Which task identifies the category of an object in an image?

Table: Gridworld MDP Table: Gridworld MDP   Figure: Transition Function Figure: Transition Function   Review Table: Gridworld MDP and Figure: Transition Function. The gridworld MDP operates like the one discussed in lecture. The states are grid squares, identified by their column (A, B, or C) and row (1 or 2) values, as presented in the table. The agent always starts in state (A,1), marked with the letter S. There are two terminal goal states: (C,2) with reward +1, and (A,2) with reward -1. Rewards are 0 in non-terminal states. (The reward for a state is received before the agent applies the next action.) The transition function in Figure: Transition Function is such that the intended agent movement (Up, Down, Left, or Right) happens with probability 0.8. The probability that the agent ends up in one of the states perpendicular to the intended direction is 0.1 each. If a collision with a wall happens, the agent stays in the same state, and the drift probability is added to the probability of remaining in the same state. The discounting factor is 1. The agent starts with the policy that always chooses to go Up, and it executes three trials: the first trial is (A,1)–(A,2), the second is (A,1)–(A,2), and the third is (A,1)–(B,1)–(C,1)–(C,2). Given these traces, what is the Monte Carlo (direct utility) estimate for state (A,1)?

Table: Gridworld MDP Table: Gridworld MDP   Figure: Transition Function Figure: Transition Function   Review Table: Gridworld MDP and Figure: Transition Function. The gridworld MDP operates like the one discussed in lecture. The states are grid squares, identified by their column (A, B, or C) and row (1 or 2) values, as presented in the table. The agent always starts in state (A,1), marked with the letter S. There are two terminal goal states: (B,1) with reward -5, and (B,2) with reward +5. Rewards are -0.1 in non-terminal states. (The reward for a state is received before the agent applies the next action.) The transition function in Figure: Transition Function is such that the intended agent movement (Up, Down, Left, or Right) happens with probability 0.8. The probability that the agent ends up in one of the states perpendicular to the intended direction is 0.1 each. If a collision with a wall happens, the agent stays in the same state, and the drift probability is added to the probability of remaining in the same state. The discounting factor is 1. Given this information, what will be the optimal policy for state (C,1)?

Which equation describes the job of a camera-based robotic perception model?

Motion estimation is only possible with specific pre-assumptions. Which situation satisfies the requirement for a camera to estimate motion?

Suppose that you are given a set of noisy data points of the height and speed of soccer players. What should you use to approximate the joint distribution over these variables?

Suppose that you are trying to fit a neural network into data that were sampled from a sine-curve function. Your network has only one input (phase). Which neural network is best suited for this?

Which statement describes a characteristic of the Convolutional Neural Network (CNN) in recognition correctly?

Suppose you are training a neural network. One of the neurons has an input vector , weights . What is the input to this neuron? Give your answer to two decimal places.

Suppose that you are training a neural network, and the input data are the age and annual income of every person in the United States. What is the first thing you should do to improve training accuracy?