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Part 1. ÀΰøÁö´É(ìÑÍïò±Òö, Artificial Intelligence)ÀÇ °³¿ä
ÀΰøÁö´ÉÀÇ ¿ª»ç, ÀΰøÁö´ÉÀÇ ºÐ·ù, ƯÀÌÁ¡, ÀΰøÁö´É ¿øÄ¢, Àü¹®°¡ ½Ã½ºÅÛ, Æ©¸µ Å×½ºÆ®(Turing Test), Agent, ÀΰøÁö´É À±¸® µî¿¡ ´ëÇÑ ³»¿ëÀ¸·Î ÀÛ¼ºÇß½À´Ï´Ù. [°ü·Ã ÅäÇÈ - 17°³]
Part 2. ÀΰøÁö´É ¾Ë°í¸®Áò
À¯ÀüÀÚ ¾Ë°í¸®Áò, ±×¸®µð ¾Ë°í¸®Áò, »ó°üºÐ¼®, ȸ±ÍºÐ¼®, ±ºÁýºÐ¼®, ÀÚÄ«µå°è¼ö, ÇعְŸ®, ¿¬°ü±ÔÄ¢, ÁöÁöµµ/½Å·Úµµ/Çâ»óµµ, ¾Ó»óºíÇнÀ, Bagging°ú Boosting, Random Forest, Decision Tree, K-NN, ½Ã°è¿ ºÐ¼®, SVM(Support Vector Machine), K-Means, DBSCAN, Â÷¿ø Ãà¼Ò, Ư¡ ÃßÃâ, PCA, ICA, Q-Learning, Word2Vec µî¿¡ ´ëÇØ ÇнÀÇÒ ¼ö ÀÖµµ·Ï ÇÏ¿´½À´Ï´Ù. [°ü·Ã ÅäÇÈ - 41°³]
Part 3. ½ÉÃþ ½Å°æ¸Á »ó¼¼
±â°èÇнÀ, ÁöµµÇнÀ, ºñÁöµµ(ºñ°¨µ¶) ÇнÀ, °ÈÇнÀ, Deep Learning, Perceptron·Ð, È°¼ºÈ ÇÔ¼ö, ÇÏÀÌÆÛÆĶó¹ÌÅÍ, ¿ªÀüÆĹý, ±â¿ï±â ¼Ò½Ç ¹®Á¦, °æ»çÇÏ°¹ý, °úÀûÇÕ°ú ºÎÀûÇÕ, Dropout, ANN, DNN, CNN, RNN, LSTM, GRU, RBM, DBN, DQN, GAN, DL4J, È¥µ¿Çà·Ä, ±â°èÇнÀÀÇ Æò°¡ ¹æ¹ý, Á¤È®µµ, ÀçÇöÀ², Á¤¹Ðµµ, F1 Score µî¿¡ ´ëÇØ ÇнÀÇÒ ¼ö ÀÖµµ·Ï ÇÏ¿´½À´Ï´Ù. [°ü·Ã ÅäÇÈ - 35°³]
Part 4. ÀΰøÁö´É È°¿ë
À½¼ºÀνıâ¼ú, 꺿(ChatBot), °¡»ó°³Àκñ¼, ÆÐÅÏÀνÄ, ¸Ó½Å·¯´× ÆÄÀÌÇÁ¶óÀÎ(Machine Learning Pipeline), ÀÚ¿¬¾î ó¸®, ¿¢¼Òºê·¹ÀÎ(Exobrain)°ú Deepview, SNA, ÅÙ¼Ç÷οì, ÆÄÀ̼ÇÀÇ Æ¯Â¡ ¹× ÀÚ·áÇü, ÆÐ¼Ç ÀÇ·ù¿ë À̹ÌÁö¸¦ ºÐ·ùÇÏ´Â ´ÙÃþ ½Å°æ¸Á ¿¹½Ã µîÀ» ¼ö·ÏÇß½À´Ï´Ù. [°ü·Ã ÅäÇÈ - 14°³]
Part 5. ±âÃâ ¹× ¿¹»ó ÅäÇÈ
GPU¿Í CPU, ±³Â÷°ËÁõ±â¹ý, ¸Ó½Å·¯´× ¸ðµ¨ÀÇ Æò°¡ ¹æ¹ý, º¸¾È Ãë¾àÁ¡, Data Annotation, AIaaS(AI as a Service), ÀΰøÁö´É V ¸ðµ¨, ÀΰøÁö´É Á¡°ËÇÒ Ç׸ñ, ÀΰøÁö´É µ¥ÀÌÅÍ Ç°Áú ¿ä±¸»çÇ×, XAI, ÀΰøÁö´É µ¥ÀÌÅÍ Æò°¡¸¦ À§ÇÑ °í·Á»çÇ× µî¿¡ ´ëÇØ ÇнÀÇÒ ¼ö ÀÖ½À´Ï´Ù. [°ü·Ã ÅäÇÈ - 15°³] |
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PART 1. ÀΰøÁö´É(ìÑÍïò±Òö, Artificial Intelligence)ÀÇ °³¿ä
1. ÀΰøÁö´É(Artificial Intelligence)ÀÇ ¿ª»ç
2. ÀΰøÁö´É
3. ¾à ÀΰøÁö´É(Weak AI), ° ÀΰøÁö´É(Strong AI), ÃÊ ÀΰøÁö´É(Super AI)
4. ÀΰøÁö´É(AI)ÀÇ Æ¯ÀÌÁ¡(Singularity)
5. ¾Æ½Ç·Î¸¶(ASILOMA) AI(ÀΰøÁö´É) ¿øÄ¢
6. ±ÔÄ¢±â¹Ý¸ðµ¨
7. Ãßõ¿£Áø(Recommendation Engine)
8. Àü¹®°¡½Ã½ºÅÛ(Expert System)
9. Á¤±ÔÇ¥Çö½Ä°ú À¯ÇÑ ¿ÀÅ丶Ÿ
10. À¯ÇÑ ¿ÀŸ¸¶Å¸(Finite Automata)
11. Æ©¸µÅ×½ºÆ®(Turing Test)
12. ¿¡ÀÌÀüÆ®(Agent) - 1±³½ÃÇü ´ä¾È
13. ¿¡ÀÌÀüÆ®(Agent) - 2±³½ÃÇü ´ä¾È
14. ų ½ºÀ§Ä¡(Kill Switch)
15. Æ®·Ñ¸® µô·¹¸¶(Trolley Dilemma)
16. ÀΰøÁö´É(AI) À±¸®ÀÇ °³³ä, ÁÖ¿ä »ç·Ê, °í·Á»çÇ× ¹× ÃßÁø ¹æÇâ
17. ÀÌ¿ëÀÚ Áß½ÉÀÇ Áö´ÉÁ¤º¸»çȸ¸¦ À§ÇÑ ¿øÄ¢
PART 2. ÀΰøÁö´É ¾Ë°í¸®Áò(Algorithm)
18. À¯ÀüÀÚ ¾Ë°í¸®Áò(Genetic Algorithm)
19. ±×¸®µð ¾Ë°í¸®Áò(Greedy Algorithm)
20. »ó°üºÐ¼®(Correlation Analysis)
21. ȸ±ÍºÐ¼®(Regression Analysis)
22. ·ÎÁö½ºÆ½ ȸ±ÍºÐ¼®(Logistic Regression Analysis)
23. ±ºÁýºÐ¼®(Cluster Analysis) - 1±³½ÃÇü ´ä¾È
24. ±ºÁýºÐ¼®(Cluster Analysis) - 2±³½ÃÇü ´ä¾È
25. °èÃþÀû ±ºÁýºÐ¼®(Hierarchical Clustering)
26. ÀÚÄ«µå(Jaccard)°è¼ö
27. ÇعְŸ®(Hamming Distance)
28. À¯Å¬¸®µð¾È °Å¸®(Euclidean Distance)
29. À¯Å¬¸®µð¾È °Å¸®(Euclidean Distance)¸¦ °è»êÇϽÿÀ.
30. ¸¶ÇÒ¶ó³ëºñ½º °Å¸®(Mahalanobis Distance)¸¦ °è»êÇϽÿÀ.
31. Apriori(¿¬°ü±ÔÄ¢) ¾Ë°í¸®Áò
32. ÁöÁöµµ(Support), ½Å·Úµµ(Confidence), Çâ»óµµ(Lift)
33. »ç·Ê1(TV ±¸ÀԽà DVD ±¸ÀÔ), »ç·Ê2(¿ìÀ¯ ±¸ÀԽà ÁÖ½º ±¸ÀÔ)¿¡ ´ëÇØ ¿¬°ü±ÔÄ¢(ÁöÁöµµ, ½Å·Úµµ, Çâ»óµµ)À» Á¦½ÃÇϽÿÀ.
34. ¾Ó»óºíÇнÀ(Ensemble Learning)
35. ¸Ó½Å·¯´×(Machine Learning)¿¡ È°¿ë, ¾Ó»óºí(Ensemble) ±â¹ý
36. Bagging°ú Boosting ºñ±³
37. ·£´ý Æ÷·¹½ºÆ®(Random Forest)
38. ÀÇ»ç°áÁ¤Æ®¸®(Decision Tree)
39. K-NN(K-Nearest Neighbor)
40. ½Ã°è¿ ºÐ¼®
41. ½Ã°è¿ ºÐ¼®(ARIMA)
42. SVM(Support Vector Machine)- 1±³½ÃÇü ´ä¾È
43. SVM(Support Vector Machine)- 2±³½ÃÇü ´ä¾È
44. º£ÀÌÁî(Bayes)Á¤¸®
45. Å©±â¿Í ¸ð¾çÀÌ °°Àº °øÀÌ »óÀÚ A¿¡´Â °ËÀº °ø 2°³¿Í Èò°ø 2°³, »óÀÚ B¿¡´Â °ËÀº°ø 1°³¿Í Èò°ø 2°³°¡ µé¾î ÀÖ´Ù. µÎ »óÀÚ A, B Áß ÀÓÀÇ·Î ¼±ÅÃÇÑ ÇϳªÀÇ »óÀÚ¿¡¼ °øÀ» 1°³ ²¨³Â´õ´Ï °ËÀº°øÀÌ ³ª¿ÔÀ» ¶§, ±× »óÀÚ¿¡ ³²Àº °øÀÌ ¸ðµÎ Èò°øÀÏ È®·üÀº? (º£ÀÌÁî(Bayes)Á¤¸®¸¦ È°¿ëÇϽÿÀ)
46. K-Means
47. DBSCAN(Density Based Spatial Clustering with Application Notes)
48. Â÷¿øÃà¼Ò(Dimensionality Reduction)
49. Ư¡ÃßÃâ(Feature Extraction)
50. ÁÖ¼ººÐ ºÐ¼®, PCA(Principal Component Analysis)
51. µ¶¸³¼ººÐºÐ¼®, ICA(Independent Component Analysis)
52. ¸¶¸£ÄÚÇÁ °áÁ¤ ÇÁ·Î¼¼½º(Markov Decision Process, MDP)
53. Àº´Ð ¸¶¸£ÄÚÇÁ ¸ðµ¨(HMM-Hidden Markov Model)
54. ¸óÅ×Ä«¸¦·Î Æ®¸® Ž»ö(MCTS)
55. Q-Learning
56. Tokenization(Åä±ÙÈ), N-gram
57. Word2Vec
58. Word2VecÇнÀ¸ðµ¨, CBOW(Continuous Bag Of Words), Skip-gram
PART 3. ½ÉÃþ ½Å°æ¸Á »ó¼¼
59. ÀϹÝÀûÀÎ ÇÁ·Î±×·¥ ¹æ½Ä°ú Machine Learning(±â°èÇнÀ) ÇÁ·Î±×·¡¹Ö ¹æ½Ä
60. AI(Artificial Intelligence), ML(Machine Learning), DL(Deep Learning)
61. ±â°èÇнÀ(Machine Learning)
62. ÁöµµÇнÀ(Supervised Learning)
63. ºñÁöµµ(ºñ°¨µ¶)(Unsupervised Learning)ÇнÀ
64. °ÈÇнÀ(Reinforcement Learning)
65. µö·¯´×(Deep Learning)
66. MCP(McCulloch-Pitts)´º·±(Neuron)
67. Çñ ±ÔÄ¢(Hebb Rule)
68. ÆÛ¼ÁÆ®·Ð(Perceptron)
69. ¾Æ´Þ¶óÀÎ(Adaline- Adaptive Linear Neutron)
70. È°¼ºÈ ÇÔ¼ö(Activation Function) - 1
71. È°¼ºÈ ÇÔ¼ö(Activation Function) - 2
72. ½Å°æ¸Á ÇнÀ - FFNN(Feed Forward Neural Network)
73. µö·¯´×(Deep Learning)ÀÇ ÆĶó¹ÌÅÍ(Parameter)¿Í ÇÏÀÌÅÍÆĶó¹ÌÅÍ (Hyperparameter)¸¦ ºñ±³ÇÏ°í ÇÏÀÌÆÛÆĶó¹ÌÅÍÀÇ Æ©´×¹æ¹ýÀ» ¼³¸íÇϽÿÀ
74. ¿ªÀüÆĹý(Back-Propagation)
75. ±â¿ï±â ¼Ò½Ç ¹®Á¦(Vanishing Gradient Problem)
76. °æ»çÇÏ°¹ý(Gradient Descent)
77. °úÀûÇÕ(Overfitting)°ú ºÎÀûÇÕ(Underfitting), ÀûÇÕ(Bestfitting)
78. °úÀûÇÕ(Overfitting)°ú ºÎÀûÇÕ(Underfitting) ÇØ°á¹æ¾È
79. Dropout
80. ANN(Artificial Neural Network)
81. DNN(Deep Neural Network)
82. CNN(Convolution Neural Network)
83. RNN(Recurrent Neural Network)
84. LSTM(Long Short-Term Memory)
85. GRU(Gated Recurrent Unit)
86. RBM(Restricted Boltzmann Machine)
87. DBN(Deep Belief Network)
88. DQN(Deep Q-Network)
89. GAN(Generative Adversarial Networks) ¡´ GANÀÇ ÀÌÇØ ¡µ
90. DL4J(Deep Learning 4J)
91. È¥µ¿Çà·Ä(Confusion Matrix)
92. Machine Learning(±â°èÇнÀ)ÀÇ Æò°¡¹æ¹ý-Accuracy(Á¤È®µµ), Recall(ÀçÇöÀ²), Precision(Á¤¹Ðµµ)
93. F1 Score
PART 4. ÀΰøÁö´É È°¿ë
94. À½¼ºÀνıâ¼ú, ASR(Automatic Speech Recognition), NLU(Natural Language Understanding)
TTS(Text to Speech)
95. À½¼ºÀνÄ(Voice Recognition)
96. 꺿(ChatBot)
97. °¡»ó°³Àκñ¼(Virtual Personal Assistant)
98. ÆÐÅÏÀνÄ(Pattern Recognition)
99. ¸Ó½Å·¯´× ÆÄÀÌÇÁ¶óÀÎ(Machine Learning Pipeline)
100. ÀÚ¿¬¾î ó¸®
101. ¿¢¼Òºê·¹ÀÎ(Exobrain)
102. ¿¢¼Òºê·¹ÀÎ(Exobrain)°ú Deepview ±â¼ú¿ä¼Ò
103. µöºä(Deepview)
104. SNA(Social Network Analysis)
105. ÅÙ¼Ç÷Î(Tensorflow)
106. ÆÄÀ̼Ç(Python)ÀÇ Æ¯Â¡ ¹× ÀÚ·áÇü(Data Type)
107. ÆÐ¼Ç ÀÇ·ù¿ë À̹ÌÁö¸¦ ºÐ·ùÇÏ´Â ´ÙÃþ ½Å°æ¸ÁÀ» µé·Á°í ÇÑ´Ù. ÀÇ·ù¿ë À̹ÌÁö´Â ¹ÙÁö, Ä¡¸¶, ¼ÅÃ÷ µî 10°¡Áö À¯ÇüÀÇ Èæ¹é À̹ÌÁö(32*32 pixels)·Î ±¸¼ºµÇ¾î ÀÖ°í, ÇнÀ¿¡ ÅõÀÔÇÒ À̹ÌÁö µ¥ÀÌÅÍ´Â °ËÁõ ¹× Å×½ºÆ®¿ë µ¥ÀÌÅ͸¦ Á¦¿ÜÇÏ°í ÃÑ 48,000ÀåÀÌ´Ù. ÀÔ·ÂÃþ, Àº´ÐÃþ, Ãâ·ÂÃþÀÇ ¿ÏÀü¿¬°á(fully connected) 3°èÃþÀ¸·Î ±¸¼ºµÇ¾î ÀÖ°í Àº´ÐÃþÀÇ ´º·±°³¼ö´Â 100°³ÀÏ ¶§, ´ÙÀ½¿¡ ´ëÇÏ¿© ¼³¸íÇϽÿÀ
°¡. ½Å°æ¸Á ±¸¼ºµµ
³ª. ÀÔ·ÂÃþÀÇ ÀԷ°³¼ö, Ãâ·ÂÃþÀÇ ´º·± °³¼ö, ÇнÀÇÒ °¡ÁßÄ¡¿Í ÀýÆíÀÇ ÃÑ °³¼ö
´Ù. ¿øÇÖÀÎÄÚµù(One-Hot Encoding)°ú ¼ÒÇÁÆ®¸Æ½º(Softmax)ÇÔ¼ö
PART 5. ±âÃâ ¹× ¿¹»ó ÅäÇÈ
108.GPU(Graphic Processing Unit)¿Í CPU(Central Processing Unit)ÀÇ Â÷ÀÌÁ¡
109. ¸Ó½Å·¯´× ¸ðµ¨Àº ÇнÀ°ú ÇÔ²² °ËÁõ ¹× Æò°¡ °úÁ¤ÀÌ ÇÊ¿äÇÏ´Ù
°¡. ±³Â÷°ËÁõ(k-fold Cross Validation)±â¹ý¿¡ ´ëÇØ ¼³¸íÇϽÿÀ
³ª. ¸Ó½Å·¯´× ¸ðµ¨ÀÇ Æò°¡¹æ¹ý¿¡ ´ëÇÏ¿© ¼³¸íÇϽÿÀ
110. ¸Ó½Å·¯´× º¸¾È Ãë¾àÁ¡¿¡ ´ëÇØ ¼³¸íÇϽÿÀ.
°¡. ¸Ó½Å·¯´× ÇнÀ°úÁ¤¿¡¼ÀÇ Àû´ëÀû °ø°Ý 4°¡Áö
³ª. °¢°¢ Àû´ëÀû °ø°ÝÀÇ ¹æ¾î ±â¹ý
111. µ¥ÀÌÅÍ ¾î³ëÅ×À̼Ç(Data Annotation)
112. AIaaS(AI as a Service)¿Í µµÀԽà °í·Á»çÇ×
113. ÀüÀÌ ÇнÀ(Transfer Learning)
114. Pre-Crime
115. Àΰø½Å°æ¸ÁÀÇ ¿À·ù ¿ªÀüÆÄ(Backpropagation) ¾Ë°í¸®Áò
116. ¸Ó½Å·¯´×(Machine learning)ÀÇ ÇнÀ¹æ¹ýÀº Å©°Ô 3°¡Áö[ÁöµµÇнÀ(Supervised Learning), ºñÁöµµ ÇнÀ(Unsupervised Learning), °ÈÇнÀ(Reinforcement Learning)]·Î ºÐ·ùÇÑ´Ù. ÀΰøÁö´É¼ÒÇÁÆ®¿þ¾î °³¹ß ÇÁ·Î¼¼½º¸¦ V ¸ðµ¨ ±âÁØÀ¸·Î µµ½ÄÈÇÏ°í °ü·Ã±â¼úÀÇ Ãֽŵ¿Çâ ¹× ¾ÈÀüÃë¾àÁ¡À» ¼³¸íÇϽÿÀ
117. ÀΰøÁö´É °³¹ß°úÁ¤¿¡¼ ÁßÁ¡ÀûÀ¸·Î Á¡°ËÇÒ Ç׸ñ
118. ÀΰøÁö´É µ¥ÀÌÅÍ Ç°Áú ¿ä±¸»çÇ×
119. ¸óÅ×Ä«¸¦·Î(Monte Carlo) Æ®¸®(Tree) Ž»ö(MCTS)
120. µðÁöÅÐ Ä«¸£ÅÚ(Digital Cartel)
121. XAI(eXplainable AI)
122. ÀΰøÁö´É(AI) µ¥ÀÌÅÍ Æò°¡¸¦ À§ÇÑ °í·Á»çÇ× |
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