关于此课程： 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 first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.
教学方： Daphne Koller, Professor
School of Engineering
| 基本信息 | 课程 1（共 3 门，Probabilistic Graphical Models Specialization ） |
| 级别 | Advanced |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Semantics & Factorization
- 视频: Reasoning Patterns
- 视频: Flow of Probabilistic Influence
- 视频: Conditional Independence
- 视频: Independencies in Bayesian Networks
- 视频: Naive Bayes
- 视频: Application - Medical Diagnosis
- 视频: Knowledge Engineering Example - SAMIAM
- Reading: Setting Up Your Programming Assignment Environment
- Reading: Installing Octave/MATLAB on Windows
- Reading: Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)
- Reading: Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)
- Reading: Installing Octave/MATLAB on GNU/Linux
- Reading: More Octave/MATLAB resources
- 视频: Basic Operations
- 视频: Moving Data Around
- 视频: Computing On Data
- 视频: Plotting Data
- 视频: Control Statements: for, while, if statements
- 视频: Vectorization
- 视频: Working on and Submitting Programming Exercises
- 视频: Overview of Template Models
- 视频: Temporal Models - DBNs
- 视频: Temporal Models - HMMs
- 视频: Plate Models
- 视频: Overview: Structured CPDs
- 视频: Tree-Structured CPDs
- 视频: Independence of Causal Influence
- 视频: Continuous Variables
- 视频: Pairwise Markov Networks
- 视频: General Gibbs Distribution
- 视频: Conditional Random Fields
- 视频: Independencies in Markov Networks
- 视频: I-maps and perfect maps
- 视频: Log-Linear Models
- 视频: Shared Features in Log-Linear Models
- 视频: Maximum Expected Utility
- 视频: Utility Functions
- 视频: Value of Perfect Information
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This is not an easy course, so beware. The instruction is solid but you still need to reason through a lot on your own, and especially if you choose to complete the Honors programming section (which I highly recommend to prove to yourself that you really understand what you have learned and can apply it), you really need to plan on allocating sufficient number of hours to work through the programming assignments. You'll likely need to re-watch several of the video segments several times for it to really sink in, as well as referencing the Discussion Forum when you are stuck and need inspiration. Once you do complete this course (after many hours of work and thought) you will enjoy a deep sense of accomplishment, will look and think about decision-making in a fresh new way, and have learned many very useful skills.
The course helps me understand what a probabilistic graphical model is and how and why it works. One aspect I like the most about the course is the programming assignments. Those PA really make a lot of concepts clearer although sometimes you need to think carefully when the instruction is hard to follow. I think there should be more test case and expected results so that students know what is asked and to evaluate their own code. The instructor is generally clear but sometimes she goes too fast on certain concept. The course is hard but if you gives in time and effort you can complete it.
Great instructor and information.
JW
very good, practical.