BOUBAKER EL HADJ AMOR a effectué ses études supérieures à l'Ecole Normale Supérieure de Cachan en Intelligence Artificielle, Robotique, et Reconnaissance de formes. Il est maître de conférences en Signaux et Systèmes et Traitement d'Images à l'Insti...
Machine learning defines the development of algorithms that provide predictions based on the analysis of complex data patterns. By analyzing the given data, machine learning algorithms can extract information and make predictions based on the obtained information.
Boubaker EL HADJ AMOR discusses the different methods of Machine Learning:
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement of Machine Learning
Supervised Learning: Supervised machine learning identifies behavior patterns and makes predictions based on historical data. Data sets are used in this system. The inputs and outputs of a system are controlled by parameters. The machine learning algorithm analyzes the newly acquired data and determines the correct output based on the set parameters. Supervised learning is used to classify or predict data. Activities that fall under classification include image classification, face recognition, unsolicited email classification, fraud identification, and performing regressions, such as weather forecasting and population growth forecasting .
Unsupervised Learning: No classes or labels are used in unsupervised machine learning. This method finds hidden structures in unlabeled data so that functions can be correctly interpreted. To determine hidden structures in unlabeled data, it uses clustering or dimension reduction techniques. Grouping data on the basis of the same metric makes it possible to use the clustering technique. The clustering process is data-driven, and examples include recommender systems like Netflix, customer segmentation, shopping habits, and more. Examples of dimensionality reduction include
Semi-Supervised Learning: By combining labeled and unlabeled data, semi-supervised machine learning improves learning accuracy. In cases where data tagging is prohibitively expensive, semi-supervised learning may be the best option.
Reinforcement Machine Learning: Comparing supervised and unsupervised learning, reinforcement learning is very different. One experiment ended with a positive result as a result of trial and error. Teaching autonomous driving using reinforcement learning algorithms in simulated environments, such as Q-learning, has been developed. Moreover, reinforcement learning uses the principle of iterative improvement as a method to improve learning.
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Deep Learning
Deep learning refers to a form of machine learning that involves building algorithms based on a layered architecture. The raw input is gradually transformed into higher level features using multiple layers. Boubaker EL HADJ AMOR describes that image processing can include bottom layers identifying edges and top layers identifying concepts relevant to human beings, such as numbers, letters, or faces. Deep learning, also known as deep neural networks, is generally used for issues such as sound recognition, image recognition, natural language processing, etc.
Machine learning is part of data science, which also encompasses AI. Deep learning is also a sub-technology of machine learning. With the help of artificial intelligence (AI), we are able to solve increasingly difficult problems than humans, including detecting cancer more efficiently than oncologists.
Boubaker EL HADJ AMOR conducts research in the fields of robotics and image processing. He resides in Poitiers, France. He teaches Signals and Systems and Image Processing at the Higher Institute of Aeronautics and Space (ISAE-ENSMA) in Poitiers.
If you want to learn more about Deep Learning, Boubaker's expertise in this area can provide you with the information you need.
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