Might sound silly for many of all but is this device's knob for the current for obtaining constant current or limiting the max current? Can I use this Dc power supply to obtain a constant current regardless of the resisyances? If not what would u prefer as an adjustable current source? Here is the device:
http://electronics123.net/amazon/datasheet/ps613u.pdf

When feeding a DC motor with this the motor spins faster when increasing the current knob. Im a bit confused if it is only for limiting purpose or not.


Question: What does mathematics have to do with programming?
Answer: Two specific examples where a math education is important to computer science are:

1) Relational databases where relational calculus is used.


  Relational calculus consists of two calculi, the tuple relational
  calculus and the domain relational calculus, that are part of the
  relational model for databases and provide a declarative way to
  specify database queries. This in contrast to the relational algebra
  which is also part of the relational model but provides a more
  procedural way for specifying queries.
  
  The relational algebra might suggest these steps to retrieve the phone
  numbers and names of book stores that supply Some Sample Book:

Join book stores and titles over the BookstoreID.
Restrict the result of that join to tuples for the book Some Sample Book.
Project the result of that restriction over StoreName and StorePhone.

  
  The relational calculus would formulate a descriptive, declarative
  way:

Get StoreName and StorePhone for supplies such that there exists a title BK with the same BookstoreID value and with a BookTitle value of

  
  Some Sample Book.
  
  The relational algebra and the relational calculus are essentially
  logically equivalent: for any algebraic expression, there is an
  equivalent expression in the calculus, and vice versa. This result is
  known as Codd's theorem.


The next area is artificial intelligence (AI) and machine learning.

For an example on how these are used take a look at udacity's class CS 373: PROGRAMMING A ROBOTIC CAR.

Description: This class, taught by one of the foremost experts in AI, will teach you basic methods in Artificial Intelligence, including: probabilistic inference, computer vision, machine learning, and planning, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. You will get a chance to visit, via video, the leading research labs in the field, and meet the scientists and engineers who are building self-driving cars at Stanford and Google.

Prerequisites: The instructor will assume solid knowledge of programming, all programming will be in Python. Knowledge of probability and linear algebra will be helpful.


  WEEK 1:
  
  Basics of probability Car localization with particle filters
  
  WEEK 2:
  
  Gaussians and continuous probability Tracking other cars with Kalman
  filters
  
  WEEK 3:
  
  Image Processing and Machine Learning Finding objects in sensor data
  
  WEEK 4:
  
  Planning and search Determining where to drive with A* search Finding
  optimal routes with dynamic programming
  
  WEEK 5:
  
  Controls Controlling steering and speeds with PID
  
  WEEK 6:
  
  Putting it all together Programming a self-driving car
  
  WEEK 7:
  
  Final Exam Exam testing your knowledge