6CCS3AIN, Tutorial 05 (Version 1.0) 1. You’re given the data matrix X with n = 4 houses and d = 3 features. Your goal is to run the PCA algorithm from the slides to X in order to reduce the number of dimensions to k = 2. 423 X=6 1 3 4 2 5 […]
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6CCS3AIN Week 10: AI and Ethics: Tutorial Questions and Solutions Q1. In the lectures, we mention that for model-driven (or symbolic) AI systems it is usually straightforward to generate explanations of the reasoning used by the AI system in each particular case where the system is applied. An expert system which uses IF-THEN rules is
Computer Science is the science of delegation Key question: How do we delegate tasks to a machine? Hardware engineering: How to design machines able to effectively & efficiently undertake the tasks we delegate to them? Programming theory: How do we best write instructions for machines to do what we what we want them
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Gibbs sampling A rather different approach to sampling. Part of the Markov Chain Monte Carlo (MCMC) family of algorithms. Don’t generate each sample from scratch. Generate samples by making a random change to the previous sample. ⃝c -Trenn, King’s College London 2 Gibbs sampling Gibbs sampling for Bayesian networks starts with an arbitrary state. So
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What are the hierarchical algorithms actually doing? ⃝c -Trenn, King’s College London 2 What quantity are these algorithms optimizing? For flat clustering, algorithms designed to optimize some objective function ‚ Remember: for flat clustering the goal was to find k points μ1 , . . . , μk that minimize, e.g. 1. k-medianobjective 2. k-meansobjective
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Introduction This is Part C of the lecture for Week 9. The topic for this week is consensus mechanisms, which are an important part of automated decision-making in decentralized and distributed systems. In this lecture, we look at the type of mechanisms used to reach consensus in Blockchain systems, as in the
Bayesian Networks Okay, so what can we do with Bayesian Networks? ⃝c -Trenn, King’s College London 2 Bayesian Networks Okay, so what can we do with Bayesian Networks? They are useful for inference (a conclusion reached on the basis of evidence and reasoning) ⃝c -Trenn, King’s College London 3 Inference tasks Simple queries: compute posterior
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6CCS3AIN Week 10: AI and Ethics: Tutorial Questions Q1. In the lectures, we mention that for model-driven (or symbolic) AI systems it is usually straightforward to generate explanations of the reasoning used by the AI system in each particular case where the system is applied. An expert system which uses IF-THEN rules is a common
6CCS3AIN – Week 9 – Part B 1 / 17 Motivation Motivational Example v2 v4 t=0 v1 v3 v5 • Consider a graph such as the one above. • Nodes in this graph represent agents. • Colour of nodes represent their current opinion. (it may change over time!) • Edges represent what other agents a
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6CCS3AIN, Tutorial 06 Answers (Version 1.0) 3 4 1 1 1 1. Performk-Medianonthefollowinginput: x1 = 4 x2 = 2 x3 = 2 x4 = 1 x5 = 3 . Assume that k=2 and the initial clusters location of the clusters are 1 2 μ1= 1 andμ2= 4 . Recall, that the distance between two points
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