Course Description: Machine Learning can be deployed in manufacturing to significantly increase production efficiency and capacity. In this course, step-by-step tutorials on how to apply machine learning to analyze manufacturing data are presented. Students will learn how to create artificial intelligence solutions for manufacturing analytics.

Prerequisites: Please Note: Prior knowledge of machine learning and python programming is recommended to support success, but is not required.

Learning Outcome:
– Explain the benefits of machine learning in manufacturing
– Describe the common operations in developing machine learning applications
– Apply machine learning for manufacturing analytics

Faculty Name: Xiumin Diao  

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Course Description: This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on real-world examples in Python. The overall aim is to provide the students with a good understanding of machine-learning technologies, building machine learning with Python, and applying machine-learning technologies to address real-world problems.

Prerequisites: None

Learning Outcome: 
– Explain the relationship (main mechanisms, internal logic, computing components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoder, General Adversarial Network (GAN), and Reinforcement Learning (RL))
– Program the basic realization of the machine learning models, stated in Learning Objective 1, in Python
– Apply the eight machine learning models stated in Learning Objective 1 to solve real-world problems

Faculty: Jin Kocsis   

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