A machine learning apparatus, which learns a condition associated with a filter unit for filtering an analog input signal, includes a state observer for observing a state variable that includes at least one of a noise component and noise amount of an output signal from the filter unit and a responsivity to the input signal; and a learner for learning the condition associated with the filter unit in accordance with a training data set that includes the state variable.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus for learning a condition associated with a filter unit for filtering an analog input signal, comprising: a state observer for observing a state variable that includes a noise component and noise amount of an output signal from the filter unit and a responsivity to the input signal; and a learner for learning the condition associated with the filter unit in accordance with a training data set that includes the state variable. 2. The machine learning apparatus according to claim 1, wherein the state observer further observes information about a machine state from a machine commander; and the learner further updates the training data set based on the information about the machine state. 3. The machine learning apparatus according to claim 1, wherein the learner learns the condition in accordance with training data sets acquired on a plurality of filter units. 4. The machine learning apparatus according to claim 1, further comprising a decision maker for updating a filter based on a result of learning by the learner in accordance with the training data set. 5. The machine learning apparatus according to claim 1, further comprising a reward calculator for calculating a reward based on the noise component, the noise amount, and the responsivity; and a function updater for updating a function to modify the filter unit from the current state variable based on the reward. 6. The machine learning apparatus according to claim 5, wherein the reward calculator decreases the reward when the noise amount is increased or the responsivity is lower than a specified value, while the reward calculator increases the reward when the noise amount is decreased and the responsivity is equal to or higher than the specified value. 7. The machine learning apparatus according to claim 5, wherein the learner relearns and updates the condition in accordance with an additional training data set that includes the current state variable. 8. The machine learning apparatus according to claim 7, wherein the machine learning apparatus is connected to the filter unit through a network; and the state observer acquires the current state variable through the network. 9. A motor drive apparatus comprising: the machine learning apparatus according to claim 1; and a filter unit having a variable filter and a filter modifier for modifying the variable filter. 10. The motor drive apparatus according to claim 9, wherein the machine learning apparatus is present in a cloud server. 11. A motor drive system comprising: the motor drive apparatus according to claim 9; and a machine commander for providing notification of information about the operation of a machine. 12. A machine learning method for learning a condition associated with a filter unit for filtering an analog input signal, comprising the steps of: observing a state variable that includes a noise component and noise amount of an output signal from the filter unit and a responsivity to the input signal; and learning the condition associated with the filter unit in accordance with a training data set that includes the state variable.