Machine Learning and Cognitive Science

Machine Learning and Cognitive Science

Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.
A prime example of the application of machine learning is the autonomous vehicle. Sensors around the vehicle deliver thousands of data points which are analyzed and processed to move the vehicle toward its destination. Collective data from thousands of self-driving cars can be used to improve vehicle safety and prevent accidents.
Another simple example would be: An AI based system that can identify people, their gender, their various activities from the video. This is real time video processing application.
In other words, we can say that machine learns based on experience which is also known as training. The system generalizes based on the number of cases that it is exposed to and then later performs after unanticipated events.
The branch of machine learning also includes other data analysis areas which ranges from predictive analysis to data mining to pattern recognition. There are variety of algorithms that can be used depending on the type of input required.
One of the noticeable applications of machine learning is the automation of acquisition of various knowledge bases used by expert systems which aim to echo the process of decision making of human expertise in a field.
The major approach in machine learning scenarios include using case-based learning, rule indication, genetic algorithms, analytic learning and neural networks. In recent times, these models are used in a hybrid approach thus enabling the effective model development. The combination of these analytic method can ensure effective and reliable results which is required in the industry and business solutions.
Machine learning is based on the simple principle, which is

Learning=Representation+Evaluation+Optimization

  • Representation means a classifier element should be represented in a language which computer can interpret
  • Evaluation means a function which differentiates between good and bad classifiers
  • Optimization means a method which is used to search the highest scoring one classifier among these

The process of Machine learning comprises of good blend of methodologies, algorithms and mathematical techniques. Machine learning can be done by following two strategies:

1) Supervised strategy: Maps data inputs to model them against desired outputs
2) Unsupervised strategy: Maps data to model them to find new trends

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There are numerous applications, where machine learning can be used, having large volume of very different data.
Due to the enormous success in various areas like robot control, computer vision, speech recognition etc., the machine learning technologies have been adopted with the growing interest.

Various scenarios where machine learning plays a vital role are:

1) For the complex systems where it is difficult to design an algorithm
2) Complex systems that require to operate on complex data sets
3) Application requiring software to adjust to operational environment

Thus, machine learning plays an important role not only in the field of computer science but in applications which require in depth analysis like Big Data and Business Intelligence.

The following are the best use cases for machine learning in the enterprise:

1. Process Automation
2. Sales Optimization
3. Customer Service
4. Security
5. Collaboration

Cognitive Science is the study of mind and a multi-disciplinary field. It works on the concept of how it does and what it does. The applications of cognitive science are widespread:
1. Determine the relations at international level
2. Determine arrangement of products to boost the sales
3. Determine the best training for teachers
4. Methods of rehabilitation for addicts
The applications of machine learning and cognitive science vary across a wide range of fields and are not limited to above mentioned examples.

2019-02-26T08:06:58+00:00