This question concerns online learning in a non-agnostic setting. Recall that at each time-step, a new example is revealed,and a classification algorithm makes a prediction, and then the actual (true) label (which is 0 or 1 based on an unknownhypothesis c*) is revealed. Let n denote the number of variables (features). In your solution, you can refer to anyobservation/result established in A3 without re-proving it.(a) Prove that there is an online learning algorithm for learning monotone conjunctions that makes at most n mistakesbefore learning an unknown monotone conjunction c*.(b) Prove that any online learning algorithm for learning monotone conjunction makes at least n mistakes before learningan unknown monotone conjunction c*.(c) Prove that any online learning algorithm for learning decision lists makes at least n + 1 mistakes before learning anunknown decision list c*.

Let's break down each part of the problem in detail. (a) Prove that there is an online learning…

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