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Sebastian Thrun,
Thad Starner,
and
Peter Norvig
Master probabilistic graphical models with our comprehensive online training course. Learn the intricacies of Bayes Nets, algorithms, and advanced modeling.
Prerequisite details
Read more
Master probabilistic graphical models with our comprehensive online training course. Learn the intricacies of Bayes Nets, algorithms, and advanced modeling.
Prerequisite details
Read more
Master probabilistic graphical models with our comprehensive online training course. Learn the intricacies of Bayes Nets, algorithms, and advanced modeling.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Scripting
- Jupyter notebooks
- Basic data structures and algorithms
- Basic descriptive statistics
- Intermediate Python
- Linear algebra
- Differential calculus
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/4c92j6/fundamentals
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What's inside
Syllabus
Welcome to Fundamentals of Probabilistic Graphical Models. In this lesson, we will cover the course overview, prerequisites, and do a brief introduction to probability.
Read more
Syllabus
Welcome to Fundamentals of Probabilistic Graphical Models. In this lesson, we will cover the course overview, prerequisites, and do a brief introduction to probability.
Read more
Sebastian Thrun briefly reviews basic probability theory including discrete distributions, independence, joint probabilities, and conditional distributions to model uncertainty in the real world.
In this section, you'll learn how to build a spam email classifier using the naive Bayes algorithm.
Sebastian explains using Bayes Nets as a compact graphical model to encode probability distributions for efficient analysis.
Sebastian explains probabilistic inference using Bayes Nets, i.e. how to use evidence to calculate probabilities from the network.
Learn Hidden Markov Models, and apply them to part-of-speech tagging, a very popular problem in Natural Language Processing.
Thad explains the Dynamic Time Warping technique for working with time-series data.
In this project, you'll build a hidden Markov model for part of speech tagging with a universal tagset.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops competence in probabilistic graphical models, which are used in many domains, including natural language processing, speech recognition, and image processing
Taught by Dr. Sebastian Thrun, Thad Starner, and Peter Norvig who are authorities in Artificial Intelligence, Robotics, and Computer Science
Strengthens an existing foundation for intermediate learners
Provides hands-on practice through projects, which helps learners apply concepts to real-world problems
Assumes learners have proficiency in scripting, Jupyter notebooks, and Python programming
Save this course
Save Fundamentals of Probabilistic Graphical Models 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 Fundamentals of Probabilistic Graphical Models with these
activities:
Refresher: Naive Bayes from first principles
Show steps
Review the basics of Naive Bayes to understand its core concepts and prepare for the course.
Browse courses on
Naive Bayes
Show steps
-
Revisit conditional probabilities and joint probabilities
-
Understand Bayes' theorem and its application in Naive Bayes
-
Practice implementing a Naive Bayes classifier from scratch
Create a Comprehensive Resource Collection
Show steps
Enhance your learning experience by organizing and expanding your notes and course materials into a comprehensive resource collection.
Browse courses on
Probabilistic Graphical Models
Show steps
-
Gather all relevant materials from the course, including notes, assignments, and lecture slides
-
Organize the materials into logical categories and subcategories
-
Create a system for easy retrieval and review
Dynamic Time Warping Practice Problems
Show steps
Sharpen your skills by solving practice problems involving Dynamic Time Warping.
Browse courses on
Time Series Analysis
Show steps
-
Study the fundamentals of Dynamic Time Warping
-
Attempt a set of practice problems to apply your understanding
-
Analyze your solutions and identify areas for improvement
Five other activities
Expand to see all activities and additional details
Show all eight activities
Interactive Tutorial: Bayes Nets with Python
Show steps
Deepen your understanding of Bayes Nets through hands-on practice with Python.
Show steps
-
Follow an interactive tutorial on building and manipulating Bayes Nets
-
Implement Bayesian inference algorithms using Python
-
Explore practical applications of Bayes Nets
Build a Spam Email Classifier
Show steps
Apply your understanding of probabilistic graphical models by building a practical spam email classifier.
Browse courses on
Spam Filtering
Show steps
-
Collect a dataset of spam and non-spam emails
-
Preprocess the data and extract relevant features
-
Implement a Naive Bayes classifier to classify emails
-
Evaluate the performance of your classifier and identify areas for improvement
Study Group: Advanced Modeling with Probabilistic Graphical Models
Show steps
Engage in collaborative learning and knowledge sharing by joining a study group focused on advanced modeling with probabilistic graphical models.
Browse courses on
Probabilistic Graphical Models
Show steps
-
Find or form a study group with peers who share similar interests
-
Establish regular meeting times and a communication channel
-
Brainstorm ideas for advanced modeling projects
-
Work together to implement and evaluate your models
Blog Post: Hidden Markov Models in Natural Language Processing
Show steps
Expand your knowledge by creating a blog post that explains the application of Hidden Markov Models in Natural Language Processing.
Browse courses on
Hidden Markov Models
Show steps
-
Research and gather information on Hidden Markov Models and their use in NLP
-
Choose a specific NLP task, such as part-of-speech tagging, and demonstrate how HMMs are used to solve it
-
Write a clear and well-structured blog post explaining the concepts and implementation
Contribute to a Probabilistic Graphical Models Library
Show steps
Gain practical experience and contribute to the community by contributing to an open-source probabilistic graphical models library.
Browse courses on
Open Source
Show steps
-
Identify an open-source probabilistic graphical models library
-
Choose a specific feature or enhancement to work on
-
Implement your changes and submit a pull request
-
Review feedback and iterate on your contribution
Refresher: Naive Bayes from first principles
Show steps
Review the basics of Naive Bayes to understand its core concepts and prepare for the course.
Browse courses on
Naive Bayes
Show steps
Show steps
Show steps
- Revisit conditional probabilities and joint probabilities
- Understand Bayes' theorem and its application in Naive Bayes
- Practice implementing a Naive Bayes classifier from scratch
Create a Comprehensive Resource Collection
Show steps
Enhance your learning experience by organizing and expanding your notes and course materials into a comprehensive resource collection.
Browse courses on
Probabilistic Graphical Models
Show steps
Show steps
Show steps
- Gather all relevant materials from the course, including notes, assignments, and lecture slides
- Organize the materials into logical categories and subcategories
- Create a system for easy retrieval and review
Dynamic Time Warping Practice Problems
Show steps
Sharpen your skills by solving practice problems involving Dynamic Time Warping.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Study the fundamentals of Dynamic Time Warping
- Attempt a set of practice problems to apply your understanding
- Analyze your solutions and identify areas for improvement
Five other activities
Expand to see all activities and additional details
Show all eight activities
Interactive Tutorial: Bayes Nets with Python
Show steps
Deepen your understanding of Bayes Nets through hands-on practice with Python.
Show steps
Show steps
Show steps
- Follow an interactive tutorial on building and manipulating Bayes Nets
- Implement Bayesian inference algorithms using Python
- Explore practical applications of Bayes Nets
Build a Spam Email Classifier
Show steps
Apply your understanding of probabilistic graphical models by building a practical spam email classifier.
Browse courses on
Spam Filtering
Show steps
Show steps
Show steps
- Collect a dataset of spam and non-spam emails
- Preprocess the data and extract relevant features
- Implement a Naive Bayes classifier to classify emails
- Evaluate the performance of your classifier and identify areas for improvement
Study Group: Advanced Modeling with Probabilistic Graphical Models
Show steps
Engage in collaborative learning and knowledge sharing by joining a study group focused on advanced modeling with probabilistic graphical models.
Browse courses on
Probabilistic Graphical Models
Show steps
Show steps
Show steps
- Find or form a study group with peers who share similar interests
- Establish regular meeting times and a communication channel
- Brainstorm ideas for advanced modeling projects
- Work together to implement and evaluate your models
Blog Post: Hidden Markov Models in Natural Language Processing
Show steps
Expand your knowledge by creating a blog post that explains the application of Hidden Markov Models in Natural Language Processing.
Browse courses on
Hidden Markov Models
Show steps
Show steps
Show steps
- Research and gather information on Hidden Markov Models and their use in NLP
- Choose a specific NLP task, such as part-of-speech tagging, and demonstrate how HMMs are used to solve it
- Write a clear and well-structured blog post explaining the concepts and implementation
Contribute to a Probabilistic Graphical Models Library
Show steps
Gain practical experience and contribute to the community by contributing to an open-source probabilistic graphical models library.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source probabilistic graphical models library
- Choose a specific feature or enhancement to work on
- Implement your changes and submit a pull request
- Review feedback and iterate on your contribution
Career center
Learners who complete Fundamentals of Probabilistic Graphical Models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer constructs new machine learning models and applies them to business problems. The Fundamentals of Probabilistic Graphical Models course can help you build a foundation in the models and algorithms used in this work, and will make you more effective at implementing machine learning concepts to solve business problems.
Data Scientist
Data Scientist
A Data Scientist uses mathematics, statistics, visualization, and modeling to understand data patterns, make predictions, and find insights, as well as build new models and systems. This Fundamentals of Probabilistic Graphical Models course may be especially helpful if you want to build a foundation in using Bayes Nets, algorithms, and advanced modeling. These are necessary skills for a Data Scientist, and the knowledge you will gain in this course will enable you to more effectively build new models and systems.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to understand and solve problems, often in scientific research. This Fundamentals of Probabilistic Graphical Models course will introduce some of the models and algorithms they use to solve business problems.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst, or Quant, uses quantitative models and techniques to solve business problems in banking and finance. The Fundamentals of Probabilistic Graphical Models course can help build a foundation for this career by teaching you about the underlying math, models, and algorithms used in Quantitative Analysis.
Data Analyst
Data Analyst
A Data Analyst uses data to solve business problems. This Fundamentals of Probabilistic Graphical Models course will be especially useful if you want to build a foundation in the models and algorithms used in this work.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and maintains computer systems and software. The Fundamentals of Probabilistic Graphical Models course will help you to build the math and statistics foundation necessary to solve complex system problems and apply new modeling concepts in the field.
Market Researcher
Market Researcher
A Market Researcher uses data and analysis to help businesses make informed decisions. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Business Analyst
Business Analyst
A Business Analyst uses data and analysis to solve business problems. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses math and statistics to solve business problems in various industries. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Financial Analyst
Financial Analyst
A Financial Analyst uses financial data to recommend investments and make other financial decisions. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Risk Analyst
Risk Analyst
A Risk Analyst uses math and statistics to measure and manage risk in various industries. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Actuary
Actuary
An Actuary uses math and statistics to measure and manage risk, often in the insurance industry. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data systems and infrastructure. The Fundamentals of Probabilistic Graphical Models course will help you to build the math and statistics foundation necessary to design and implement data systems and infrastructure that can handle complex models and algorithms.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data and analysis to help businesses make informed decisions. The Fundamentals of Probabilistic Graphical Models course will help you to build a foundation in the math and statistics used in this field.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist uses data to create visual representations that communicate information and insights. The Fundamentals of Probabilistic Graphical Models course will help you to build the math and statistics foundation necessary to understand and interpret data for visualization purposes.
For more career information including salaries, visit:
OpenCourser.com/course/4c92j6/fundamentals
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
Fundamentals of Probabilistic Graphical Models.
Provides a comprehensive overview of probabilistic graphical models, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about this important topic.
Provides a probabilistic perspective on machine learning, covering a wide range of topics from supervised learning to unsupervised learning. It valuable resource for anyone interested in learning more about the probabilistic foundations of machine learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about this important topic.
Provides a comprehensive overview of graphical models, exponential families, and variational inference. It valuable resource for anyone interested in learning more about these important topics.
Provides a concise and accessible introduction to Bayesian statistics. It covers the basics of Bayesian inference, as well as more advanced topics such as hierarchical models and Markov chain Monte Carlo. It valuable resource for anyone interested in learning more about Bayesian statistics.
Provides a comprehensive overview of probabilistic robotics. It covers a wide range of topics, from robot motion planning to sensor data fusion. It valuable resource for anyone interested in learning more about using probabilistic methods for robotics.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from neural networks to reinforcement learning. It valuable resource for anyone interested in learning more about deep learning.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, from Markov decision processes to deep reinforcement learning. It valuable resource for anyone interested in learning more about reinforcement learning.
Save
Provides a comprehensive overview of computer vision. It covers a wide range of topics, from image processing to object recognition. It valuable resource for anyone interested in learning more about computer vision.
Provides a comprehensive overview of natural language processing. It covers a wide range of topics, from natural language understanding to natural language generation. It valuable resource for anyone interested in learning more about natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4c92j6/fundamentals
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Effort
3 weeks
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Thad Starner
Peter Norvig
Language
English
Good to know
Develops competence in probabilistic graphical models, which are used in many domains, including natural language processing, speech recognition, and image processing
Taught by Dr. Sebastian Thrun, Thad Starner, and Peter Norvig who are authorities in Artificial Intelligence, Robotics, and Computer Science
Strengthens an existing foundation for intermediate learners
Provides hands-on practice through projects, which helps learners apply concepts to real-world problems
Assumes learners have proficiency in scripting, Jupyter notebooks, and Python programming
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