ํฌ์ŠคํŠธ

๐ŸŒ’ AI, ๋กœ๋ด‡ ์‹ค์Šต ๊ณผ๋ชฉ

@ ํ™”3์ˆ˜2 โ†’ ๋‘˜ ๋‹ค 201ํ˜ธ

@ Brief ์š”์•ฝ
@ Manner ๋ฐฉ์‹
@ Exhibiting
@ Characteristics ํŠน์ง•
@ Fraud ์‚ฌ๊ธฐ
@ Investigate ์กฐ์‚ฌ

๐Ÿ’ซ @


@ U ์ค‘๊ฐ„๊ณ ์‚ฌ ์ถœ์ œ : ๊ธฐ๊ณ„๋Š” ์ƒ๊ฐํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๊ทผ๊ฑฐ๋ฅผ ๋“ค์–ด์„œ

Nature of Code
์ง€๋Šฅ? ๋ญ”์ง€ ๋งํ•  ์ˆ˜๋Š” ์—†์ง€๋งŒ ๋Š๋‚Œ์€ ์•Œ๊ณ  ์žˆ๋‹ค

๊ธฐํ˜ธ์ฃผ์˜ VS ์—ฐ๊ฒฐ์ฃผ์˜
๋จธ์‹ ๋Ÿฌ๋‹ VS ๋”ฅ๋Ÿฌ๋‹

๊ฐ•์ธ๊ณต์ง€๋Šฅ, Strong AI,
์ธ๊ฐ„ ์ˆ˜์ค€์˜ ์ง€๋Šฅ

์ธ๊ณต์ผ๋ฐ˜์ง€๋Šฅ, A General I, AGI
๋‹ค์–‘ํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ  ๋‹คํ–ฅํ•œ ์ง€๋Šฅ์  ํ™œ๋™์„ ์ˆ˜ํ•ผํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ
๊ฐ•์ธ๊ณต์ง€๋Šฅ, ์•ฝ์ธ๊ณต์ง€๋Šฅ ์‚ฌ์ด

์•ฝ์ธ๊ณต์ง€๋Šฅ, Weak AI, Narrow AI
ํŠน์ •ํ•œ ์ž‘์—…/๋„๋ฉ”์ธ์—์„œ ์ธ๊ฐ„๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ๋ฐœํœ˜ํ•˜๋Š”

ํŠœ๋ง ํ…Œ์ŠคํŠธ
Imitation/๋ชจ๋ฐฉ Game

์— ๋Œ€ํ•œ ๋ฐ˜๋ฐ•
์ค‘๊ตญ์–ด๋ฐฉ

์— ๋Œ€ํ•œ ๋ฐ˜๋ฐ•
System Response

@ ๋น„์š˜๋“œ EBS ์ง€๋Šฅ ๋งŒ๋“ค๊ธฐ

AI Application Programming

ํผ์ง€ ์ถ”๋ก 
I.E. ์นด๋ฉ”๋ผ ์ดˆ์ 

Lisp โ†’ Python?

NP-complete

๐Ÿ’ซ ๋จธ์‹ ๋Ÿฌ๋‹ VS ๋”ฅ๋Ÿฌ๋‹


๋จธ์‹ ๋Ÿฌ๋‹
Input โ†’ ํŠน์ง• ์ถ”์ถœ โ†’ ๋ถ„๋ฅ˜, ํšŒ๊ท€ โ†’ Output
์ธ๊ณต์‹ ๊ฒฝ๋ง ์—†์ด Classification ๋ถ„๋ฅ˜๋‚˜ Regression ํšŒ๊ท€ํ•˜๋Š”

Feature Extraction ํŠน์ง• ์ถ”์ถœ ์ดํ›„์—์„œ์•ผ ๋ถ„๋ฅ˜, ํšŒ๊ท€๊ฐ€ ๊ฐ€๋Šฅ
โ†’ ๋•Œ๋ฌธ์— Domain Knowledge ์ „๋ฌธ ์ง€์‹์ด ๋”ฅ๋Ÿฌ๋‹๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ๋” ์š”๊ตฌ๋จ

๋”ฅ๋Ÿฌ๋‹
Input โ†’ ํŠน์ง• ์ถ”์ถœ + ๋ถ„๋ฅ˜ ํšŒ๊ท€ โ†’ Output
์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ์ˆ˜์น˜ ํ•ด์„
์ธ๊ณต์‹ ๊ฒฝ๋ง ๋‚ด๋ถ€์—์„œ (์ž์ฒด์—์„œ) ๋ถ„๋ฅ˜, ํšŒ๊ท€๊ฐ€ ๊ฐ€๋Šฅ

๋จธ์‹ ๋Ÿฌ๋‹๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ์ข€ ๋” ๋ณต์žกํ•œ ๋ชจ๋ธ
โ†’ ๋ณต์žกํ•œ ๋ฐ์ดํ„ฐ(์–‘, ์ฐจ์›์ ์œผ๋กœ)๋ฅผ ๋‹ค๋ฃฐ ๋•Œ, ๋จธ์‹ ๋Ÿฌ๋‹๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ๋” ์ •ํ™•ํ•œ ๋ชจ๋ธ์„ ์ƒ์„ฑํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Œ

์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ๊ตฌ์„ฑํ•  ๋•Œ ๊ณ„์‚ฐ๋˜๋Š” Parameter๊ฐ€ ๊ต‰์žฅํžˆ ๋งŽ๊ณ , ๊ฒฐ์ •ํ•ด์•ผํ•˜๋Š” Hyper Parameter๊ฐ€ ๋งŽ๊ธฐ ๋•Œ๋ฌธ์—
๋ฐ์ดํ„ฐ, ๋ชจ๋ธ ์‚ฌ์ด์ฆˆ๊ฐ€ ํด ์ˆ˜๋ก ๋”ฅ๋Ÿฌ๋‹์ด ๋” ์ž˜ ๋งž๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Œ

๐Ÿ’ซ ๋จธ์‹ ๋Ÿฌ๋‹, ๋”ฅ๋Ÿฌ๋‹์˜ ์„ธ ํƒ€์ž…


ํšŒ๊ท€ - ์•ž์œผ๋กœ ์–ด๋–ป๊ฒŒ ๋˜๋‚˜?

Supervised ์ง€๋„ํ•™์Šต
Input, Output์„ ์ฃผ๊ณ  ๋ถ„๋ฅ˜, ํšŒ๊ท€

Unsupervised ๋น„์ง€๋„ํ•™์Šต
Input๋งŒ ์ฃผ๋Š”
๋ฏธ์ง€์ˆ˜ 2๊ฐœ, ๋ฐฉ์ •์‹ 1๊ฐœ = ํ’€ ์ˆ˜ ์—†๋Š” ๋ฌธ์ œ
Clustering ๊ตฐ์ง‘ํ™”

Reinforcement ๊ฐ•ํ™”ํ•™์Šต
Agent๊ฐ€ Action์„ Environment์— ์ทจํ–ˆ์„ ๋•Œ Reword๋ฅผ ์ฃผ๋Š”..?

๐Ÿ’ซ ๋จธ์‹ ๋Ÿฌ๋‹, ๋”ฅ๋Ÿฌ๋‹


Distribution ํ™•๋ฅ ๋ถ„ํฌ๊ฐ€ ์œ ์‚ฌํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ•™์Šตํ•ด์•ผํ•œ๋‹ค

์ „ํ†ต์ ์ธ ๋ฐฉ๋ฒ• : ๊ทœ์น™ ๋งŒ๋“ค๊ณ  ๊ณ ์น˜๊ณ 
๊ทธ๋ž˜์„œ ์ „๋ฌธ ์ง€์‹์ด ์žˆ์–ด์•ผ ํ•˜๋Š”

๋จธ์‹ ๋Ÿฌ๋‹ ๋ฐฉ๋ฒ• : ์ง€ํ˜ผ์ž ๊ณ„์† ์ตœ์ ํ™”ํ•˜๋Š”
๋ฐ์ดํ„ฐ๋งŒ ์ž˜ ๋ณผ ์ˆ˜ ์žˆ๋‹ค๋ฉด, ์ „๋ฌธ ์ง€์‹ ์—†์ด๋„

๐Ÿ’ซ ๋จธ์‹ ๋Ÿฌ๋‹


KNN Classifier
K Nearest Neighbors
๊ทผ์ฒ˜ K ๊ฐœ์˜ ์ด์›ƒ์„ ๊ฐ€์ง€๊ณ , ๋‚ด๊ฐ€ ๋ˆ„๊ตฌ์ธ์ง€
๋•Œ๋ฌธ์— K๋Š” ํ™€์ˆ˜๋กœ
๋ณต์žกํ•œ ๋””๋ฉ˜์…˜ ๋Š๋ฆฌ๋‹ค

Decision Tree
์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ ์ธก์ •์— ์ข‹๋‹ค
๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„๊ฐœํ•ด์„œ Entropy ๋ณต์žก๋„๊ฐ€ ๊ฐ€์žฅ ๋‚ฎ์€ ์ •๋„์˜ ๋ฐ์ดํ„ฐ๊นŒ์ง€ ๊ฐ€๋„๋ก
์ดํ›„ ๋ถ„๋ฅ˜, ํšŒ๊ท€
๋งŽ์ด ๋ชจ์ด๋ฉด Random Forest

Support Vector Machine SVM
๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„๋ฅ˜ํ•˜๋Š” ์„  Hyper Line, Hyper Plane
๋ถ„๋ฅ˜ ๋ณ„๋กœ ๊ฐ€์žฅ Hyper Line, Hyper Plane๊ณผ ๊ฐ€๊นŒ์šด ์š”์†Œ์™€์˜ ๊ฑฐ๋ฆฌ๊ฐ€ ์ตœ๋Œ€๊ฐ€ ๋˜๋„๋ก
Hyper Line, Hyper Plane์„ ๋งŒ๋“œ๋Š” Kernel Function โ†’ ์–ด๋ ค์›€
Cost Function, Object Function, ๋ชฉ์  ํ•จ์ˆ˜๋ฅผ ๋งŒ๋“ค๋•Œ๋Š” ์ด์ •๋„๋งŒ ์ดํ•ดํ•ด๋„

๐Ÿ’ซ ๋”ฅ๋Ÿฌ๋‹


์ธ๊ณต์‹ ๊ฒฝ๋ง, ์ฒด๋‚ด ์‹ ๊ฒฝ๋ง์„ ์ˆ˜ํ•™์ ์œผ๋กœ ํ‘œ์‹œํ•œ
๋‰ด๋Ÿฐ โ†’ Node
Input Layer, Hidden Layer, Output Layer

๋‚ด๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ๋งŒ ์ ํ•ฉํ•œ, Overfitting ๋ฌธ์ œ ์ฃผ์˜

Activation Function ํ™œ์„ฑํ•จ์ˆ˜
์ž๊ทน ๋ฐ›์•˜, ์ž„๊ณ„์น˜ ๊ฐ’ ์ด์ƒ์„ ๋„˜๊ฒจ์ฃผ๋Š” ์—ญํ• 
๊ณ„๋‹จ์‹ ํ•จ์ˆ˜, 0 ~ 1 ์‹œ๊ทธ๋ชจ์ด๋“œ ํ•จ์ˆ˜, ๋ ์œ  ํ•จ์ˆ˜ (์˜ค๋ฒ„ํ”ผํŒ… ๋ฐฉ์ง€)

Linearํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ข€ ๋” Non Linearํ•˜๊ฒŒ ๋ณต์žกํ•˜๊ฒŒ ๋งŒ๋“ค์–ด์ฃผ๋Š”

์ด ๊ธฐ์‚ฌ๋Š” ์ €์ž‘๊ถŒ์ž์˜ CC BY 4.0 ๋ผ์ด์„ผ์Šค๋ฅผ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค.