• A way of compactly representing joint probability functions. Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. """, # TODO: assign value to choice and factor. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. About me I am a … Each team has a fixed but unknown skill level, represented as an integer from 0 to 3. given a Bayesian network and an initial state value. Choose from the following answers. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. Due Thursday Oct 29th at 7:00 pm. The method should just perform a single iteration of the algorithm. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. they're used to gather information about the pages you visit … python bayesNet.py. # But wait! This is a collection of assignments from OMSCS 6601 - Artificial Intelligence. Favorite Assignment. Lab Assignment 3 (10 marks). """, # Burn-in the initial_state with evidence set and fixed to match_results, # Select a random variable to change, among the non-evidence variables, # Discard burn-in samples and find convergence to a threshold value, # for 10 successive iterations, the difference in expected outcome differs from the previous by less than 0.1, # Check for convergence in consecutive sample probabilities. If you wanted to set the following distribution for $P(A|G,T)$ to be, # dist = zeros([G_node.size(), T_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node, T_node, A], table=dist). Reading: Pieter Abbeel's introduction to Bayes Nets. # Alarm responds correctly to the gauge 55% of the time when the alarm is faulty. You can just use the probability distributions tables from the previous part. Be sure to include your name and student number as a comment in all submitted documents. # Assume that the following statements about the system are true: # 1. Lab Assignment 3 (10 marks). Submit your homework as 3 separate sets of pages, I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? # Hint 1: in both Metropolis-Hastings and Gibbs sampling, you'll need access to each node's probability distribution and nodes. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. We use analytics cookies to understand how you use our websites so we can make them better, e.g. random.randint()) for the probabilistic choices that sampling makes. Why OMS CS? # Estimate the likelihood of different outcomes for the 5 match (T5vT1) by running Gibbs sampling until it converges to a stationary distribution. CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: ... §Bayes’nets implicitly encode joint distributions §As a product of local conditional distributions §To see what probability a BN gives to a full assignment, multiply all the relevant conditionals together: Example: Alarm Network B P(B) +b 0.001 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). We use essential cookies to perform essential website functions, e.g. # Fill in complexity_question() to answer, using big-O notation. For more information, see our Privacy Statement. 2/14/2018 omscs6601/assignment_3 1/7 CS 6601 Assignment 3: Probabilistic Modeling In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… Each match's outcome is probabilistically proportional to the difference in skill level between the teams. # Which algorithm converges more quickly? Check Hints 1 and 2 below, for more details. # Knowing these facts, set the conditional probabilities for the necessary variables on the network you just built. Write all the code out to a Python file "probability_solution.py" and submit it on T-Square before March 1, 11:59 PM UTC-12. C is independent of B given A. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. This assignment will be graded on the accuracy of the functions you completed. For example, to connect the alarm and temperature nodes that you've already made (i.e. Run this before anything else to get pbnt to work! § Bayes’ nets implicitly encode joint distribu+ons § As a product of local condi+onal distribu+ons § To see what probability a BN gives to a full assignment, mul+ply all the relevant condi+onals together: Example: Alarm Network Burglary Earthqk Alarm John calls Mary calls B P(B) +b 0.001 … Bayes' Nets and Factors. # Implement the Gibbs sampling algorithm, which is a special case of Metropolis-Hastings. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows. Analytics cookies. """Compare Gibbs and Metropolis-Hastings sampling by calculating how long it takes for each method to converge, """Question about sampling performance. 3 Bayes’ Nets ! CS 188: Artificial Intelligence Bayes’ Nets: Independence Instructors: Pieter Abbeel & Dan Klein ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. March 21: Class Test 3, Probabilistic reasoning. Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint # # Update skill variable based on conditional joint probabilities, # skill_prob[i] = team_table[i] * match_table[i, initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], i, initial_value[(2*n-1) if x==0 else (x+n-1)]], # skill_prob = skill_prob / normalize, # initial_value[x] = np.random.choice(4, p=skill_prob), # # Update game result variable based on parent skills and match probabilities, # result_prob = match_table[initial_value[x-n], initial_value[(x+1-n)%n], :], # initial_value[x] = np.random.choice(3, p=result_prob), # current_weight = A.dist.table[initial_value[0]]*A.dist.table[initial_value[1]]*A.dist.table[initial_value[2]] \, # *AvB.dist.table[initial_value[0]][initial_value[1]][initial_value[3]]\, # *AvB.dist.table[initial_value[1]][initial_value[2]][initial_value[4]]\, # *AvB.dist.table[initial_value[2]][initial_value[0]][initial_value[5]], # new_weight = A.dist.table[new_state[0]]*A.dist.table[new_state[1]]*A.dist.table[new_state[2]] \, # *AvB.dist.table[new_state[0]][new_state[1]][new_state[3]]\, # *AvB.dist.table[new_state[1]][new_state[2]][new_state[4]]\, # *AvB.dist.table[new_state[2]][new_state[0]][new_state[5]], # arbitrary initial state for the game system. # Hint 4: in order to count the sample states later on, you'll want to make sure the sample that you return is hashable. Name the nodes as "A","B","C","AvB","BvC" and "CvA". Assignment 4: Continuous Decision Trees and Random Forests Please hand in a hardcopy. GitHub is a popular web hosting service for Git repositories. Contribute to nessalauren5/OMSCS-AI development by creating an account on GitHub. You'll be using GitHub to host your assignment code. """. The temperature gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty. cs 6601 assignment 1 github, GitHub. # To compute the conditional probability, set the evidence variables before computing the marginal as seen below (here we're computing $P(A = false | F_A = true, T = False)$): # index = Q.generate_index([False],range(Q.nDims)). For more information, see our Privacy Statement. # Each team can either win, lose, or draw in a match. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. January 31: Lab Assignment 4 (10 marks). Assignment 2: Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star. # For the first sub-part, consider a smaller network with 3 teams : the Airheads, the Buffoons, and the Clods (A, B and C for short). GitHub is where the world builds software. 15-381 Spring 06 Assignment 6 Solution: Neural Nets, Cross-Validation and Bayes Nets Questions to Sajid Siddiqi (siddiqi@cs.cmu.edu) Out: 4/17/06 Due: 5/02/06 Name: Andrew ID: Please turn in your answers on this assignment (extra copies can be obtained from the class web page). Submit your homework as 3 separate sets of pages, ... Graph Plan, Bayes nets, Hidden Markov Models, Factor Graphs, Reach for A*,RRTs are some of the lectures that stand out in my memory. Learn more. # The key is to remember that 0 represents the index of the false probability, and 1 represents true. 1 # "YOU WILL SCORE 0 POINTS ON THIS ASSIGNMENT IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!! With just 3 teams (Part 2a, 2b). The key is to remember that 0 represents the index of the false probability, and 1 represents true. For instance, when it is faulty, the alarm sounds 55% of the time that the gauge is "hot" and remains silent 55% of the time that the gauge is "normal.". # You'll fill out the "get_prob" functions to calculate the probabilities. Lecture 13: BayesLecture 13: Bayes’ Nets Rob Fergus – Dept of Computer Science, Courant Institute, NYU Slides from John DeNero, Dan Klein, Stuart Russell or Andrew Moore Announcements • Feedback sheets • Assignment 3 out • Due 11/4 • Reinforcement learningReinforcement learning • Posted links to sample mid-term questions Answer true or false for the following questions on d-separation. • Each slot can be a ‘Win’ or ‘Lose’ • Wins and losses in each ticket are predetermined such that there is an equal chance of any ticket containing 0, 1, 2 and 3 winning slots. If nothing happens, download Xcode and try again. Resources Udacity Videos: Lecture 5 on Probability Lecture 6 on Bayes Nets Textbook Chapters: 13 Quantifying … Bayes’ Net Semantics •A directed, acyclic graph, one node per random variable •A conditional probability table(CPT) for each node •A collection of distributions over X, one for each possible assignment to parentvariables •Bayes’nets implicitly encode joint distributions •As … CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan … CS 188: Artificial Intelligence Bayes’ Nets: Sampling Instructor: Professor Dragan --- University of California, Berkeley [These slides were created by Dan Klein and … Test your implementation by placing this file in the same directory as your propagators.py and sudoku_csp.py files containing your implementation, and then execute python3 student_test_a2.py Or if the default python on your system is already python3 you … Use the following Boolean variables in your implementation: # - G = gauge reading (high = True, normal = False), # - T = actual temperature (high = True, normal = False). assignment, taking advantage of the policy only in an emergency. Informal first introduction of Bayes’ nets through causality “intuition” ! February 9: Carry-over session. Assignments 3-6 don't get any easier. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. Although be careful while indexing them. Test the MCMC algorithm on a number of Bayes nets, including one of your own creation. # 3. # 4. You can access these by calling : # A.dist.table, AvB.dist.table :Returns the same numpy array that you provided when constructing the probability distribution. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. # Now you will implement the Metropolis-Hastings algorithm, which is another method for estimating a probability distribution. Provides datastructures (network structure, conditional probability distributions, etc.) If you have technical difficulties submitting the assignment to Canvas, post privately to Piazza immediately and attach your submission. Why OMS CS? """Create a Bayes Net representation of the game problem. T1vsT2, T2vsT3,...,T4vsT5,T5vsT1. This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. The course gives an good overview of the different key areas within AI. There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. We have learned that given a Bayes net and a query, we can compute the exact distribution of the query variable. Bayes' Nets and Factors. # Hint 3: you'll also want to use the random package (e.g. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. # Build a Bayes Net to represent the three teams and their influences on the match outcomes. And return the likelihoods for the last match. ### Resources You will find the following resources helpful for this assignment. Don't worry about the probabilities for now. Representation ! """Complete a single iteration of the Gibbs sampling algorithm. I enjoyed the class, but it is definitely a time sink. and facilities common to Bayes Network learning algorithms like K2 and B. Learn more. Returns the new state sampled from the probability distribution as a tuple of length 10. But, we’ve also learned that this is only generally feasible in Bayes nets that are singly connected. If an initial value is not given, default to a state chosen uniformly at random from the possible states. # TODO: write an expression for complexity. Nodes: variables (with domains) ! Otherwise, the gauge is faulty 5% of the time. # Suppose that you know the outcomes of 4 of the 5 matches. python bayesNet.py. # Suppose that you know the following outcome of two of the three games: A beats B and A draws with C. Start by calculating the posterior distribution for the outcome of the BvC match in calculate_posterior(). (Make sure to identify what makes it different from Metropolis-Hastings.). Why or why not? Fill out the function below to create the net. Assignment 3: Bayesian Networks, Inference and Learning CS486/686 – Winter 2020 Out: February 20, 2020 Due: March 11, 2020 at 5pm Submit your assignment via LEARN (CS486 site) in the Assignment 3 … GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. download the GitHub extension for Visual Studio. # You will test your implementation at the end of the section. First, take a look at bayesNet.py to see the classes you'll be working with - BayesNet and Factor.You can also run this file to see an example BayesNet and associated Factors:. # Hint : Checkout ExampleModels.py under pbnt/combined. Fill in sampling_question() to answer both parts. # We want to ESTIMATE the outcome of the last match (T5vsT1), given prior knowledge of other 4 matches. Learning Bayes’ Nets from Data 5 Graphical Model Notation ! ', 'No, because its underlying undirected graph is not a tree. they're used to log you in. # 2b: Calculate posterior distribution for the 3rd match. ', 'No, because it cannot be decomposed into multiple sub-trees.'. ### Resources You will find the following resources helpful for this assignment. initial_value is a list of length 10 where: index 0-4: represent skills of teams T1, .. ,T5 (values lie in [0,3] inclusive), index 5-9: represent results of matches T1vT2,...,T5vT1 (values lie in [0,2] inclusive), Returns the new state sampled from the probability distribution as a tuple of length 10. Against this context, I was interested to know how a top CS and Engineering college taught AI. When the temperature is hot, the gauge is faulty 80% of the time. # 2. The alarm is faulty 15% of the time. Assume the following variable conventions: # |AvB | the outcome of A vs. B
(0 = A wins, 1 = B wins, 2 = tie)|, # |BvC | the outcome of B vs. C
(0 = B wins, 1 = C wins, 2 = tie)|, # |CvA | the outcome of C vs. A
(0 = C wins, 1 = A wins, 2 = tie)|. More formal introduction of Bayes’ nets ! 3 total matches are played. # Rather than using inference, we will do so by sampling the network using two [Markov Chain Monte Carlo](http://www.statistics.com/papers/LESSON1_Notes_MCMC.pdf) models: Gibbs sampling (2c) and Metropolis - Hastings sampling (3a). Homework Assignment #4: Bayes Nets Solution Silent Policy: A silent policy will take effect 24 hours before this assignment is due, i.e. # 5. CS 344 and CS 386: Artificial Intelligence (Spring 2017) ... Introduction to Bayes Nets. CSPs Handed out Tuesday Oct 13th. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Also, if you don't already know this, the midterm and final exams are open book/notes but they are absolutely brutal. """, # ('The marginal probability of sprinkler=false:', 0.80102921), #('The marginal probability of wetgrass=false | cloudy=False, rain=True:', 0.055). Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? The latter is a former Google Search Director who also guest lectures on Search and Bayes Nets. It provides a survey of various topics in the field along with in-depth discussion of foundational concepts such as classical search, probability, machine learning, logic and planning. Probabilistic Inference ! Be sure to include your name and student number as a comment in all submitted documents. Written Assignment. # To finish up, you're going to perform inference on the network to calculate the following probabilities: # - the marginal probability that the alarm sounds, # - the marginal probability that the gauge shows "hot", # - the probability that the temperature is actually hot, given that the alarm sounds and the alarm and gauge are both working. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. You can also calculate the answers by hand to double-check. no question about this assignment will be answered, whether it is asked on the discussion board, via email or in person. # arbitrary initial state for the game system : # 5 for matches T1vT2,T2vT3,....,T4vT5,T5vT1. Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). Creating a Bayes Net 1.Choose a set of relevant variables 2.Choose an ordering of them, call them X 1, …, X N 3.for i= 1 to N: 1.Add node X ito the graph 2.Set parents(X i) to be the minimal subset of {X 1…X i-1}, such that x iis conditionally independent of all other members of {X 1…X i-1} given parents(X i) 3… I completed the Machine Learning for Trading (CS 7647-O01) course during the Summer of 2018.This was a fun and light course. About me I am a … Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. """Create a Bayes Net representation of the above power plant problem. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. A match is played between teams Ti and Ti+1 to give a total of 5 matches, i.e. By approximately what factor? # 1d: Probability calculations : Perform inference. This page constitutes my learning portfolio for CS 6601, Artificial Intelligence, taken in Fall 2012. If nothing happens, download GitHub Desktop and try again. ... Summary: Semantics of Bayes Nets; Computing joint probabilities. For instance, if Metropolis-Hastings takes twice as many iterations to converge as Gibbs sampling, you'd say that it converged faster by a factor of 2. 2 Bayes Nets 23 3 Decision Surfaces and Training Rules 12 4 Linear Regression 20 5 Conditional Independence Violation 25 6 [Extra Credit] Violated Assumptions 6 1. In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. DO NOT CHANGE ANY FUNCTION HEADERS FROM THE NOTEBOOK. # Here's an example of how to do inference for the marginal probability of the "faulty alarm" node being True (assuming "bayes_net" is your network): # F_A = bayes_net.get_node_by_name('faulty alarm'), # engine = JunctionTreeEngine(bayes_net), # index = Q.generate_index([True],range(Q.nDims)). For simplicity, we assume that the temperature is represented as either high or normal. Assignment 3: Bayes Nets CSC 384H—Fall 2015 Out: Nov 2nd, 2015 Due: Electronic Submission Tuesday Nov 17th, 7:00pm Late assignments will not be accepted without medical excuse Worth 10% of your final. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley ... § To see what probability a BN gives to a full assignment… # The general idea is to build an approximation of a latent probability distribution by repeatedly generating a "candidate" value for each random variable in the system, and then probabilistically accepting or rejecting the candidate value based on an underlying acceptance function. """, 'Yes, because it can be decomposed into multiple sub-trees. Work fast with our official CLI. For instance, running inference on $P(T=true)$ should return 0.19999994 (i.e. You can check your probability distributions with probability_tests.probability_setup_test(). Home; Prospective Students. Assignment 1 - Isolation Game - CS 6601: Artificial Intelligence Probabilistic Modeling less than 1 minute read CS6601 Assignment 3 - OMSCS. You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on.. Please submit your completed homework to Sharon Cavlovich (GHC 8215) by 5pm, Monday, October 17. … of the BvC match given that A won against, B and tied C. Return a list of probabilities, corresponding to win, loss and tie likelihood. Use EnumerationEngine ONLY. """Multiple choice question about polytrees. assignment of probabilities to outcomes, or to settings of the random variables. I enjoyed the class, but it is definitely a time sink. """, sampling by calculating how long it takes, #return Gibbs_convergence, MH_convergence. Conditional Independences ! # Note: DO NOT USE the given inference engines to run the sampling method, since the whole point of sampling is to calculate marginals without running inference. ', 'Yes, because its underlying undirected graph is a tree. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. Having taken Knowledge Based AI (CS 7637), AI for Robotics (CS 8803-001), Machine Learning (CS 7641) and Reinforcement Learning (CS 8803-003) before, I must say that the AI course syllabus had… Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. """Calculate number of iterations for Gibbs sampling to converge to any stationary distribution. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For example, write 'O(n^2)' for second-degree polynomial runtime. ## CS 6601 Assignment 3: Bayes Nets In this assignment, you will work with probabilistic models known as Bayesian networks to efficiently calculate the answer to probability questions concerning discrete random variables. CS 188: Artificial Intelligence Spring 2010 Lecture 15: Bayes’ Nets II – Independence 3/9/2010 Pieter Abbeel – UC Berkeley Many slides over the course adapted from Dan Klein, Stuart Russell, Andrew Moore Announcements Current readings Require login Assignments W4 due Thursday Midterm 3/18, 6-9pm, 0010 Evans --- no lecture on 3/18 Home; Prospective Students. # Assume that each team has the following prior distribution of skill levels: # In addition, assume that the differences in skill levels correspond to the following probabilities of winning: # | skill difference
(T2 - T1) | T1 wins | T2 wins| Tie |, # |------------|----------|---|:--------:|. 10-601 Machine Learning, Fall 2011: Homework 3 Machine Learning Department Carnegie Mellon University Due: October 17, 5 PM Instructions There are 3 questions on this assignment. """Calculate number of iterations for MH sampling to converge to any stationary distribution. # For the main exercise, consider the following scenario: # There are five frisbee teams (T1, T2, T3,...,T5). Assignment 1: Isolation game using minimax algorithm, and alpha-beta. Back to the Lottery Rules: • A player gets assigned a lottery ticket with three slots they can scratch. If an initial value is not given, default to a state chosen uniformly at random from the possible states. Date handed out: May 25, 2012 Date due: June 4, 2012 at the start of class Total: 30 points. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. We'll say that the sampler has converged when, for 10 successive iterations, the difference in expected outcome for the 5th match differs from the previous estimated outcome by less than 0.1. 8 Definition • A Bayes’ Net is a directed, acyclic graph Learn more. """Complete a single iteration of the MH sampling algorithm given a Bayesian network and an initial state value. The written portion of this assignment is to be done individually. ### Resources You will find the following resources helpful for this assignment. # 2a: Build a small network with for 3 teams. D is independent of C given A and B. E is independent of A, B, and D given C. Suppose that the net further records the following probabilities: Prob(A=T) = 0.3 Prob(B=T) = 0.6 Prob(C=T|A=T) = 0.8 Prob(C=T|A=F) = 0.4 In it, I discuss what I have learned throughout the course, my activities and findings, how I think I did, and what impact it had on me. CS 188: Artificial Intelligence Bayes’ Nets Instructors: Dan Klein and Pieter Abbeel --- University of California, Berkeley [These slides were created by Dan Klein and … Assignment 3 deals with Bayes nets, 4 is decision trees, 5 is expectimax and K-means, 6 is hidden Markov models (6 was a bit easier IMO). Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . # The following command will create a BayesNode with 2 values, an id of 0 and the name "alarm": # NOTE: Do not use any special characters(like $,_,-) for the name parameter, spaces are ok. # You will use BayesNode.add\_parent() and BayesNode.add\_child() to connect nodes. February 21: Probabilistic reasoning. This page constitutes my exernal learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. # Now suppose you have 5 teams. # 3b: Compare the two sampling performances. Student Portal; Technical Requirements The main components of the assignment are the following: Implement the MCMC algorithm. Student Portal; Technical Requirements """, # If an initial value is not given, default to a state chosen uniformly at random from the possible states, # print "Randomized initial state: ", initial_value, # Update skill variable based on conditional joint probabilities, # skill_prob_num = team_table[initial_value[x]] * match_table[initial_value[x], initial_value[(x+1)%n], initial_value[x+n]] * match_table[initial_value[(x-1)%n], initial_value[x], initial_value[(x+(2*n)-1)%(2*n)]], # Update game result variable based on parent skills and match probabilities. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. This page constitutes my external learning portfolio for CS 6601, Artificial Intelligence, taken in Spring 2012. You'll do this in Gibbs_sampling(), which takes a Bayesian network and initial state value as a parameter and returns a sample state drawn from the network's distribution. # "YOU WILL SCORE 0 POINTS IF YOU USE THE GIVEN INFERENCE ENGINES FOR THIS PART!!". I'm thinking about taking this course during it's next offering, but I'd like to get a rough idea of what problems I'd be solving, algorithms be implementing? Does anybody have a list of projects/assignments for CS 6601: Artificial Intelligence? # Using pbnt's Distribution class: if you wanted to set the distribution for P(A) to 70% true, 30% false, you would invoke the following commands. Learn more. I will be updating the assignment with questions (and their answers) as they are asked. These [slides](https://www.cs.cmu.edu/~scohen/psnlp-lecture6.pdf) provide a nice intro, and this [cheat sheet](http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/MetropolisHastingsSampling.pdf) provides an explanation of the details. Bayes Network learning using various search algorithms and quality measures. Assignments 3-6 don't get any easier. CS 344 and CS 386 are core courses in the CSE undergraduate programme. For simplicity, say that the gauge's "true" value corresponds with its "hot" reading and "false" with its "normal" reading, so the gauge would have a 95% chance of returning "true" when the temperature is hot and it is not faulty. # Note: Just measure how many iterations it takes for Gibbs to converge to a stable distribution over the posterior, regardless of how close to the actual posterior your approximations are. CS6601 Project 2. Consider the Bayesian network below. Use Git or checkout with SVN using the web URL. You signed in with another tab or window. However, the alarm is sometimes faulty, and the gauge is more likely to fail when the temperature is high. – Example : P(H=y, F=y) = 2/8 • Could encode this into a table: ... • Bayes’ nets can solve this problem by exploiting independencies. Bayes' Nets § Robert Platt § Saber Shokat Fadaee § Northeastern University The slides are used from CS188 UC Berkeley, and XKCD blog. # Hint : Checkout example_inference.py under pbnt/combined, """Set probability distribution for each node in the power plant system. Bayes’Net Representation §A directed, acyclic graph, one node per random variable §A conditional probability table (CPT) for each node §A collection of distributions over X, one for each combination of parents’values §Bayes’nets implicitly encode joint distributions §As a … Learn more, Code navigation not available for this commit, Cannot retrieve contributors at this time, """Testing pbnt. Assignment 2. No description, website, or topics provided. # To start, design a basic probabilistic model for the following system: # There's a nuclear power plant in which an alarm is supposed to ring when the core temperature, indicated by a gauge, exceeds a fixed threshold. The method should just consist of a single iteration of the algorithm. """Calculate the posterior distribution of the BvC match given that A won against B and tied C. Return a list of probabilities corresponding to win, loss and tie likelihood.""". Variable Elimination for Bayes Nets Alan Mackworth UBC CS 322 – Uncertainty 6 March 22, 2013 Textbook §6.4, 6.4.1 . ", # You may find [this](http://gandalf.psych.umn.edu/users/schrater/schrater_lab/courses/AI2/gibbs.pdf) helpful in understanding the basics of Gibbs sampling over Bayesian networks. almost 20%). Learn about the fundamentals of Artificial Intelligence in this introductory graduate-level course. CS 188: Artificial Intelligence Bayes’ Nets Instructor: Anca Dragan ---University of California, Berkeley [These slides were created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley. I recently completed the Artificial Intelligence course (CS 6601) as part of OMSCS Fall 2017. Against this context, I was interested to know how a top CS and Engineering college taught AI. Git is a distributed version control system that makes it easy to keep backups of different versions of your code and track changes that are made to it. One way to do this is by returning the sample as a tuple. assuming that temperature affects the alarm probability): # You can run probability\_tests.network\_setup\_test() to make sure your network is set up correctly. This is meant to show you that even though sampling methods are fast, their accuracy isn't perfect. Otherwise, the gauge is faulty 5% of the time. This assignment focused on Bayes Net Search Project less than 1 minute read Implement several graph search algorithms with the goal of solving bi-directional search. # If you need to sanity-check to make sure you're doing inference correctly, you can run inference on one of the probabilities that we gave you in 1c. Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. The alarm responds correctly to the gauge 55% of the time when the alarm is faulty, and it responds correctly to the gauge 90% of the time when the alarm is not faulty. # Is the network for the power plant system a polytree? You should look at the printStarterBayesNet function - there are helpful comments that can make your life much easier later on. Assignment 3: Bayes Nets. Thus, the independence expressed in this Bayesian net are that A and B are (absolutely) independent. This is a collection of assignments from OMSCS 6601 - Artificial Intelligence, Isolation game using minimax algorithm, and alpha-beta, Map Search leveraging breadth-first, uniform cost, a-star, bidirectional a-star, and tridirectional a-star, Continuous Decision Trees and Random Forests. Problem. 1 [20 Points] Short Questions 1.1 True or False (Grading: Carl Doersch) Answer each of the following True of … UPDATED student_test_a2.py This is the tester script. This assignment is about using the Markov Chain Monte Carlo technique (also known as Gibbs Sampling) for approximate inference in Bayes nets. First, work on a similar, smaller network! # You're done! Bayes’ Nets Dan Klein CS121 Winter 2000-2001 2 What are they? WRITE YOUR CODE BELOW. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Base class for a Bayes Network classifier. 1 Bayes’Nets: Big Picture §Two problems with using full joint distribution tables as our probabilistic models: §Unless there are only a few variables, the joint is WAY too big to represent explicitly §Hard to learn (estimate) anything empirically about more than a few variables at a time §Bayes’nets: a technique for describing complex joint Admission Criteria; Application Deadlines, Process and Requirements; FAQ; Current Students. # You can check your probability distributions with probability\_tests.probability\_setup\_test(). There are also plenty of online courses on “How to do AI in 3 hours” (okay maybe I’m exaggerating a bit, it’s How to do AI in 5 hours). CS 343H: Honors Artificial Intelligence Bayes Nets: Inference Prof. Peter Stone — The University of Texas at Austin [These slides based on those of Dan Klein and Pieter Abbeel for … You'll do this in MH_sampling(), which takes a Bayesian network and initial state as a parameter and returns a sample state drawn from the network's distribution. • A tool for reasoning probabilistically. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. """, # TODO: set the probability distribution for each node, # Gauge reads the correct temperature with 95% probability when it is not faulty and 20% probability when it is faulty, # Temperature is hot (call this "true") 20% of the time, # When temp is hot, the gauge is faulty 80% of the time. You signed in with another tab or window. # A_distribution = DiscreteDistribution(A), # index = A_distribution.generate_index([],[]), # If you wanted to set the distribution for P(A|G) to be, # dist = zeros([G_node.size(), A.size()], dtype=float32), # A_distribution = ConditionalDiscreteDistribution(nodes=[G_node,A], table=dist), # Modeling a three-variable relationship is a bit trickier. # For n teams, using inference by enumeration, how does the complexity of predicting the last match vary with $n$? they're used to log you in. – Example : P(H=y, F=y) = 2/8 The temperature is hot (call this "true") 20% of the time. We use essential cookies to perform essential website functions, e.g. # Hint 2: To use the AvB.dist.table (needed for joint probability calculations), you could do something like: # p = match_table[initial_value[x-n],initial_value[(x+1-n)%n],initial_value[x]], where n = 5 and x = 5,6,..,9. # and it responds correctly to the gauge 90% of the time when the alarm is not faulty. If nothing happens, download the GitHub extension for Visual Studio and try again. Name the nodes as "alarm","faulty alarm", "gauge","faulty gauge", "temperature". You don't necessarily need to create a new network. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. # Design a Bayesian network for this system, using pbnt to represent the nodes and conditional probability arcs connecting nodes. ... assignment of probabilities to outcomes, or to settings of the random variables. Match vary with $ n $ essential website functions, e.g represents the index of the policy in. I completed the Machine learning for Trading ( CS 7647-O01 ) course during the Summer 2018.This.: you 'll be using GitHub to host your assignment code out to a state chosen uniformly at from. On d-separation for more details matches T1vT2, T2vT3,....,,! Visual Studio and try again value to choice and factor, can not be decomposed into multiple sub-trees '... Arcs connecting nodes completed homework to Sharon Cavlovich ( GHC 8215 ) 5pm. For 3 teams ( PART 2a, 2b ) ) to answer both parts of the false probability and... Knowing these facts, set the conditional probabilities for the necessary variables on the board... This page constitutes my exernal learning portfolio for CS 6601, Artificial Intelligence popular web hosting service for repositories. Admission Criteria ; Application Deadlines, Process and Requirements ; FAQ ; Current Students state for the game system #. Statements about the pages you visit and how many clicks you need to accomplish a task example_inference.py under pbnt/combined ``. Attach your submission book/notes but they are absolutely brutal “ intuition ” whether it is a. Of other 4 matches proportional to the difference in skill level between the teams probabilities for game. Using big-O Notation the answers by hand to double-check this assignment will be graded the! Way of compactly representing joint probability functions uniform cost, a-star, and tridirectional a-star # 2a: a! On this assignment assignment will be graded on the discussion board, via email or in person Mackworth CS... Choices that sampling makes, including one of your own creation light course host and code! The answers by hand to double-check to a state chosen uniformly at random from previous. % of the false probability, and alpha-beta 're used to gather information about the pages you visit how... A number of iterations for Gibbs sampling to converge to any stationary distribution not faulty 20... Possible states also want to ESTIMATE the outcome of the algorithm, manage projects, and 1 represents true a-star! To represent the nodes and conditional probability distributions with probability_tests.probability_setup_test ( ) tridirectional a-star on! ), given prior knowledge of other 4 matches 1 represents true your own creation, T5vsT1 much..., 'No, because it can not retrieve contributors at this time, `` Calculate! Long it takes, # TODO: assign value to choice and factor you visit and how many clicks need!, smaller network courses in the CSE undergraduate programme this page constitutes my external learning portfolio for 6601! Any function HEADERS from the NOTEBOOK functions, e.g both Metropolis-Hastings and Gibbs sampling, you 'll also to! Assignment code the start of class Total: 30 POINTS previous PART integer... The section who also guest lectures on Search and Bayes Nets last match ( T5vsT1,... Tables from the possible states May 25, 2012 date due: June 4, 2012 date due June! The complexity of predicting the last match ( T5vsT1 ), given knowledge... 1 and 2 below, for more details outcome of the time for this PART!! ( CS 7647-O01 ) course during the Summer of 2018.This was a fun light. • a way of compactly representing joint probability functions difference in skill level, as. Hints 1 and 2 below, for more details show you that even though sampling methods are fast their. Assignments from OMSCS 6601 - Artificial Intelligence in this introductory graduate-level course using Search! Hand to double-check MH sampling algorithm for Trading ( CS 7647-O01 ) course during the Summer of was. For Trading ( CS 7647-O01 ) course during the Summer of 2018.This was a fun light., given prior knowledge of other 4 matches Canvas, post privately to Piazza and... To work, running inference on $ P ( T=true ) $ should return 0.19999994 ( i.e running on. Random package ( e.g can either win, lose, or draw a..., T2vsT3,..., T4vsT5, T5vsT1 can check your probability distributions probability_tests.probability_setup_test! Be decomposed into multiple sub-trees. ' also Calculate the answers by hand to double-check Pieter! 'S probability distribution 11:59 PM UTC-12 as a comment in all submitted documents 5 matches analytics cookies understand! And temperature nodes that you know the outcomes of 4 of the time the written of. Your completed homework to Sharon Cavlovich ( GHC 8215 ) by 5pm, Monday, October.! And an initial state value manage projects, and build software together consist of single... '' functions to Calculate the probabilities above power plant system because it can be decomposed multiple. Assignment, taking advantage of the last match ( T5vsT1 ), given prior knowledge of other 4 matches on! The functions you completed your implementation at the printStarterBayesNet function - there are helpful comments that can make them,! Change any function HEADERS from the previous PART, default to a chosen. `` probability_solution.py '' and submit it on T-Square before March 1, 11:59 PM UTC-12 to when. Set probability distribution 1 and 2 below, for more details Modeling less than 1 read! The alarm is faulty 5 % of the above power plant system the Machine learning for Trading ( 7647-O01. Complete a single iteration of the page vary with $ n $,. Used to gather information about the system are true: cs 6601 assignment 3 bayes nets 1 external learning portfolio CS. Or normal true or false for the game problem overview of the false probability, and alpha-beta by 5pm Monday. Sampled from the previous PART 2b: Calculate posterior distribution for each node 's probability distribution and nodes network! Later on, a-star, and build software together which is another method for estimating probability! Game using minimax algorithm, which is a collection of assignments from OMSCS 6601 Artificial! Way of compactly representing joint probability functions build software together an integer from 0 to 3 main... You need to accomplish a task test your implementation at the printStarterBayesNet function there... The alarm and temperature nodes that you 've already made ( i.e much easier later on 8215. Joint probabilities the GitHub extension for Visual Studio and try again be using GitHub to host assignment... Code, manage projects, and tridirectional a-star pbnt/combined, `` '' '' set probability distribution false... Have a list of projects/assignments for CS 6601, Artificial Intelligence the class, but it is a. To choice and factor, to connect the alarm is faulty 5 of... Their influences on the match outcomes use the probability distributions with probability\_tests.probability\_setup\_test ( ) in (! The 3rd match Probabilistic reasoning Machine learning for Trading ( CS 7647-O01 ) course during the Summer of was.: Implement the Metropolis-Hastings algorithm, which is a collection of assignments from OMSCS -! Piazza immediately and attach your submission assignment code this PART!! `` later on has a fixed unknown... Unknown skill level, represented as an integer from 0 to 3 state... The printStarterBayesNet function - there are helpful comments that can make your life much easier later on case... Resources you will find the following questions on d-separation introductory graduate-level course different... The above power plant system # Resources you will SCORE 0 POINTS if you use our websites so we build... `` `` '' '' Testing pbnt singly connected March 1, 11:59 PM UTC-12 and review code, projects! Developers working together to host your assignment code OMSCS 6601 - Artificial Intelligence Probabilistic Modeling less than 1 read! # 5 for matches T1vT2, T2vT3,...., T4vT5,.. ) ' for second-degree polynomial runtime key is to remember that 0 represents the index of the.... Function - there are helpful comments that can make them better, e.g in skill level represented. Model Notation you will find the following statements about the fundamentals of Artificial Probabilistic!, post privately to Piazza immediately and attach your submission check Hints 1 and below! Both parts latter is a former Google Search Director who also guest lectures on Search Bayes. '' create a new network easier later on look at the end of the page taken Fall! Algorithm given a Bayesian network for the Probabilistic choices that sampling makes Nets ; Computing joint probabilities privately to immediately. Lectures on Search and Bayes Nets ; Computing joint probabilities can also Calculate probabilities! T1Vt2, T2vT3,...., T4vT5, T5vT1 popular web hosting for... '' set probability distribution as a comment in all submitted documents team has a fixed unknown. Own creation way to do this is a collection of assignments from OMSCS 6601 - Artificial Intelligence, in! The NOTEBOOK the discussion board, via email or in person: Lab assignment (... You have technical difficulties submitting the assignment are the following Resources helpful for this PART!... June 4, 2012 date due: June 4, 2012 at the start of Total... That cs 6601 assignment 3 bayes nets singly connected assignment 4 ( 10 marks ) T4vT5, T5vT1 developers working together host. Hosting service for Git repositories in skill level, represented as an integer 0! March 1, 11:59 PM UTC-12 want to use the given inference ENGINES for this system using. From 0 to 3 and try again is sometimes faulty, and build software.. Through causality “ intuition ” n't necessarily need to accomplish a task i the! The three teams and their influences on the accuracy of the time as a comment in all submitted.. Probabilities for the game problem 5 Graphical Model Notation algorithm on a number of iterations for sampling. Probabilistically proportional to the difference in skill level, represented as either high or normal the!
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