First Lecture of Machine Learning. Hung-yi Lee

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1 Firs Lcur of Machin Larning Hung-yi L

2 Larning o say ys/no Binary Classificaion

3 Larning o say ys/no Sam filring Is an -mail sam or no? Rcommndaion sysms rcommnd h roduc o h cusomr or no? Malwar dcion Is h sofwar malicious or no? Sock rdicion Will h fuur valu of a sock incras or no wih rsc o is currn valu? Binary Classificaion

4 Eaml Alicaion: Sam filring f : X Y { ys, no} Sam No sam 2 f ys 2 (h://sam-filr-rviw.onrviws.com/) f no

5 Eaml Alicaion: Sam filring f : X Y { ys, no} Wha dos h funcion f look lik? y f ys no P P ys ys How o sima P(ys )?

6 Eaml Alicaion: Sam filring To sima P(ys ), collc amls firs.. Earn fr fr Ys (Sam) Som words frqunly aar in h sam.g., fr 2 Win fr Ys (Sam) Us h frquncy of fr o dcid if an -mail is sam 3. Talk Ming. No (No Sam) Esima P(ys fr = k) fr is h numbr of fr in -mail

7 Rgrssion In raining daa, hr is no - mail conaining 3 fr. (ys fr ) (ys fr = ) = 0.4 (ys fr = 0 ) = 0. Frquncy of Fr ( fr ) in an -mail Problm: Wha if on day you rciv an -mail wih 3 fr.

8 Rgrssion f( fr ) = w fr + b (f( fr ) is an sima of (ys fr ) ) Sor w and b (ys fr ) Rgrssion Frquncy of Fr ( fr ) in an -mail

9 Rgrssion f( fr ) = w fr + b Th ouu of f is no bwn 0 and (ys fr ) Rgrssion Frquncy of Fr ( fr ) in an -mail Problm: Wha if on day you rciv an -mail wih 6 fr.

10 Logi ln fr vrical lin: Probabiliy o b sam (ys fr ) () is always bwn 0 and vrical lin: logi() logi ln

11 Logi f ( fr ) = w fr + b (f ( fr ) is an sima of logi() ) fr fr vrical lin: Probabiliy o b sam (ys fr ) () is always bwn 0 and vrical lin: logi() logi ln

12 Logi Sor w and b fr f 3 fr w 3 b.5 logi ln > 0.5, so ys f ( fr ) = w fr + b (f ( fr ) is an sima of logi() ) f fr w fr b 0 fr ln ys vrical lin: logi() logi ln

13 Mulil Variabls Considr wo words fr and hllo comu (ys fr, hllo ) () logi ln hllo fr

14 Mulil Variabls Considr wo words fr and hllo comu (ys fr, hllo ) () logi ln f fr, hllo Rgrssion w fr w2 hllo b hllo fr

15 Mulil Variabls Of cours, w can considr all words {, 2, N } in a dicionary b w w w f N N N 2 2 2, b w w N w w w 2 N 2 is o aroima logi() N ys 2, P :

16 Logisic Rgrssion w b aroima logi ln : P ys If h robabiliy = or 0, ln(/-) = +infiniy or infiniy Can no do rgrssion, 2 N Th robabiliy o b sam is always or 0. aars 3 ims 2 aars 0 im N aars im P ys 2 N 3 0

17 Logisic Rgrssion b w b w b w ln Sigmoid Funcion

18 Logisic Rgrssion Ys (Sam) N b w clos o b w No (no Sam) 2 2 b w 2 clos o 0

19 Logisic Rgrssion Ys No 2 N faur w w 2 w N wb b bias w This is a nuron in nural nwork. b 0

20 Mor han saying ys/no Muliclass Classificaion

21 Mor han saying ys/no Handwriing digi classificaion This is Muliclass Classificaion

22 Mor han saying ys/no Handwriing digi classificaion Simlify h qusion: whhr an imag is 2 or no Dscrib h characrisics of inu objc 2 Each il corrsonds o on dimnsion in N h faur faur of an imag

23 Mor han saying ys/no Handwriing digi classificaion Simlify h qusion: whhr an imag is 2 or no 2 2 or no N

24 Mor han saying ys/no Handwriing digi classificaion Binary classificaion of, 2, 3 If y 2 is h ma, hn h imag is 2. y or no 2 y 2 2 or no N y 3 3 or no

25 This is no good nough

26 Limiaion of Logisic Rgrssion w w 2 2 b a ys no a a ys no w w2 2 b 0 0 Inu Ouu No 0 Ys 0 Ys No Ys No

27 So w nd nural nwork Inu Layr Layr 2 Layr L Ouu y 2 y 2 N y M D mans many layrs

28 Thank you for your lisning!

29 Andi

30 Mor rfrnc h:// GD_REG_on_NN/lcur_nos/logisic_rgrssion_l oss_funcion/logisic_rgrssion_loss.df h://mahgochas.blogso.w/20/0/why-is-rrorfuncion-minimid-in.hml hs://cs.nyu.du/~yann/alks/lcun nonconv.df h:// h://grgor.chruala.m/ars/ml4nl/linarclassifirs.df

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