Methods and systems for selecting an optimal schedule for exploiting value in certain domains

1. A method of developing a personalized and interactive curriculum, the method comprising:

receiving, by a computing device, user data, wherein the user data comprises scheduling data and domain-specific data;

identifying, by the computing device, at least a domain target for at least a domain as a function of the domain-specific data;

generating, by the computing device, a plurality of candidate schedules, each candidate schedule having a plurality of lessons related to a domain corresponding to the domain-specific data, as a function of the at least a domain target and the scheduling data, wherein generating the plurality of candidate schedules comprises:

receiving scheduling training data correlating the domain-specific data to scheduling data;

training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network and training the scheduling machine-learning model comprises:

generating scheduling training data wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules;

retraining the scheduling machine-learning model with an updated scheduling training data; and

generating a plurality of candidate schedules as a function of the domain-specific data, the scheduling machine-learning model, and the neural network;

selecting an optimal user schedule from the plurality of candidate schedules, wherein selecting comprises:

generating a score associated with each candidate schedule of the plurality of candidate schedules using an objective function;

minimizing an expected loss between each candidate schedule of the plurality of candidate schedules and the user schedule as a function of an error function; and

selecting, by the computing device, the optimal user schedule as a function of the score and the minimization of the expected loss; and

presenting, by the computing device at a remote device, the optimal user schedule to a user; and

tracking, by the computing device, a user's progress with regard to the optimal user schedule.

2. The method of claim 1 , wherein generating the at least a domain target comprises: inputting the domain-specific data to a target-setting machine learning model; and

generating the at least a domain target as a function of the domain-specific data and the target-setting machine learning model.

3. The method of claim 2 , comprising training the target-setting machine learning model, wherein training the target-setting machine learning model comprises:

inputting target-setting training data to a machine learning algorithm, wherein the target-setting training data comprises a plurality of domain-specific data correlated to a domain target; and

training the target-setting machine learning model as a function of the machine-learning algorithm.

4. The method of claim 1 comprising:

interrogating, by the computing device, the remote device for update data associated with the user;

evaluating, by the computing device, the update data as a function of the optimal user schedule; and

displaying, by the computing device at the remote device, evaluation results to the user.

5. The method of claim 4 , wherein the update data includes one or more of objective update data and subjective update data.

6. The method of claim 4 , wherein evaluating the update data comprises:

inputting the update data and the user schedule to an evaluating machine learning model; and

generating the evaluation results as a function of the evaluating machine learning model.

7. The method of claim 6 comprising:

training the evaluating machine learning model, wherein training the evaluating machine learning model comprises:

inputting evaluating training data to a machine learning algorithm, wherein the evaluating training data comprises a plurality of update data correlated to evaluations; and

training the evaluating machine learning model as a function of the machine-learning algorithm.

8. The method of claim 1 , wherein tracking the user's progress comprises:

sending, by the computing device, one or more push notifications indicating that one or more lessons of the plurality of lessons are complete after a predetermined amount of time has passed.

9. The method of claim 1 , wherein tracking the user's progress comprises:

sending, by the computing device, one or more periodic scans that interrogate the remote device for completion data, wherein the completion data is a function of the user completing one or more lessons of the plurality of lessons related to the domain.

10. The method of claim 1 , wherein tracking the user's progress comprises:

presenting, by the computing device at the remote device, a graphic showing a user's completion percentage of the plurality of lessons, wherein the graphic is a chart.

11. The method of claim 1 , wherein tracking the user's progress comprises:

accessing, by the computing device, location services of the remote device to track a location of the user; and

comparing user location data to lesson location data for a predetermined lesson time to determine whether the user completed an in-person lesson.

12. The method of claim 1 , wherein the objective function comprises a greedy algorithm.

13. A system for developing a personalized and interactive curriculum comprising a computing device configured to:

receive user data, wherein the user data comprises scheduling data, and domain-specific data,

identify, by the computing device, at least a domain target for at least a domain as a function of the domain-specific data;

generate, by the computing device, a plurality of candidate schedules, each candidate schedule having a plurality of lessons related to a domain corresponding to the domain-specific data, as a function of the at least a domain target and the scheduling data, wherein generating the plurality of candidate schedules comprises:

receiving scheduling training data correlating the domain-specific data to scheduling data;

training a scheduling machine-learning model as a function of the scheduling training data, wherein the scheduling machine-learning model includes a neural network and training the scheduling machine-learning model comprises:

generating scheduling training data wherein the scheduling training data further comprises at least a historical domain target input and outputs at least a plurality of candidate schedules, wherein outputting the at least a plurality of candidate schedules further comprises applying weighted values to the at least a historical domain target input and correlating the weighted values of the at least a historical datum target input to adjacent layers of at least a plurality of candidate schedules;

retraining the scheduling machine-learning model with an updated scheduling training data; and

generating a plurality of candidate schedules as a function of the domain-specific data, the scheduling machine-learning model, and the neural network;

select an optimal user schedule from the plurality of candidate schedules, wherein selecting comprises:

generating a score associated with each candidate schedule of the plurality of candidate schedules using an objective function;

minimizing an expected loss between each candidate schedule of the plurality of candidate schedules and the user schedule as a function of an error function; and

selecting, by the computing device, the optimal user schedule as a function of the score and the minimization of the expected loss; and

present, by the computing device at a remote device, the optimal user schedule to a user; and

track, by the computing device, a user's progress with regard to the optimal user schedule.

14. The system of claim 13 , wherein tracking the user's progress with the optimal user schedule comprises:

sending, by the computing device, one or more push notifications indicating that one or more lessons of the plurality of lessons in complete after a predetermined amount of time has passed.

15. The system of claim 13 , wherein tracking the user's progress comprises:

presenting, by the computing device at the remote device, a graphic showing a user's completion percentage of the plurality of lessons, wherein the graphic is a chart.

16. The system of claim 13 , wherein the computing device is configured to:

interrogate the remote device for update data associated with the user;

evaluate the update data as a function of the optimal user schedule; and

displaying, by the computing device at a remote device, evaluation results to the user.

17. The system of claim 16 , wherein the update data includes objective update data and subjective update data.

18. The system of claim 17 , wherein the computing device is configured to:

input the update data and the user schedule to an evaluating machine learning model; and

generate the evaluation results as a function of the evaluating machine learning model.

19. The system of claim 18 , wherein the computing device is configured to:

train the evaluating machine learning model, wherein training the evaluating machine learning model comprises:

input evaluating training data to a machine learning algorithm, wherein the evaluating training data comprises a plurality of update data correlated to evaluations; and

train the evaluating machine learning model as a function of the machine-learning algorithm.

20. The system of claim 13 , wherein the computing device receives the user data from the remote device.