Statistical Learning for Engineering Part 2
$49
ENROLL NOWCourse Overview
What You'll Learn
- This course covers practical algorithms and the theory for machine learning from a variety of perspectives.
- Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods).
- The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics.
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch.
Course FAQs
Is this an accredited online course?
Accreditation for 'Statistical Learning for Engineering Part 2' is determined by the provider, Northeastern University. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
Many of the courses listed on our platform are suitable for professional continuing education. However, acceptance for credit varies by state and licensing board. Please confirm with your board and {course.provider} that this specific course qualifies.
How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'Statistical Learning for Engineering Part 2' on the Northeastern University online class platform, where you can complete your registration.
