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Sebastian Thrun,
Thad Starner,
and
Peter Norvig
Additional lecture material on hidden Markov models and applications for gesture recognition.
Register for this course and see more details by visiting:
OpenCourser.com/course/rr8l33/extracurricula
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What's inside
Syllabus
Thad returns to discuss using Hidden Markov Models for pattern recognition with sequential data.
Thad shares advanced techniques that can improve performance of HMMs recognizing American Sign Language, and more complex HMM models for applications like speech synthesis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by researchers who are highly accomplished in work with gesture recognition
Develops techniques for improving performance of Hidden Markov Models for gesture recognition
Examines advanced Hidden Markov Modeling for speech synthesis
Save this course
Save Extracurricular to your list so you can find it easily later:
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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 Extracurricular with these
activities:
Review probabilistic modeling concepts
Show steps
Revisiting probability theory and statistics will strengthen your foundation for understanding the probabilistic nature of HMMs.
Browse courses on
Probability Theory
Show steps
-
Review textbooks or online resources on probability theory.
-
Solve practice problems and exercises to test your understanding.
Follow online tutorials on advanced HMM techniques
Show steps
Online tutorials can provide step-by-step guidance on advanced HMM techniques, such as Viterbi training and Baum-Welch reestimation.
Browse courses on
Hidden Markov Models
Show steps
-
Identify online tutorials covering advanced HMM techniques.
-
Follow the tutorials and implement the techniques.
-
Apply the techniques to real-world datasets to evaluate their effectiveness.
Practice building HMMs from scratch
Show steps
Building HMMs from scratch will deepen your understanding of the underlying principles and help you troubleshoot implementation issues when using libraries.
Browse courses on
Hidden Markov Models
Show steps
-
Start with a simple 1D HMM with discrete states.
-
Extend to 2D HMMs to model more complex sequential data.
-
Implement the forward-backward algorithm for inference.
-
Experiment with different parameter estimation techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve HMM-based recognition tasks
Show steps
Solving real-world recognition tasks using HMMs will solidify your understanding of how to apply the theory to practical problems.
Browse courses on
Hidden Markov Models
Show steps
-
Create an HMM for recognizing a set of gestures.
-
Train the HMM on a labeled dataset.
-
Evaluate the HMM on a test set.
Mentor a beginner in HMMs
Show steps
Mentoring a beginner reinforces your understanding of HMMs and develops your communication and teaching skills.
Browse courses on
Hidden Markov Models
Show steps
-
Identify a beginner who is interested in learning about HMMs.
-
Schedule regular meetings to guide the learner through the concepts.
-
Provide resources, answer questions, and offer feedback.
Create a blog post or video tutorial explaining HMMs
Show steps
Explaining HMMs to others will force you to deeply understand the concepts and improve your communication skills.
Browse courses on
Hidden Markov Models
Show steps
-
Choose a topic related to HMMs that you want to explain.
-
Research and gather information about the topic.
-
Write a blog post or create a video tutorial explaining the topic clearly and concisely.
-
Publish your blog post or video tutorial and share it with others.
Attend a conference or workshop on HMMs
Show steps
Networking at conferences and workshops allows you to connect with experts and learn about the latest advancements in HMM research.
Browse courses on
Hidden Markov Models
Show steps
-
Identify relevant conferences or workshops.
-
Register for the event and plan your attendance.
-
Attend sessions, participate in discussions, and network with attendees.
Contribute to an open-source HMM library
Show steps
Contributing to an open-source HMM library provides practical experience with HMM implementation and exposes you to real-world codebases.
Browse courses on
Hidden Markov Models
Show steps
-
Identify an open-source HMM library to contribute to.
-
Fork the repository and create a branch for your changes.
-
Implement new features, fix bugs, or improve documentation.
-
Submit a pull request and engage with the maintainers to merge your changes.
Review probabilistic modeling concepts
Show steps
Revisiting probability theory and statistics will strengthen your foundation for understanding the probabilistic nature of HMMs.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Review textbooks or online resources on probability theory.
- Solve practice problems and exercises to test your understanding.
Follow online tutorials on advanced HMM techniques
Show steps
Online tutorials can provide step-by-step guidance on advanced HMM techniques, such as Viterbi training and Baum-Welch reestimation.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Identify online tutorials covering advanced HMM techniques.
- Follow the tutorials and implement the techniques.
- Apply the techniques to real-world datasets to evaluate their effectiveness.
Practice building HMMs from scratch
Show steps
Building HMMs from scratch will deepen your understanding of the underlying principles and help you troubleshoot implementation issues when using libraries.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Start with a simple 1D HMM with discrete states.
- Extend to 2D HMMs to model more complex sequential data.
- Implement the forward-backward algorithm for inference.
- Experiment with different parameter estimation techniques.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve HMM-based recognition tasks
Show steps
Solving real-world recognition tasks using HMMs will solidify your understanding of how to apply the theory to practical problems.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Create an HMM for recognizing a set of gestures.
- Train the HMM on a labeled dataset.
- Evaluate the HMM on a test set.
Mentor a beginner in HMMs
Show steps
Mentoring a beginner reinforces your understanding of HMMs and develops your communication and teaching skills.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Identify a beginner who is interested in learning about HMMs.
- Schedule regular meetings to guide the learner through the concepts.
- Provide resources, answer questions, and offer feedback.
Create a blog post or video tutorial explaining HMMs
Show steps
Explaining HMMs to others will force you to deeply understand the concepts and improve your communication skills.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Choose a topic related to HMMs that you want to explain.
- Research and gather information about the topic.
- Write a blog post or create a video tutorial explaining the topic clearly and concisely.
- Publish your blog post or video tutorial and share it with others.
Attend a conference or workshop on HMMs
Show steps
Networking at conferences and workshops allows you to connect with experts and learn about the latest advancements in HMM research.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Identify relevant conferences or workshops.
- Register for the event and plan your attendance.
- Attend sessions, participate in discussions, and network with attendees.
Contribute to an open-source HMM library
Show steps
Contributing to an open-source HMM library provides practical experience with HMM implementation and exposes you to real-world codebases.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Identify an open-source HMM library to contribute to.
- Fork the repository and create a branch for your changes.
- Implement new features, fix bugs, or improve documentation.
- Submit a pull request and engage with the maintainers to merge your changes.
Career center
Learners who complete Extracurricular will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their knowledge of mathematics, statistics, and computer science to build models that can learn from data and make predictions. This course may be useful for Machine Learning Engineers who want to learn more about hidden Markov models and their applications in gesture recognition.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They use these insights to solve business problems and make informed decisions. This course may be useful for Data Scientists who want to learn more about hidden Markov models and their applications in gesture recognition.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve problems in a variety of industries. They use their skills to improve efficiency and productivity. This course may be useful for Operations Research Analysts who want to learn more about hidden Markov models and their applications in gesture recognition.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their knowledge of statistics to draw conclusions about the world around them. This course may be useful for Statisticians who want to learn more about hidden Markov models and their applications in gesture recognition.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science. They develop new theories and algorithms that can be used to solve problems in a variety of fields. This course may be useful for Computer Scientists who want to learn more about hidden Markov models and their applications in gesture recognition.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science to create software that meets the needs of users. This course may be useful for Software Engineers who want to learn more about hidden Markov models and their applications in gesture recognition.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data about consumer behavior. They use their findings to help businesses develop marketing strategies. This course may be useful for Market Researchers who want to learn more about hidden Markov models and their applications in gesture recognition.
Systems Analyst
Systems Analyst
Systems Analysts use their knowledge of business and technology to design and implement computer systems. They use their skills to ensure that systems meet the needs of users. This course may be useful for Systems Analysts who want to learn more about hidden Markov models and their applications in gesture recognition.
Management Consultant
Management Consultant
Management Consultants use their knowledge of business and technology to help businesses improve their performance. They use their skills to identify problems and develop solutions. This course may be useful for Management Consultants who want to learn more about hidden Markov models and their applications in gesture recognition.
Security Analyst
Security Analyst
Security Analysts use their knowledge of security to protect computer systems and networks from attack. They use their skills to identify vulnerabilities and develop security measures. This course may be useful for Security Analysts who want to learn more about hidden Markov models and their applications in gesture recognition.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to analyze business processes and identify opportunities for improvement. They use their skills to help businesses make better decisions. This course may be useful for Business Analysts who want to learn more about hidden Markov models and their applications in gesture recognition.
Database Administrator
Database Administrator
Database Administrators use their knowledge of databases to design, implement, and maintain database systems. They use their skills to ensure that databases are reliable and efficient. This course may be useful for Database Administrators who want to learn more about hidden Markov models and their applications in gesture recognition.
Network Administrator
Network Administrator
Network Administrators use their knowledge of networks to design, implement, and maintain computer networks. They use their skills to ensure that networks are reliable and efficient. This course may be useful for Network Administrators who want to learn more about hidden Markov models and their applications in gesture recognition.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of mathematics, statistics, and finance to analyze financial data. They use their findings to make investment recommendations and advise clients on financial matters. This course may be useful for Financial Analysts who want to learn more about hidden Markov models and their applications in gesture recognition.
Actuary
Actuary
Actuaries use their knowledge of mathematics, statistics, and finance to assess risk and uncertainty. They use their skills to develop insurance products and advise clients on risk management. This course may be useful for Actuaries who want to learn more about hidden Markov models and their applications in gesture recognition.
For more career information including salaries, visit:
OpenCourser.com/course/rr8l33/extracurricula
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
Extracurricular.
This comprehensive textbook covers a wide range of machine learning topics, including hidden Markov models. It valuable resource for those seeking a broad understanding of machine learning and its applications, including in the context of gesture recognition.
This widely-used textbook provides a comprehensive overview of speech and language processing, including chapters on hidden Markov models and their applications in speech recognition and synthesis.
This classic textbook provides a comprehensive treatment of hidden Markov models. It valuable resource for those seeking a thorough understanding of the theory and applications of HMMs.
This practical guide provides a hands-on introduction to machine learning, including a chapter on hidden Markov models. It valuable resource for those seeking to apply HMMs in real-world applications.
This textbook provides a comprehensive overview of machine learning, including a chapter on hidden Markov models. It valuable resource for those seeking a broad understanding of machine learning and its applications.
This practical guide provides a hands-on introduction to machine learning, including a chapter on hidden Markov models. It valuable resource for those seeking to apply HMMs in real-world applications using popular machine learning libraries.
This textbook provides a comprehensive overview of machine learning, including a chapter on hidden Markov models. It valuable resource for those seeking a broad understanding of machine learning and its applications.
This practical guide provides a hands-on introduction to machine learning, including a chapter on hidden Markov models. It valuable resource for those seeking to apply HMMs in real-world applications.
Save
This textbook provides a comprehensive overview of data mining, including a chapter on hidden Markov models. It valuable resource for those seeking a broad understanding of data mining and its applications.
This textbook provides a comprehensive overview of machine learning in the R programming language, including a chapter on hidden Markov models. It valuable resource for those seeking to apply HMMs in real-world applications using R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rr8l33/extracurricula
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Time
75 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Thad Starner
Peter Norvig
Language
English
Good to know
Taught by researchers who are highly accomplished in work with gesture recognition
Develops techniques for improving performance of Hidden Markov Models for gesture recognition
Examines advanced Hidden Markov Modeling for speech synthesis
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