Symbolické strojové učení

• Stránky předmětu: Symbolické strojové učení
• Přednášející: Jiří Kléma, Filip Železný
• Cvičící: Jáchym Barvínek, Ondřej Hubáček, Petr Ryšavý, Martin Svatoš

06.06.2018

1. (5 pnts) Difference between PAC-learning agent and mistake-bound agent. What does it mean when an agent in both frameworks learns? What does it mean when it learns efficiently? Online?
2. (10 pnts) Space-version agent. There are given two agent with different hypotheses spaces. First is all possible 3-conjunctions (non-negative) of n variables. Second is all n-conjunctions of positive and negative literals. For each agent: does it learn online? does it learn efficiently? For the first agent: given the first negative observation (0,1,1,1,…,1), what will be the agent's decision on the next observation (0,1,0,1,…)?
3. (15 pnts) Relative Least General Generalization (rlgg). Given background knowledge B = {half(4,2), half(2,1), int(2), int(1)}. What will be the rlgg of o1 = even(4) and o2 = even(2) relative to the background? Apply algorithm, draw tables, theta functions. Make a reduction step relative to B. Why is it needed?
4. (10 pnts) Bayesian networks. Find optimal, efficient, complete network (something like Season → Temperature → (two children: → Ice Cream Sales, → Heart Attack Rate)). Then compute CPT (conditional probability tables). For two queries compute its probability: 1) Pr(Spring|Good Ice Cream Sales, No Heart Attack) 2) Pr(Heart Attack|Winter, Bad Sales).
5. (5 pnts) Q-learning. Given 5 small questions, response True/False and provide your reasoning.
6. (5 pnts) Q-learning representation. Describe states, actions, rewards.