Machine Learning. CS 2750 Machine Learning. Administration. Lecture 1. Milos Hauskrecht 5329 Sennott Square, x4-8845

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1 CS 75 Mache Learg Lecture Mache Learg Mlos Hauskrecht 539 Seott Square, 5 people.cs.ptt.edu/~mlos/courses/cs75/ Admstrato Istructor: Prof. Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square, 5 TA: Yabg Xue ya@ptt.edu 53 Seott Square Offce hours: TBA

2 Who am I? Mlos Hauskrecht Professor of Computer Scece Secodary afflatos: Itellget Systems Program (ISP), Departmet of Bomedcal Iformatcs (DBMI) Research work: Mache learg, Data mg, Outler detecto, Probablstc modelg, Tme-seres models ad aalyss Applcatos to healthcare: EHR data aalyss, Patet motorg ad alertg, Patet safety Admstrato Study materal Hadouts, your otes ad course readgs Prmary tetbook: Chrs. Bshop. Patter Recogto ad Mache Learg. Sprger,.

3 Admstrato Study materal Other books: K. Murphy. Mache Learg: A probablstc perspectve, MIT Press,. J. Ha, M. Kamber. Data Mg. Morga Kauffma,. Fredma, Haste, Tbshra. Elemets of statstcal learg. Sprger, d edto,. Koller, Fredma. Probablstc graphcal models. MIT Press, 9. Duda, Hart, Stork. Patter classfcato. d edto. J Wley ad Sos,. T. Mtchell. Mache Learg. McGraw Hll, 997. Admstrato Homework assgmets: weekly Programmg tool: Matlab (free lcese, CSSD maches ad labs) Matlab Tutoral: et week Eams: Mdterm + Fal Mdterm before Sprg break Term project Lectures: Attedace ad Actvty 3

4 Tetatve topcs Itroducto Desty estmato Supervsed Learg Lear models for regresso ad classfcato. Mult-layer eural etworks. Support vector maches. Kerel methods. Usupervsed Learg Learg Bayesa etworks Latet varable models. Epectato mamzato. Clusterg Tetatve topcs (cot) Esemble methods Mture models Baggg ad boostg Dmesoalty reducto Feature selecto Prcpal compoet aalyss (PCA) Reforcemet learg

5 Mache Learg The feld of mache learg studes the desg of computer programs (agets) capable of learg from past eperece or adaptg to chages the evromet The eed for buldg agets capable of learg s everywhere. Eamples: tet, web page classfcato web search speech recogto Mache Learg Eamples: Image/vdeo classfcato, aotato ad retreval adaptve terfaces Car Race car commercal software Game playg 5

6 Learg process Model of data Learer (a computer program) processes data D represetg past epereces ad tres to buld a model that ether: Geerates approprate respose to future data, or Descrbes some meagful way the data see Eample: Learer sees a set of patet cases (patet records) wth correspodg dagoses. It ca ether try: to predct the occurrece of a dsease for future patets descrbe the depedeces betwee dseases, symptoms Types of learg problems Supervsed learg Takes data that cossts of pars (,y) Lears mappg f: (put) y (output, respose) Usupervsed learg Takes data that cosst of vectors Lears relatos amog vector compoets Groups/clusters data to the groups Reforcemet learg Lears mappg f: (put) y (desred output) From (,y,r) trplets where s a put, y s a respose chose by the user/system, ad r s a reforcemet sgal Ole: see, choose y ad observe r Other types of learg: Actve learg, Trasfer learg, Deep learg

7 Data: D { d, d,.., d d, y Supervsed learg a set of eamples s put vector, ad y s desred output (gve by a teacher) Objectve: lear the mappg f : X Y s.t. y f ( ) for all,.., Two types of problems: Regresso: X dscrete or cotuous Y s cotuous Classfcato: X dscrete or cotuous Y s dscrete } Supervsed learg eamples Regresso: Y s cotuous Debt/equty Eargs Future product orders Stock prce Data: Debt/equty Eargs Future prod orders Stock prce

8 Supervsed learg eamples Classfcato: Y s dscrete Label 3 Hadwrtte dgt (array of,s) Data: mage dgt Usupervsed learg Data: D { d, d,.., d} d vector of values No target value (output) y Objectve: lear relatos betwee samples, compoets of samples Types of problems: Clusterg Group together smlar eamples, e.g. patet cases Desty estmato Model probablstcally the populato of samples

9 Usupervsed learg eample Clusterg. Group together smlar eamples d Usupervsed learg eample Clusterg. Group together smlar eamples d

10 Usupervsed learg eample Clusterg. Group together smlar eamples Usupervsed learg eample Desty estmato. We wat to buld a probablty model P() of a populato from whch we drew eamples d

11 Usupervsed learg. Desty estmato A probablty desty of a pot the two dmesoal space Model used here: Mture of Gaussas Reforcemet learg We wat to lear: f : X Y We see eamples of puts but ot y We select y for observed from avalable choces We get a feedback (reforcemet) from a crtc about how good our choce of y was Iput Learer Output/acto y reforcemet Crtc The goal s to select outputs that lead to the best reforcemet

12 Learg: frst look Assume we see eamples of pars (, y) D ad we wat to lear the mappg f : X Y to predct y for some future We get the data D - what should we do? y Learg: frst look Problem: may possble fuctos f : X Y ests for represetg the mappg betwee ad y Whch oe to choose? May eamples stll usee! y

13 Learg: frst look Soluto: make a assumpto about the model, say, f ( ) a b y Learg: frst look Choosg a parametrc model or a set of models s ot eough Stll too may fuctos f ( ) a b Oe for every par of parameters a, b y

14 Learg: frst look We wat the best set of model parameters reduce the msft betwee the model M ad observed data D Or, ( other words) epla the data the best How to measure the msft? y Learg: frst look We wat the best set of model parameters reduce the msft betwee the model M ad observed data D Or, ( other words) epla the data the best How to measure the msft? y - The dfferece observed value of y ad model predcto

15 Learg: frst look We wat the best set of model parameters reduce the msft betwee the model M ad observed data D Or, ( other words) epla the data the best How to measure the msft? y Learg: frst look We wat the best set of model parameters reduce the msft betwee the model M ad observed data D Or, ( other words) epla the data the best How to measure the msft? Objectve fucto: Error fucto: Measures the msft betwee D ad M Eamples of error fuctos: Average Square Error Average Absolute Error ( y y f ( )) f ( ) 5

16 Learg: frst look Lear regresso problem Mmzes the squared error fucto for the lear model ( y f ( )) y Learg: frst look Applcato: A ew eample wth ukow value y s checked agast the model, ad y s calculated y f ( ) a b yy y =?

17 Supervsed learg: Classfcato Data D: pars (, y) where y s a class label: y eamples: patet wll be readmtted or o, has dsease (case) or o (cotrol) case cotrol Supervsed learg: Classfcato Fd a model f: X R, say f ( ) a b c that defes a decso boudary f () = that separates well the two classes Note that some eamples are ot correctly classfed case f () = cotrol 7

18 Supervsed learg: Classfcato A ew eample wth ukow class label s checked agast the model, the class label s assged case cotrol? Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a b 3. Choose the objectve fucto Squared error ( y f ( )). Learg: Fd the set of parameters optmzg the error fucto The model ad parameters wth the smallest error 5. Applcato Apply the leared model to ew data E.g. predct ys for ew puts usg leared f () CS 75 Mache Learg

19 Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a y b 3. Choose the objectve fucto Squared error ( y f ( )) -. Learg: - Fd the set of parameters optmzg the error fucto - The model ad parameters wth the smallest error 5. Applcato: Apply the leared model to ew data E.g. predct ys for ew puts usg leared f () CS 75 Mache Learg A learg system: basc cycle. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a b 3. Choose the objectve fucto Squared error y ( y f ( )). Learg: Fd the set of parameters optmzg the error fucto The model ad parameters wth the smallest error - 5. Applcato - Apply the leared model to ew data - E.g. predct ys for ew puts usg leared f () CS 75 Mache Learg 9

20 Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a b 3. Choose the objectve fucto Squared error ( y f ( )). Learg: Fd the set of parameters optmzg the error fucto The model ad parameters wth the smallest error 5. Applcato Apply the leared - model to ew data - E.g. predct ys for - ew puts usg leared f () Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a modelor a set of models (wth parameters) - - E.g. y a b - 3. Choose the objectve fucto Squared error ( y f ( )). Learg: Fd the set of parameters optmzg the error fucto The model ad parameters wth the smallest error 5. Applcato: Apply the leared model to ew data E.g. predct ys for ew puts usg leared f ()

21 Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a b 3. Choose the objectve fucto Squared error ( y f ( )). Learg: - - Fd the set of parameters - optmzg the error fucto The model ad - parameters wth the smallest error Applcato: Apply the leared model to ew data E.g. predct ys for ew puts usg leared f () Learg: frst look. Data: D { d, d,.., d}. Model selecto: Select a model or a set of models (wth parameters) E.g. y a b 3. Choose the objectve fucto Squared error ( y f ( )). Learg: Fd the set of parameters optmzg the error fucto The model ad parameters wth the smallest error 5. Applcato Apply the leared model to ew data Looks straghtforward, but there are problems.