In this series of posts, I will share some experiences from the on-line learning platform coursera.org
Today: Probabilistic Graphical Models by DaphneKoller from Stanford University.
1. What is the course about?
Probabilistic Graphical Models (PGMs) is a framework of mathematical models. It spans methods such as Bayesian networks, Markov random fields, coding theory, and discrete data structures in used in modern computer science. The goal is to efficiently encode and manipulate high-dimensional probability distributions, often involving thousands of variables. These methods have been used in a wide range of applications, like: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more.
The course includes video lecturing, discussion groups, test quizzes, final exam, and programming assignments. The programming assignments are needed only for students who wish to follow the 'advanced track'.
Level: graduate.
2. Who should use it?
People with very strong background in technical disciplines like mathematics, computer science or engineering. Students who are interested in understanding and building large-scale data analysis applications in complex, multidimensional environments.
3. Who should NOT use it?
Students with limited experience in algorithms and/or without strong technical background (including high-level math skills, not just programming).
4. What is the best thing about this course?
Excellent lecturer who explains clearly pretty complex notions through carefully selected examples. Presented are many links to real-world applications. Unlike other courses on coursera, this one is not 'watered down'.
5. What is the worst thing about this course?
The course claims that 'the average Stanford student needs between 15 and 20 hours of work per week to complete the course'. This might be true for some students. In my personal experience this statement was exaggerated, but nevertheless I needed about 7h/week (60% of which was spend on the programming assignments for the advanced track). For a working professional this might be too much.
Today: Probabilistic Graphical Models by DaphneKoller from Stanford University.
1. What is the course about?
Probabilistic Graphical Models (PGMs) is a framework of mathematical models. It spans methods such as Bayesian networks, Markov random fields, coding theory, and discrete data structures in used in modern computer science. The goal is to efficiently encode and manipulate high-dimensional probability distributions, often involving thousands of variables. These methods have been used in a wide range of applications, like: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more.
The course includes video lecturing, discussion groups, test quizzes, final exam, and programming assignments. The programming assignments are needed only for students who wish to follow the 'advanced track'.
Level: graduate.
2. Who should use it?
People with very strong background in technical disciplines like mathematics, computer science or engineering. Students who are interested in understanding and building large-scale data analysis applications in complex, multidimensional environments.
3. Who should NOT use it?
Students with limited experience in algorithms and/or without strong technical background (including high-level math skills, not just programming).
4. What is the best thing about this course?
Excellent lecturer who explains clearly pretty complex notions through carefully selected examples. Presented are many links to real-world applications. Unlike other courses on coursera, this one is not 'watered down'.
5. What is the worst thing about this course?
The course claims that 'the average Stanford student needs between 15 and 20 hours of work per week to complete the course'. This might be true for some students. In my personal experience this statement was exaggerated, but nevertheless I needed about 7h/week (60% of which was spend on the programming assignments for the advanced track). For a working professional this might be too much.
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