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Daphne Koller

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

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Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

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What's inside

Syllabus

Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
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This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.
Parameter Estimation in Bayesian Networks
This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.
Learning Undirected Models
In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.
Learning BN Structure
This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.
Learning BNs with Incomplete Data
In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
Learning Summary and Final
This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.
PGM Wrapup
This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Daphne Koller, a recognized expert in the field of probabilistic graphical models
Covers highly relevant and in-demand skills, such as Bayesian estimation and the Expectation Maximization (EM) algorithm
Provides a comprehensive overview of probabilistic graphical models and their applications, from medical diagnosis to image understanding
Assumes basic concepts from probability theory, graph algorithms, machine learning, and related topics
Requires a strong foundation in mathematics and probability, making it more suitable for experienced learners
Emphasizes the theory and algorithms behind probabilistic graphical models, but a stronger focus on practical applications may be beneficial

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Reviews summary

Intuitive probabilistic graphical models overview

Learners say that Probabilistic Graphical Models 3: Learning is a good course for laying a solid foundation in graphical models. With real world examples, engaging lectures, and a knowledgeable instructor, students feel well prepared to take their knowledge of graphical models to the next level.
Students feel well prepared due to the inclusion of real-world examples.
Many students say the lectures make probabilistic graphical models come alive.
"The way the teacher teaches sets a good example for me to learn to demonstrate complicated things in an easy and vivid way."

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 Probabilistic Graphical Models 3: Learning with these activities:
Organize your course notes and assignments
Having organized notes and assignments will make it easier for you to review the course material and prepare for assessments.
Show steps
  • Create a system for organizing your notes and assignments
  • Regularly review and update your organized materials
Review 'Probabilistic Graphical Models' by Koller and Friedman
This book provides a comprehensive overview of the theory and algorithms for probabilistic graphical models.
Show steps
  • Read the overview and introductory chapters
  • Focus on chapters relevant to the course material
  • Complete the exercises and review questions
Complete the Coursera tutorial on PGMs
This tutorial provides a gentle introduction to the concepts and algorithms of PGMs.
Show steps
  • Sign up for the Coursera tutorial
  • Watch the video lectures and complete the exercises
  • Review the tutorial materials
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve PGMs practice problems
Solving practice problems will reinforce your understanding of PGM concepts and algorithms.
Show steps
  • Identify the type of PGM you are working with
  • Apply the appropriate algorithm to solve the problem
  • Check your answer for correctness
Create a visual representation of a PGM
Creating a visual representation of a PGM will help you visualize the structure and relationships between the variables.
Show steps
  • Choose an appropriate software tool
  • Input the variables and their relationships
  • Generate the visual representation
Attend a study group or online forum
Discussing the course material with peers will help you understand the concepts more deeply.
Show steps
  • Find a study group or online forum
  • Participate in discussions and ask questions
  • Review the notes from the study group or forum
Develop a prototype PGM application
Developing a prototype PGM application will give you hands-on experience with the practical implementation of PGMs.
Show steps
  • Identify a problem that can be solved using a PGM
  • Design and implement the PGM application
  • Test and evaluate the application

Career center

Learners who complete Probabilistic Graphical Models 3: Learning will develop knowledge and skills that may be useful to these careers:
Data Scientist
As a Data Scientist, you will play a key role in building and deploying probabilistic graphical models to solve complex problems in various domains. This course will provide you with the foundational knowledge and skills necessary to excel in this role. You will learn how to represent and reason with probabilistic graphical models, and how to develop algorithms for parameter estimation and structure learning. This expertise will empower you to build and deploy data-driven models that can effectively handle complex and uncertain data, making you a highly sought-after professional in the field.
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning solutions to address real-world problems. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in data. You will learn how to apply these models to a wide range of machine learning tasks, including classification, regression, and clustering. This knowledge will enable you to build and deploy robust and accurate machine learning models, making you a valuable asset to any organization.
Artificial Intelligence Researcher
As an Artificial Intelligence Researcher, you will be at the forefront of developing new and innovative AI technologies. This course will provide you with a deep understanding of probabilistic graphical models, which are a fundamental tool for representing and reasoning with uncertainty in AI systems. You will learn how to apply these models to a wide range of AI problems, including natural language processing, computer vision, and robotics. This knowledge will empower you to push the boundaries of AI research and contribute to the development of groundbreaking AI technologies.
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze and predict financial markets. This course will provide you with a strong foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in financial data. You will learn how to apply these models to a wide range of financial problems, including risk management, portfolio optimization, and trading strategies. This knowledge will enable you to make informed and data-driven decisions in the financial markets, giving you a competitive edge in this highly competitive field.
Biostatistician
As a Biostatistician, you will use statistical methods to design and analyze clinical trials and other health-related studies. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in biological data. You will learn how to apply these models to a wide range of biostatistical problems, including disease modeling, biomarker discovery, and personalized medicine. This knowledge will empower you to make significant contributions to the field of biostatistics and improve the lives of patients.
Computational Biologist
As a Computational Biologist, you will use computational methods to study biological systems. This course will provide you with a strong foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in biological data. You will learn how to apply these models to a wide range of computational biology problems, including gene regulation, protein-protein interactions, and drug discovery. This knowledge will enable you to make significant contributions to the field of computational biology and advance our understanding of life.
Statistician
As a Statistician, you will use statistical methods to collect, analyze, and interpret data. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in data. You will learn how to apply these models to a wide range of statistical problems, including hypothesis testing, regression analysis, and Bayesian inference. This knowledge will empower you to make informed and data-driven decisions in a variety of fields, including finance, healthcare, and public policy.
Software Engineer
As a Software Engineer, you will design, develop, and maintain software applications. This course will provide you with a strong foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with uncertainty in software systems. You will learn how to apply these models to a wide range of software engineering problems, including natural language processing, computer vision, and robotics. This knowledge will enable you to build and deploy robust and reliable software applications, making you a valuable asset to any software development team.
Data Analyst
As a Data Analyst, you will use data to solve business problems. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in data. You will learn how to apply these models to a wide range of data analysis problems, including customer segmentation, fraud detection, and social network analysis. This knowledge will enable you to extract valuable insights from data and make informed decisions, giving you a competitive edge in the data-driven business world.
Operations Research Analyst
As an Operations Research Analyst, you will use mathematical and statistical models to solve complex business problems. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in data. You will learn how to apply these models to a wide range of operations research problems, including supply chain management, scheduling, and resource allocation. This knowledge will enable you to make informed and data-driven decisions, helping organizations to improve their efficiency and profitability.
Financial Analyst
As a Financial Analyst, you will use financial data to make investment recommendations and financial planning decisions. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with uncertainty in financial data. You will learn how to apply these models to a wide range of financial analysis problems, including risk assessment, portfolio optimization, and credit scoring. This knowledge will enable you to make informed and data-driven decisions, helping individuals and organizations to achieve their financial goals.
Risk Manager
As a Risk Manager, you will identify, assess, and manage risks to an organization. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with uncertainty in risk management. You will learn how to apply these models to a wide range of risk management problems, including operational risk, financial risk, and regulatory compliance. This knowledge will enable you to make informed and data-driven decisions, helping organizations to mitigate their risks and protect their assets.
Actuary
As an Actuary, you will use mathematical and statistical methods to assess and manage financial risks. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with uncertainty in financial data. You will learn how to apply these models to a wide range of actuarial problems, including life insurance, health insurance, and pension plans. This knowledge will enable you to make informed and data-driven decisions, helping individuals and organizations to manage their financial risks.
Market Researcher
As a Market Researcher, you will collect, analyze, and interpret data to understand consumer behavior and market trends. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in market research data. You will learn how to apply these models to a wide range of market research problems, including customer segmentation, product development, and advertising effectiveness. This knowledge will enable you to make informed and data-driven decisions, helping organizations to better understand their customers and develop successful marketing strategies.
Epidemiologist
As an Epidemiologist, you will study the distribution and determinants of health-related states or events in specified populations. This course will provide you with a solid foundation in probabilistic graphical models, which are a powerful tool for representing and reasoning with complex dependencies in epidemiological data. You will learn how to apply these models to a wide range of epidemiological problems, including disease surveillance, outbreak investigation, and risk factor identification. This knowledge will enable you to make informed and data-driven decisions, helping to improve public health and prevent disease.

Reading list

We've selected seven 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 Probabilistic Graphical Models 3: Learning.
Provides a comprehensive overview of probabilistic graphical models, covering both the theoretical foundations and practical applications. It valuable resource for students and researchers in machine learning, statistics, and computer science.
Provides a probabilistic perspective on machine learning, covering topics such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for students and researchers in machine learning and related fields.
Provides a comprehensive overview of Gaussian processes, a powerful non-parametric machine learning model. It valuable resource for students and researchers in machine learning and related fields.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It valuable resource for students and researchers in machine learning, statistics, and computer science.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for students and researchers in machine learning, statistics, and computer science.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It valuable resource for students and researchers in machine learning, statistics, and computer science.
Provides a comprehensive overview of machine learning for signal processing. It valuable resource for students and researchers in machine learning, signal processing, and related fields.

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