
|
- ÀÌ·Ð : Neural Networks, Autoencoder, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Advanced Deep Learning (Style transfer, GAN) - ½Ç½À : MNIST Data - ½Ç½À S/W : Python with TensorFlow
|
<¼¼¼Ç3> ½ÉÃþ°ÈÇнÀ (Deep Reinforcement Learning)
|
- ÀÌ·Ð : ½ÉÃþ°ÈÇнÀ °æÇâ, °ÈÇнÀ °³³ä, Q-Learning, Q-Networks Deep Q-Learning (DQN), Double DQN, Dueling DQN - ½Ç½À : Q-Learning, Q-Networks, Deep Q-Learning, Double DQN, - ½Ç½À S/W : Python, TensorFlow, Theano, OpenAI Gym - µ¥¸ð : ½ÉÃþ°ÈÇнÀ±â¹Ý ÀÚÀ²ÁÖÇà ½Ã¹Ä·¹À̼Ç
|
* ½Ç½À °ü·Ã ¾È³» »çÇ×
- ½Ç½ÀÀ» ¿øÇϽô ºÐÀº °³º° ³ëÆ®ºÏÀ» ÁöÂüÇÏ¿©¾ß ÇÕ´Ï´Ù.
- ÇÊ¿äÇÑ ÇÁ·Î±×·¥ ¼³Ä¡´Â µî·ÏÇÑ ºÐ¿¡°Ô¸¸ ¸ÞÀÏ·Î 1¿ù 12ÀÏ°æ ¼ÛºÎÇØ µå¸± ¿¹Á¤ÀÔ´Ï´Ù.
¢Â µî·Ï ¾È³»
1. µî·Ï Á¢¼ö ¸¶°¨ : 2018. 1. 5.(±Ý)±îÁö ¢Ñ [µî·Ï ¹Ù·Î°¡±â]
* Á¤¿ø ÃÊ°ú ½Ã Á¶±â¿¡ ¸¶°¨µÉ ¼ö ÀÖ½À´Ï´Ù.
2. µî·Ïºñ
°úÁ¤¸í
|
ÀϹÝ
|
Çлý
|
ȸ¿ø
|
ºñȸ¿ø
|
ȸ¿ø
|
ºñȸ¿ø
|
<¼¼¼Ç1> ±â°èÇнÀ
|
100,000
|
150,000
|
60,000
|
90,000
|
<¼¼¼Ç2> µö·¯´×
|
150,000
|
200,000
|
100,000
|
130,000
|
<¼¼¼Ç3> ½ÉÃþ°ÈÇнÀ
|
150,000
|
200,000
|
100,000
|
130,000
|
* ¼¼¼Çº°·Î ½ÅûÀÌ °¡´ÉÇÕ´Ï´Ù.
* ºñȸ¿øÀÌ 2°³ ¼¼¼Ç ÀÌ»ó ¼ö° ½Ã 1°³ ¼¼¼Ç ºñ¿ë¸¸ ºñȸ¿ø°¡·Î Àû¿ëÇÕ´Ï´Ù.
* °ÀDZ³Àç´Â Á¦°øÇØ µå¸³´Ï´Ù.
3. µî·Ïºñ ³³ºÎ¹æ¹ý
¢Å ÀºÇàÀ» ÀÌ¿ëÇÑ ³³ºÎ¹æ¹ý
: ¿ì¸®ÀºÇà / ¿¹±ÝÁÖ ´ëÇѱâ°èÇÐȸ / 1005-403-359047
¢Å Ä«µå ¹× °èÁÂÀÌü ÀÌ¿ëÇÑ ³³ºÎ¹æ¹ý: »çÀüµî·Ï ÆäÀÌÁö¿¡¼ ÀüÀÚ°áÁ¦½Ã½ºÅÛ ÀÌ¿ë
¢Â Çà»ç ¹®ÀÇ
´ëÇѱâ°èÇÐȸ ¹Ú±â¼ ½ÇÀå(02-501-5035, manage@ksme.or.kr)
´ëÇѱâ°èÇÐȸ ÀΰøÁö´É¸Ó½Å¿¬±¸È¸ ȸÀå ¼ÛÀ纹