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Machine Learning for Control Training Courses

Machine Learning for Control Training Courses: Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task.

Control theory in control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines.

The connection between control theory and machine learning is a significant one. It goes back to the perceptron, the basic building block of the modern machine learning paradigm. This was a hardware structure built in the 50s by American psychologist and artificial intelligence (AI) researcher Frank Rosenblatt to mimic the real neural network in our brains. The perceptron arose out of control theory literature as people were trying to identify highly complex and nonlinear dynamical systems.

Today, machine learning algorithms (neural networks in particular) are used to approximate functions that can’t be hard coded. The approximated functions map one set to another set, from a mathematical point of view.

An example commonly used is the mapping function between inputs to the neural network (cat images) set to the labels: output of the neural network (cat category) set.

Control theory does research for similar type of functions, meaning, i.e., the mapping from one set to another set. But the sets are different between the machine learning and control theory. In control theory the input set is a set of signals as in the series whose Fourier transform can be found. The output is also a set of signals or just one signal.

Major difference between the control theory and machine learning is that in control theory most of these functions can be found analytically. There is a direct way to find the desired function.

In machine learning, normally there are no analytical solutions. This is why machine learning problems are approached as an optimization problem. Approximated function can only be fond after a lot of iteration. In control theory this means the closed loop control system can’t be proven to be stable.

Usually machine learning can be used for control problems if analytical solutions can’t be found for a control system via traditional and modern methods (because neural networks can map from signal set to signal set, if you arrange it that way).

Still, there is enough intersection between these two subjects that there is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning. This subfield is called machine learning control (MLC).

Machine Learning for Control Training Courses

Tonex offers Machine Learning for Control Training, a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI), and the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.

Who Should Attend

Just about anyone whose work interfaces with data analysis, and managers who need the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data.

Why Tonex?

  • Our instructors not only possess very specialized knowledge in their areas of expertise, they also have real world experience.
  • Presenting highly customized learning solutions is what we do. For over 30 years Tonex has worked with organizations in improving their understanding and capabilities in topics often with new development, design, optimization, regulations and compliances that, frankly, can be difficult to comprehend.
  • So far we have helped over 20,000 developers in over 50 countries stay up to date with cutting edge information from our training categories.
  • Ratings tabulated from student feedback post-course evaluations show an amazing 98 percent satisfaction score.

For more information, questions, comments, Contact us.

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