1. Lehrstuhl fuer Maschinelles Lernen und Natuerlich Sprachliche Systeme Albrecht Zimmernann, Tayfun Guerel, Kristian Kersting , Prof. Dr. Luc De Raedt, Machine Learning in Games Crash Course on Machine Learning

3. Behavioural Cloning / Verhaltensimitation plays logs plays User model

5. TD-Gammon by Tesauro (1995)

11. Other Prediction Problems Process optimization Customer Purchase behavior Customer retention

14. Software that Customizes to User

15. Lehrstuhl fuer Maschinelles Lernen und Natuerlich Sprachliche Systeme Albrecht Zimmernann, Tayfun Guerel, Kristian Kersting , Prof. Dr. Luc De Raedt, Machine Learning in Games Crash Course on Decision Tree Learning Refund MarSt TaxInc YES NO NO NO Yes No Married Single, Divorced < 80K > 80K

17. Illustrating Classification Task

20. Decision Tree for PlayTennis Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No

21. Decision Tree for PlayTennis Outlook Sunny Overcast Rain Humidity High Normal No Yes Each internal node tests an attribute Each branch corresponds to an attribute value node Each leaf node assigns a classification

22. Decision Tree for PlayTennis Outlook Temperature Humidity Wind PlayTennis Sunny Hot High Weak ? No Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No

23. Decision Tree for Conjunction Outlook Sunny Overcast Rain Wind Strong Weak No Yes No Outlook=Sunny  Wind=Weak No

24. Decision Tree for Disjunction Outlook Sunny Overcast Rain Yes Outlook=Sunny  Wind=Weak Wind Strong Weak No Yes Wind Strong Weak No Yes

25. Decision Tree for XOR Outlook Sunny Overcast Rain Wind Strong Weak Yes No Outlook=Sunny XOR Wind=Weak Wind Strong Weak No Yes Wind Strong Weak No Yes

36. Another Example Humidity High Normal [ 3 +, 4 -] [ 6 +, 1 -] S= [9+,5-] E=0.940 Gain(S,Humidity) =0.940-(7/14)*0.985 – (7/14)*0.592 =0.151 E=0.985 E=0.592 Wind Weak Strong [ 6 +, 2 -] [ 3 +, 3 -] S= [9+,5-] E=0.940 E=0.811 E=1.0 Gain(S,Wind) =0.940-(8/14)*0.811 – (6/14)*1.0 =0.048

37. Yet Another Example: Playing Tennis No Strong High Mild Rain D14 Yes Weak Normal Hot Overcast D13 Yes Strong High Mild Overcast D12 Yes Strong Normal Mild Sunny D11 Yes Strong Normal Mild Rain D10 Yes Weak Normal Cold Sunny D9 No Weak High Mild Sunny D8 Yes Weak Normal Cool Overcast D7 No Strong Normal Cool Rain D6 Yes Weak Normal Cool Rain D5 Yes Weak High Mild Rain D4 Yes Weak High Hot Overcast D3 No Strong High Hot Sunny D2 No Weak High Hot Sunny D1 Play Tennis Wind Humidity Temp. Outlook Day

38. PlayTennis - Selecting Next Attribute Outlook Sunny Rain [ 2 +, 3 -] [ 3 +, 2 -] S= [9+,5-] E=0.940 Gain(S,Outlook) =0.940-(5/14)*0.971 -(4/14)*0.0 – (5/14)*0.0971 =0.247 E=0.971 E=0.971 Over cast [ 4 +, 0 ] E=0.0 Gain(S,Humidity) = 0.151 Gain(S,Wind) = 0.048 Gain(S,Temp) = 0.029

39. PlayTennis - ID3 Algorithm Outlook Sunny Overcast Rain Yes [D1,D2,…,D14] [9+,5-] S sunny =[D1,D2,D8,D9,D11] [2+,3-] ? ? [D3,D7,D12,D13] [4+,0-] [D4,D5,D6,D10,D14] [3+,2-] Gain(S sunny , Humidity)=0.970-(3/5)0.0 – 2/5(0.0) = 0.970 Gain(S sunny , Temp.)=0.970-(2/5)0.0 –2/5(1.0)-(1/5)0.0 = 0.570 Gain(S sunny , Wind)=0.970= -(2/5)1.0 – 3/5(0.918) = 0.019

40. ID3 Algorithm Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No [D3,D7,D12,D13] [D8,D9,D11] [D6,D14] [D1,D2] [D4,D5,D10]

41. Hypothesis Space Search ID3 + - + A2 - A4 + - A2 - A3 - + + - + + - + A1 - - + + - + A2 + - -

43. Converting a Tree to Rules R 1 : If (Outlook=Sunny)  (Humidity=High) Then PlayTennis=No R 2 : If (Outlook=Sunny)  (Humidity=Normal) Then PlayTennis=Yes R 3 : If (Outlook=Overcast) Then PlayTennis=Yes R 4 : If (Outlook=Rain)  (Wind=Strong) Then PlayTennis=No R 5 : If (Outlook=Rain)  (Wind=Weak) Then PlayTennis=Yes Outlook Sunny Overcast Rain Humidity High Normal Wind Strong Weak No Yes Yes Yes No