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Rajvir Dua
and
Neelesh Tiruviluamala
In this course, we’ll learn about more advanced machine learning methods that are used to tackle problems in the supply chain. We’ll start with an overview of the different ML paradigms (regression/classification) and where the latest models fit into these breakdowns. Then, we’ll dive deeper into some of the specific techniques and use cases such as using neural networks to predict product demand and random forests to classify products. An important part to using these models is understanding their assumptions and required preprocessing steps. We’ll end with a project incorporating advanced techniques with an image classification problem to find faulty products coming out of a machine.
Register for this course and see more details by visiting:
OpenCourser.com/course/bz95wk/advanced
What's inside
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.
Read more
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.
Read more
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.
Final Project: Detecting Anomalies with Image Classification
In this final project, we’ll apply what we learned in the last module to classify images of products based on whether there is a defect or not.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines real world supply chain problems and use cases of AI and ML in solving these issues
Explores advanced AI concepts namely neural networks, image analysis, and natural language processing
Teaches practical considerations of coding using libraries and frameworks
Taught by Neelesh Tiruviluamala, an instructor with experience in supply chain management and data science
Provides hands-on labs and downloadable resources that cover majority of the course concepts
Assumes knowledge of Python and basic computer science concepts
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Reviews summary
Course not recommended
Learners say that this course titled Advanced AI Techniques for the Supply Chain is not recommended. Students have complained that this course is not a supply chain course and is actually more like an intro to neural networks.
Course is not recommended.
"This is an intro to neural networks, not a supply chain course."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Advanced AI Techniques for the Supply Chain with these
activities:
Organize and review course materials
Show steps
Stay organized and prepare for the course by reviewing and compiling notes, assignments, and other materials.
Show steps
-
Gather all course materials, including notes, assignments, and textbook readings
-
Organize the materials into a logical and accessible structure
Compile a library of machine learning resources
Show steps
Organize and collect valuable resources such as articles, videos, and datasets to support your learning and reference in the future.
Browse courses on
Machine Learning
Show steps
-
Identify and gather relevant resources from various sources
-
Organize the resources into a structured and accessible format
Review applied mathematics
Show steps
Refresh your understanding of applied mathematics concepts, such as linear algebra and calculus, which are essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review notes and textbooks from previous math courses
-
Solve practice problems and exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with a mentor in the field of machine learning
Show steps
Seek guidance and support from an experienced professional who can provide personalized advice and insights.
Browse courses on
Machine Learning
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out to mentors and request their guidance
Participate in a machine learning study group
Show steps
Connect with fellow learners and discuss course concepts, share insights, and work through problems together.
Browse courses on
Machine Learning
Show steps
-
Find or create a study group with other students taking the course
-
Meet regularly to discuss course material, ask questions, and collaborate on projects
Build a simple machine learning model
Show steps
Gain hands-on experience by implementing a basic machine learning model, such as a linear regression or decision tree.
Browse courses on
Supervised Learning
Show steps
-
Choose a dataset and define the problem
-
Select and apply an appropriate machine learning algorithm
-
Train and evaluate the model
Complete online courses or tutorials on advanced machine learning
Show steps
Supplement your learning by exploring advanced machine learning concepts through online resources, such as Coursera or edX.
Browse courses on
Deep Learning
Show steps
-
Identify online courses or tutorials that align with your interests and learning goals
-
Follow the course or tutorial diligently, completing assignments and quizzes
Participate in a machine learning hackathon or competition
Show steps
Challenge yourself by applying your skills in a competitive setting and gain valuable experience in solving real-world problems.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a hackathon or competition that aligns with your interests
-
Team up with other participants or work individually
-
Develop a solution to the specified problem statement using machine learning techniques
Organize and review course materials
Show steps
Stay organized and prepare for the course by reviewing and compiling notes, assignments, and other materials.
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, and textbook readings
- Organize the materials into a logical and accessible structure
Compile a library of machine learning resources
Show steps
Organize and collect valuable resources such as articles, videos, and datasets to support your learning and reference in the future.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and gather relevant resources from various sources
- Organize the resources into a structured and accessible format
Review applied mathematics
Show steps
Refresh your understanding of applied mathematics concepts, such as linear algebra and calculus, which are essential for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes and textbooks from previous math courses
- Solve practice problems and exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Connect with a mentor in the field of machine learning
Show steps
Seek guidance and support from an experienced professional who can provide personalized advice and insights.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out to mentors and request their guidance
Participate in a machine learning study group
Show steps
Connect with fellow learners and discuss course concepts, share insights, and work through problems together.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course
- Meet regularly to discuss course material, ask questions, and collaborate on projects
Build a simple machine learning model
Show steps
Gain hands-on experience by implementing a basic machine learning model, such as a linear regression or decision tree.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Select and apply an appropriate machine learning algorithm
- Train and evaluate the model
Complete online courses or tutorials on advanced machine learning
Show steps
Supplement your learning by exploring advanced machine learning concepts through online resources, such as Coursera or edX.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify online courses or tutorials that align with your interests and learning goals
- Follow the course or tutorial diligently, completing assignments and quizzes
Participate in a machine learning hackathon or competition
Show steps
Challenge yourself by applying your skills in a competitive setting and gain valuable experience in solving real-world problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a hackathon or competition that aligns with your interests
- Team up with other participants or work individually
- Develop a solution to the specified problem statement using machine learning techniques
Career center
Learners who complete Advanced AI Techniques for the Supply Chain will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models. This course may be useful for Machine Learning Engineers who want to work on problems in the supply chain. The course covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation, which are all essential for Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists use data analysis and machine learning to solve business problems. This course may be useful for Data Scientists who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as neural networks, random forests, and image classification, which are all relevant to the work of Data Scientists in the supply chain.
Transportation Manager
Transportation Manager
Transportation Managers are responsible for the planning and execution of the transportation operations of a company. This course may be useful for Transportation Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their transportation operations. The course covers topics such as transportation planning, transportation scheduling, and transportation optimization, which are all relevant to the work of Transportation Managers.
Supply Chain Manager
Supply Chain Manager
Supply Chain Managers are responsible for the planning and execution of the supply chain. This course may be useful for Supply Chain Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of the supply chain. The course covers topics such as inventory management, transportation, and warehousing, which are all relevant to the work of Supply Chain Managers.
Logistics Manager
Logistics Manager
Logistics Managers are responsible for the planning and execution of the logistics operations of a company. This course may be useful for Logistics Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their logistics operations. The course covers topics such as transportation, warehousing, and inventory management, which are all relevant to the work of Logistics Managers.
Inventory Manager
Inventory Manager
Inventory Managers are responsible for the planning and management of inventory for a company. This course may be useful for Inventory Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their inventory management operations. The course covers topics such as inventory forecasting, inventory optimization, and inventory control, which are all relevant to the work of Inventory Managers.
Warehouse Manager
Warehouse Manager
Warehouse Managers are responsible for the planning and management of a warehouse. This course may be useful for Warehouse Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their warehouse operations. The course covers topics such as warehouse design, warehouse operations, and warehouse management systems, which are all relevant to the work of Warehouse Managers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. This course may be useful for Operations Research Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as optimization, simulation, and decision analysis, which are all relevant to the work of Operations Research Analysts in the supply chain.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. This course may be useful for Business Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as data mining, forecasting, and risk analysis, which are all relevant to the work of Business Analysts in the supply chain.
Quality Control Manager
Quality Control Manager
Quality Control Managers are responsible for the planning and execution of quality control operations for a company. This course may be useful for Quality Control Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their quality control operations. The course covers topics such as quality control planning, quality control inspection, and quality control analysis, which are all relevant to the work of Quality Control Managers.
Procurement Manager
Procurement Manager
Procurement Managers are responsible for the sourcing and procurement of goods and services for a company. This course may be useful for Procurement Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their procurement operations. The course covers topics such as supplier management, contract management, and risk management, which are all relevant to the work of Procurement Managers.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying, assessing, and mitigating risks for a company. This course may be useful for Risk Managers who want to use advanced AI techniques to improve the efficiency and effectiveness of their risk management operations. The course covers topics such as risk identification, risk assessment, and risk mitigation, which are all relevant to the work of Risk Managers.
Financial Analyst
Financial Analyst
Financial Analysts use data and analysis to help businesses make better financial decisions. This course may be useful for Financial Analysts who want to apply advanced AI techniques to solve problems in the supply chain. The course covers topics such as financial modeling, financial forecasting, and financial risk analysis, which are all relevant to the work of Financial Analysts in the supply chain.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts use data and modeling to improve the flow of goods, services, and resources within a supply chain. This course may be useful for developing the skills needed to analyze and predict demand, classify products, and identify potential problems in the supply chain using advanced AI techniques. These techniques can help Supply Chain Analysts optimize inventory levels, reduce costs, and improve customer service.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who want to use advanced AI techniques to improve the design and development of new products. The course covers topics such as user research, data analysis, and market research, which are all relevant to the work of Product Managers.
For more career information including salaries, visit:
OpenCourser.com/course/bz95wk/advanced
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced AI Techniques for the Supply Chain.
A comprehensive and highly acclaimed textbook that provides a thorough grounding in statistical learning, including supervised and unsupervised learning methods.
Save
A comprehensive and in-depth guide to deep learning, covering both theoretical foundations and practical applications.
Save
A comprehensive and in-depth guide to computer vision, covering topics such as image processing, feature extraction, and object recognition.
A practical and hands-on guide to deep learning, covering topics such as image classification, natural language processing, and computer vision.
A comprehensive textbook on supply chain management, covering topics such as supply chain strategy, planning, and operations.
A classic textbook that provides a comprehensive overview of machine learning, including advanced topics such as Bayesian inference and kernel methods.
A practical and hands-on guide to machine learning with Python libraries such as Scikit-Learn, Keras, and TensorFlow.
A practical guide to natural language processing, covering topics such as text classification, sentiment analysis, and machine translation.
A foundational book on lean supply chain management, providing insights into the principles and practices of lean.
A practical guide to machine learning with Python, covering both supervised and unsupervised learning methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bz95wk/advanced
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Effort
3 weeks of study, 3-5 hours per week
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructors
Rajvir Dua
Neelesh Tiruviluamala
Language
English
This course belongs to a
collection
called
Machine Learning for Supply Chains. It's comprised of four courses:
- Advanced AI Techniques for the Supply Chain (viewing)
- Capstone Project: Predicting Safety Stock
- Demand Forecasting Using Time Series
- Fundamentals of Machine Learning for Supply Chain
Good to know
Examines real world supply chain problems and use cases of AI and ML in solving these issues
Explores advanced AI concepts namely neural networks, image analysis, and natural language processing
Teaches practical considerations of coding using libraries and frameworks
Taught by Neelesh Tiruviluamala, an instructor with experience in supply chain management and data science
Provides hands-on labs and downloadable resources that cover majority of the course concepts
Assumes knowledge of Python and basic computer science concepts
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