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Unformatted text preview: Course Work: Homeworks 0 There will be 5 homeworks o Homework 0 will not count towards your grade a You should be able to solve all problems in HWO
o If you have trouble in HWO, you will have trouble later CSci 5512: Artificial Intelligence  ° H°mew°rk subml55i°n$
: Using the submit system. only pdf (encouraged) in Paper copy at the beginning of the class
Instructor: Arindam Banerjee o All programming in Matlab, Java, C, Python 0 Follow the instructions for programming assignments
o Other languages will not receive any credit January 18, 2012 0 Ok to discuss with others, you have to write on your own, put
the name(s) of people you discussed with Instructor: Arindam Banerjee Instructor: Arindam Banerjee —
General Information Course Work: Homeworks (Contd.) 0 Course Number: CSci 5512 0 Class: Mon Wed 12:4502:00 pm 0 Dates: . Location Keller Ha” 3_111 o HW 0: Posted Jan 18 (Wed), due Jan 23 (Mon) at 12:45 pm
' o HW 1: Jan 30 (Mon), due Feb 13 (Mon) at 12:45 pm 0 HW 2: Feb 20 (Mon), due Mar 05 (Mon) at 12:45 pm
_ HW 3: Mar 26 (Mon), due Apr 09 (Mon) at 12:45 pm
0 TA: James Parker, [email protected] o HW 4: Apr 16 (Mon), due May 30 (Mon) at 12:45 pm 0 Instructor: Arindam Banerjee, banerjee©cs.umn.edu 0 Late submission policy: Late by 0—24 hrs: 25% deducted from actual score Late by 2448 hrs: 50% deducted from actual score Late by more than 48 hrs: Will receive a zero All late submissions must be submitted in pdf using Submit 0 Office Hours: a Arindam: EE/CS 6—213 Mon Wed 02:00—03:00 pm
a James: EE/CS 2216 Thu 10:0011:00 am, Fri 01:0002:00 pm 0 Web page: http://www—users.itlabs.umn.edu/classes/Spring—
2012/csci5512 Instructor: Arindam Banerjee Instructor: Arindam Banerjee Course Work: Exams, etc. Topics 0 Exams . . .
o Mid—Term: Mar 26 (Mon) in class ° Quant'fy'"g uncerta'my
: Final: May ?? (??), ?:00?:00 o Probabilistic Reasoning
° CI°Sed b°°k' Closed "_°tes exam _ o Probabilistic Reasoning over Time
a Allowed 1 sheet for midterm, 2 sheets for final 0 Making Simple Decisions
0 Participation: 0 Making Complex Decisions
0 Ask questions 0 Learning from Examples
' Participate in discussim‘s o Learning Probabilistic Models 0 Web Links: 0 Reinforcement Learning o Online Submission for homeworks 0 Natural Language Processing
0 Bulletin Board for discussions Instructor: Arindam Banerjee Instructor: Arindam Banerjee — o Uncertainty inherent in decision problems 0 Partial knowledge of environment
in Environment may be complex or stochastic
o Existence of other agents o Homework: 50 % = 4 x 12.5 % o MidTerm: 20 % 0 Final: 25 % o Firstorder logic is inappropriate for such domains 0 Several different events are possible
0 Participation: 5 % 0 Each event 0 Has a different "probability" of happening 0 Has different "utility" or “payoffs”
o In order to pass the course: : Average on exams (midterm and final) must be at least 50%. Rational decisions maximize expected utility
° Overall Sc°re must be at least 50% Decision Theory E Utility Theory + Probability Theory Instructor: Arindam Banerjee Instructor: Arindam Banerjee ° Game of Monopoly 0 Random variables are mappings of events (to real numbers) . i o MappingX:Qi—>R
: i' V? 0 Any event w maps to X(w) 0 Example: o Tossing a coin has two possible outcomes
o Denoted by {H, T} or {0,1} ° PUVSUIt With constraints a Fair coin has uniform probabilities
: Chasing in Manhattan
 1 1
o Robotic teams for search/rescue p(X = 0) = _ P(X = 1) = _
2 2 o The Stock Market 0 Random variables (r.v.s) can be 0 Discrete, e.g., Bernoulli
0 Continuous. e.g., Gaussian Instructor: Arindam Banerjee Instructor: Arindam Banerjee — — o For a continuous r.v. a Distribution function F(x) = P(X S x)
9 Corresponding density function f(x)dx = dF(x) 0 Sample space 9 of events
0 Each "event" to E (2 has an associated "measure"
a Probability of the event P(w) a Note that x
o Axioms of Probability: F(X) =/ f(t)dt
0 Va), P(w) 6 [0,1] t=—oo
o P(Q) = 1 _
, p(w1 Uwz) : pom) + p(w2) _ P(wl mm) o For a discrete r.v.
o Probability mass function f(x) = P(X = x) = P(X)
I . . a We will call this the probability of a discrete event
° N°te We are bang '"f0’ma' . Distribution function P(X) = P(X g x) Instructor: Arindam Banerjee Instructor: Arindam Banerjee Joint Distributions, Marginals Independence o For two continuous r.v.s X1,X2
0 Joint distribution F(X1,X2) = P(X1 S X1,X2 S x2)
o Joint density function f(x1,X2) can be defined as before ° JOInt Pr0bablllty P(Xl = X17X2 = X2)
9 The marginal probability density o X1,X2 are different dice 0 X1 denotes if grass is wet, X2 denotes if sprinkler was on 00
f(X1) = / f(X1,X2)dX2
X2=—°° 0 Two r.v.s are independent if
c For two discrete r.v.s X1,X2
0 Joint probability f(X1,X2) = = X1,X2 2 X2) = P(X1,X2)
o The marginal probability P X = X = P X = X ,X = X a Two different dice are independent
( 1 1) x22 ( 1 1 2 2) o If sprinkler was on, then grass will be wet => dependent P(X1 = x1,X2 = x2) = P(Xl = x1)P(X2 2 X2) 0 Can be extended to joint distribution over several r.v.s
0 Many hard problems involve computing marginals Instructor: Arindam Banerjee Instructor: Arindam Banerjee — Grass Wet Grass Dry
Sprinkler On 0.4 Sprinkler Off 0.2 o The expected value of a r.v. X o For continuous r.v.s E[X] = fx xp(x)dx o Inference problems: ° For discrete r'V' E[X] = Z:iX’pi o Given 'grass wet' what is P('sprinkler on'‘grass wet')
o Expectation is a linear operator o Given ‘symptom' what is P(‘disease'‘symptom') o For any r.v.s X, Y, the conditional probability P(xly) = P) 0 Since P(x, y) = P(yx)P(x), we have P(ylx) = —P (1?: (y) o Expressing ‘posterior' in terms of 'conditional': Bayes Rule Instructor: Arindam Banerjee Instructor: Arindam Banerjee E[aX—l— bY + c] = aE[X] + bE[Y] + c Product Rule & Independence Conditional Independence 0 Product Rule: 0 X and Y are conditionally independent given Z
° For X1,X2.P(X1iX2)= P(X1)P(X2IX1)
. For X1,X2,X3, P(X1,X2,X3) = P(X1)P(X2X1)P(X3X1,X2) P(Xi VIZ) = P(XIZ)P(YZ)
o In general, the chain rule n
P(X1, ,X,,) = HP(X,X1,...,X,_1) ° Example:
[:1 P( Toothache, Catch Cavity)
= P( Toothache Cavity)P(Catch Cavity) 0 Joint distribution of n Boolean variables
0 Specification requires 2" — 1 parameters 0 Recall Independence: _, . .. . . . . .
O For X1,X2' P(thz) = P(X1)P(X2) o Conditional Independence Simplifies Jomt distributions a In general " o Often reduces from exponential to linear in n PX,,X,, = PX,
“ I I I P(X,Y,Z)=P(Z)P(XZ)P(YZ) 0 Independence reduces specification to n parameters Instructor: Arindam Banerjee Instructor: Arindam Banerjee — —
Independence Naive Bayes Model 0 If X1, . . . ,X,, are independent given Y A
/ I Cavity ’1
CaVIty decomposesinm {cothache Catct> P(Y, X1, _ _ _ a X") = H y)
[:1 Toothache Catch ‘ \V/
Weather
0 Exa m p le: P(Cavity, Toothache, Catch) 0 Consider 4 variables: Toothache,Catch,Cavity,Weather = P(Cavity)P( ToothacheCavity)P(CatchCavity)
0 Independence implies o More generally
I1
P(TOOthaCheacatCha caViWa weather) P(Cause, Effectl, . . . , Effect”) = P(Cause) H P(EffectilCause)
= P(Toothache, Catch, Cavity)P(Weather) i=1 0 In terms of the joint distribution: ® ®
in 32 parameters reduced to 12
o For boolean variables 2" — 1 reduces to n ’ I ‘
0 Absolute independence powerful but rare ' ' ° Instructor: Arindam Banerjee Instructor: Arindam Banerjee ...
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This note was uploaded on 02/07/2012 for the course CSCI 5512 taught by Professor Staff during the Spring '08 term at Minnesota.
 Spring '08
 Staff

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