is a clustering procedure where all objects start out in one giant cluster. Clusters are formed by dividing this cluster into smaller and smaller clusters.* (2 Points) Expectation Maximization K-Means clustering agglomerative clustering partition clustering 3 When does k-means clustering stop creating or optimizing clusters? (2 Points) A. After finding no new reassignment of data points B. After the algorithm reaches the defined number of iterations C. Both A and B D. None 4 Which of the following is true about reinforcement learning? (2 points) The agent gets rewards or penalty according to the action It's an online learning The target of an agent is to maximize the rewards All In a feedforward neural network, which of the three nodes is responsible for the calculations and has no interaction with the outside world?. 15 (2 points) Radial node Hidden node Inpot node Output node 7 Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes. (2 Points) Forward from source to sink Backward from sink to hidden nodes Backward from sink to source Forward from source to hidden nodes