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Epaminondas Kapetanios
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations. Since this explanation technique is model agnostic and treats the predictions model as a 'black-box', the guided project can be useful for decision makers within business environments, e.g., loan officers at a bank, and public organizations interested in using trusted machine learning applications for automating, or informing, decision making processes.
Read more
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations. Since this explanation technique is model agnostic and treats the predictions model as a 'black-box', the guided project can be useful for decision makers within business environments, e.g., loan officers at a bank, and public organizations interested in using trusted machine learning applications for automating, or informing, decision making processes.
Read more
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations. Since this explanation technique is model agnostic and treats the predictions model as a 'black-box', the guided project can be useful for decision makers within business environments, e.g., loan officers at a bank, and public organizations interested in using trusted machine learning applications for automating, or informing, decision making processes.
The main learning objectives are as follows:
Learning objective 1: You will be able to define, train and evaluate an artificial neural network (Sequential model) based classifier by using keras as API for TensorFlow. The pediction model will be trained and tested with the HELOC dataset for approved and rejected mortgage applications.
Learning objective 2: You will be able to generate explanations based on similar profiles for a mortgage applicant predicted either as of "Good" or "Bad" risk performance.
Learning objective 3: you will be able to generate contrastive explanations based on feature and pertinent negative values, i.e., what an applicant should change in order to turn a "rejected" application to an "approved" one.
Register for this course and see more details by visiting:
OpenCourser.com/course/euh11y/interpretable
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What's inside
Syllabus
Interpretable/Explainable machine learning applications: Part 3
By the end of this project, you will be able to generate prototypical explanations, in the form of selecting similar user profiles, for predictions being made by an Artificial Neural Network (ANN) as a machine learning (ML) model. Given that ANNs add a considerable complexity, which makes predictions even more difficult to explain or interpret, you will also learn how to work around this challenge and still provide some explanations to end users. As a use case, we will be using mortgage applications on the basis of the HELOC data of applicants being accepted or rejected for such an application. We will also be using IBM’s AIX360 Protodash algorithm for this purpose. For example, as an explanation to a loan application being rejected, a bank loan officer may argue that this is justifiable because the number of satisfactory trades the applicant performed were low, similar to another rejected user, or because his/her debts were too high similar to a different rejected user. In this sense, the project will boost your career not only as ML developer and modeler finding a way to explain and justify the behaviour of complex ML models such as ANNs, but also as a decision-maker in a business environment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Knowing how to explain a complex model's predictions is critical in a business context
This course trains you to provide justification behind decisions, enhancing your role as a decision-maker
Even those with limited ML skills will find this course accessible as it focuses on a specific explanation technique rather than a complex set of algorithms
Although the use case is specific to loan applications, the overall approach can be applied to other business domains
The project-based format allows for direct implementation of the key concept
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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 Interpretable machine learning applications: Part 3 with these
activities:
Organize Course Notes and Assignments
Show steps
Organizing materials enhances the ability to review and reinforce the concepts covered.
Browse courses on
Organization
Show steps
-
Create a system for organizing notes
-
File and label assignments
Review Python Programming Basics
Show steps
A refresher on Python will ensure familiarity with the language used in the course.
Browse courses on
Python
Show steps
-
Review Python data types and structures
-
Practice writing simple Python scripts
Read 'Interpretable Machine Learning' by Christoph Molnar
Show steps
This book provides a comprehensive overview of interpretability techniques, including the concepts covered in the course.
View
Interpretable Machine Learning
on Amazon
Show steps
-
Read chapters 4-6
-
Identify the key concepts of interpretability
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Guided Project Exercises
Show steps
Completing the exercises will reinforce the concepts of building and explaining neural network models.
Browse courses on
Keras
Show steps
-
Review explanations of the HELOC dataset
-
Build the neural network model
-
Generate explanations for mortgage applications
Volunteer at a Local Data Science Organization
Show steps
Volunteering allows for practical application of the concepts learned in the course.
Browse courses on
Data Science
Show steps
-
Find a local data science organization
-
Inquire about volunteer opportunities
Create a Presentation on Prototypical Explanations
Show steps
Creating a presentation will enhance understanding of how prototypical explanations can be applied to real-world scenarios.
Show steps
-
Gather information on prototypical explanations
-
Develop a presentation outline
Write a Blog Post on Neural Network Interpretability
Show steps
Writing a blog post requires summarizing and explaining the concepts, leading to deeper understanding.
Browse courses on
Neural Networks
Show steps
-
Research neural network interpretability techniques
-
Write a draft of the blog post
Organize Course Notes and Assignments
Show steps
Organizing materials enhances the ability to review and reinforce the concepts covered.
Browse courses on
Organization
Show steps
Show steps
Show steps
- Create a system for organizing notes
- File and label assignments
Review Python Programming Basics
Show steps
A refresher on Python will ensure familiarity with the language used in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python data types and structures
- Practice writing simple Python scripts
Read 'Interpretable Machine Learning' by Christoph Molnar
Show steps
This book provides a comprehensive overview of interpretability techniques, including the concepts covered in the course.
View
Interpretable Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read chapters 4-6
- Identify the key concepts of interpretability
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Guided Project Exercises
Show steps
Completing the exercises will reinforce the concepts of building and explaining neural network models.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Review explanations of the HELOC dataset
- Build the neural network model
- Generate explanations for mortgage applications
Volunteer at a Local Data Science Organization
Show steps
Volunteering allows for practical application of the concepts learned in the course.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a local data science organization
- Inquire about volunteer opportunities
Create a Presentation on Prototypical Explanations
Show steps
Creating a presentation will enhance understanding of how prototypical explanations can be applied to real-world scenarios.
Show steps
Show steps
Show steps
- Gather information on prototypical explanations
- Develop a presentation outline
Write a Blog Post on Neural Network Interpretability
Show steps
Writing a blog post requires summarizing and explaining the concepts, leading to deeper understanding.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Research neural network interpretability techniques
- Write a draft of the blog post
Career center
Learners who complete Interpretable machine learning applications: Part 3 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will play a key role in developing and deploying machine learning models to solve business problems. This course will provide you with the skills and knowledge you need to build and interpret complex machine learning models, such as artificial neural networks. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by your models. This will enable you to communicate the results of your work to non-technical stakeholders and build trust in your models.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for designing, building, and deploying machine learning systems. This course will provide you with the skills and knowledge you need to develop and interpret complex machine learning models, such as artificial neural networks. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by your models. This will enable you to communicate the results of your work to non-technical stakeholders and build trust in your models.
Business Analyst
Business Analyst
As a Business Analyst, you will be responsible for gathering and analyzing data to identify business problems and opportunities. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Statistician
Statistician
As a Statistician, you will be responsible for collecting, analyzing, and interpreting data. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Insurance Analyst
Insurance Analyst
As an Insurance Analyst, you will be responsible for analyzing insurance data and making recommendations to clients. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Actuary
Actuary
As an Actuary, you will be responsible for using mathematical and statistical techniques to assess risk and uncertainty. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Risk Manager
Risk Manager
As a Risk Manager, you will be responsible for identifying, assessing, and mitigating risks. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Cybersecurity Analyst
Cybersecurity Analyst
As a Cybersecurity Analyst, you will be responsible for protecting computer systems from unauthorized access. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will be responsible for using mathematical and statistical techniques to solve business problems. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data and making recommendations to clients. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will be responsible for developing and using mathematical and statistical models to analyze financial data. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for designing, developing, and testing software applications. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Computer Scientist
Computer Scientist
As a Computer Scientist, you will be responsible for conducting research in the field of computer science. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
Data Architect
Data Architect
As a Data Architect, you will be responsible for designing and managing data systems. This course will provide you with the skills and knowledge you need to interpret complex machine learning models and communicate the results of your work to non-technical stakeholders. You will also learn how to use IBM's AIX360 Protodash algorithm to generate explanations for the predictions made by machine learning models. This will enable you to build trust in machine learning models and make better decisions.
For more career information including salaries, visit:
OpenCourser.com/course/euh11y/interpretable
Reading list
We've selected eight 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
Interpretable machine learning applications: Part 3.
Provides a comprehensive overview of interpretable machine learning techniques, including methods for explaining and understanding the predictions made by complex machine learning models. It would be particularly useful as a reference for the course's learning objectives 1 and 2.
Provides a thorough treatment of pattern recognition and machine learning algorithms, including a discussion of interpretability and model evaluation. It would be valuable as a reference for the course's learning objectives 1 and 2.
Provides a detailed overview of machine learning algorithms, including their strengths and weaknesses. It would be a useful reference for gaining a deeper understanding of the algorithms used in the course.
Provides a hands-on guide to building and training machine learning models using popular Python libraries. While it does not explicitly cover interpretability, it would be useful for gaining practical experience with machine learning techniques.
Offers a comprehensive introduction to machine learning concepts and algorithms using Python. It would be helpful as background reading for the course.
Provides a practical guide to building and training deep learning models using Python. While it does not specifically focus on interpretability, it would provide valuable background knowledge for the course's learning objective 1.
Offers a gentle introduction to machine learning concepts and algorithms, including a brief overview of interpretability. It would be helpful as background reading for students with limited prior knowledge in machine learning.
Provides a simplified introduction to machine learning concepts and algorithms. It would be helpful as background reading for students with no prior knowledge in machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/euh11y/interpretable
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Epaminondas Kapetanios
Language
English
Good to know
Knowing how to explain a complex model's predictions is critical in a business context
This course trains you to provide justification behind decisions, enhancing your role as a decision-maker
Even those with limited ML skills will find this course accessible as it focuses on a specific explanation technique rather than a complex set of algorithms
Although the use case is specific to loan applications, the overall approach can be applied to other business domains
The project-based format allows for direct implementation of the key concept
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