Advanced AI Techniques for the Supply Chain

LearnQuest via Coursera

Coursera based on 14 ratings

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Overview

Advanced AI Techniques for the Supply Chain is a specialized course designed for professionals and data practitioners looking to apply cutting-edge artificial intelligence methods to modernize and optimize supply chain operations. This course goes beyond basic analytics, focusing on how advanced AI—such as machine learning, deep learning, and reinforcement learning—can transform logistics, inventory management, demand forecasting, and supplier networks. Participants will learn how to implement predictive models to anticipate demand fluctuations, optimize routing and scheduling, and enhance procurement decisions. The course...

Syllabus

Machine Learning in the Supply Chain

In this module, we'll learn about the use cases of machine learning in the supply chain. We'll start with the big picture applications before diving deeper into specific algorithms, including neural networks. Throughout the module, we'll explain not only the general artificial intelligence concepts and mathematics, but also how these algorithms can specifically be used for the supply chain.

A Classical AI Approach

In this module, we'll cover the concepts relating to the ML paradigm. We'll start by learning how to pick a model, relying on considerations such as managing the bias-variance tradeoff. Next, we'll explore how machine learning models converge, including the use of stochastic gradient descent to minimize loss functions. Finally, we'll end with some practical considerations on coding advanced AI models with libraries for hyperparamter tuning.

Images and Text

In this module, we'll expand beyond numbers and learn how to use machine learning on images and text. We'll start by talking about how to analyze text data and cover the primary methods behind natural language processing. Then, we'll learn how to analyze images by constructing convolutional neural networks complete with convolutions and pooling layers.