This professional development course is designed for engineers and technicians who need an understanding of machine learning and its basic techniques.
Machine learning is a field of computer science that programs computers with the ability to learn from data and make informed, adaptive dynamic predictions and decisions using algorithms. It is related to computational statistics, mathematical optimization, and Artificial Intelligence (A.I.).
The last decade has witnessed exciting developments in machine learning that led to impressive consumer applications such as virtual assistants and speech recognition. This remarkable development results from increasingly powerful computers and the proliferation of smart objects’ data.
Machine learning will probably revolutionize every industry. Some applications already exist, but many are to come, especially with the advent of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). Autonomous vehicles, predictive maintenance, fault diagnosis, smart alarm processing, and advanced process control are few domains where machine learning could be applied in the industry.
Over 12 weeks, this course will aim to introduce you to the basic techniques used in machine learning. You will learn how techniques such as neural networks or decision trees can solve real-world problems. You will learn how to use MATLAB and WEKA software tools to apply machine learning in practice.
The course is composed of 12 modules, covering topics such as problem-solving through search algorithms, supervised learning methods and mathematical optimization, and using WEKA software and MATLAB for data analysis and machine learning.
To obtain a certificate of completion for EIT’s Professional Certificate of Competency, students must achieve a 65% attendance rate at the live, online fortnightly webinars. Detailed summaries/notes can be submitted in lieu of attendance. In addition, students must obtain a mark of 60% in the set assignments which could take the form of written assignments and practical assignments. Students must also obtain a mark of 100% in quizzes. If a student does not achieve the required score, they will be given an opportunity to resubmit the assignment to obtain the required score.
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You are expected to spend approximately 5-8 hours per week learning the course content. This includes attending fortnightly webinars that run for about 90 minutes to facilitate class discussion and allow you to ask questions. This professional development program is delivered online and has been designed to fit around full-time work. It will take three months to complete.
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