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¡Ø ¾Ë·Áµå¸³´Ï´Ù.¹ßÇàÀÏ : 2022-02-11
Ã¥¼Ò°³ÀÌ Ã¥ÀÌ ¼ÓÇÑ ºÐ¾ß±Í¿©¿î ij¸¯Å͵é°ú ÇÔ²² ¶°³ª´Â Áñ°Å¿î ¼±Çü´ë¼ö ¿©Çà! µ¥ÀÌÅ͸¦ Àß ´Ù·ç±â À§ÇØ ²À ÇÊ¿äÇÑ ±âÃÊ Áö½ÄÀÎ ¼±Çü´ë¼ö! ±âº»ÀûÀÎ º¤ÅÍ, Çà·Ä À̷кÎÅÍ SVD, PCA µî Â÷¿ø Ãà¼Ò±îÁö ÀÌ Ã¥À» ÅëÇØ µ¿ÈÃ¥À» ÀÐµí ºÎ´ã ¾øÀÌ ¹è¿ö º¸¼¼¿ä. »ó¼¼À̹ÌÁö¸ñÂ÷ÀúÀÚ ¼Ò°³ Ãßõ»ç º£Å¸ ¸®´õ ¸®ºä ½ÃÀÛÇϸç Chapter 01 vectorµéÀÇ À̾߱â 01-1 vector°¡ ¿òÁ÷ÀÏ ¼ö ÀÖ´Â ¹æÇâ 01-2 vector°¡ º¼ ¼ö ÀÖ´Â ¹æÇâ°ú dot product¶ó´Â vectorµéÀÇ Ãã 01-3 vectorÀÇ norm°ú projection Chapter 02 matrixµéÀÇ À̾߱â 02-1 ÇÑ °³ ÀÌ»óÀÇ vectorµéÀ» ¸ðÀÌ°Ô ÇÒ ¼ö ÀÖ´Â Á¸Àç 02-2 column rank of A 02-3 rref¸¦ Ãç¼ A ¾È¿¡ column vector¸¦ B¿Í D·Î ³ª´² ¸ðÀÌ°Ô Çϱâ 02-4 matrix transpose¿Í inverse 02-5 inverse¸¦ ÇÏ´Â ÀÌÀ¯´Â identity matrix¸¦ ¸¸µé±â À§ÇØ Chapter 03 matrix ¾È vectorµéÀÌ »ç´Â space 03-1 vectorµéÀÌ »ç´Â space 03-2 rank-nullityÀÇ ¹ýÄ¢ 03-3 subspaceÀÇ basis ã´Â ¹ý 03-4 orthogonal complement subspace¿Í full rank matrix 03-5 orthogonal matrix Chapter 04 ¸ñÀûÁö·Î ¾È³»ÇÏ´Â Áöµµ ¸¸µå´Â ¹ý 04-1 ¸ñÀûÁö±îÁö °¡´Â ±æÀ» ÇÑ °¡Áö ¾Ë·Á ÁÖ´Â Áöµµ ¸¸µå´Â ¹ý 04-2 ¹Ù¶óº¸´Â Áöµµ ¸¸µå´Â ¹ý 04-3 ¹«ÇÑÈ÷ ¸¹Àº ´Ù¸¥ ±æ·Î vector bÀÇ ¹æÇâÀ» ¾Ë·Á ÁÖ´Â Áöµµ ¸¸µå´Â ¹ý 04-4 ¼±Çü ÇÁ·Î±×·¡¹Ö(linear programming) Chapter 05 õõÈ÷ °É¾î°¡±â 05-1 eigendecomposition 05-2 eigenvalue¸¦ º¸°í A°¡ singularÀÎÁö nonsingularÀÎÁö ¾Ë¾Æº¸´Â ¹ý 05-3 diagonalizable matrix 05-4 matrix¿Í vectorÀÇ ´ëÈ 05-5 orthogonally diagonalizable matrix 05-6 Markov chain 05-7 Markov chain with absorbing state Chapter 06 µÎ °³ÀÇ ´Ù¸¥ space »çÀÌÀÇ Ãß¾ï 06-1 singular value decomposition(SVD) 06-2 pseudoinverse 06-3 dot product, matrix multiplication, linear combination ±×¸®°í projection¿¡ ´ëÇÑ ÂªÀº À̾߱â 06-4 principal component analysis(PCA) ¸¶Ä¡¸ç ÀÌ Ã¥¿¡¼ »ç¿ëµÈ R ÄÚµå ¸ðÀ½ ã¾Æº¸±â
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