Probabilistic Graphical Models
Description
My lectures will cover the basics of graphical models, also known as Bayes(ian) (Belief) Net(work)s. We will cover the basic motivations for using probabilities to represent and reason about uncertain knowledge in machine learning, and introduce graphical models as a qualitative and quantitative specification of large joint probability distributions. We will see how many common classification, regression and clustering models can be cast in this framework. We will cover the basic algorithm (called belief propagation) for inference in graphical model structures. We will also cover the major approaches to learning models from data (parameter estimation). The course will focus on directed models and the basic algorithms, but time and student desire permitting, I will also try to give some preliminary explanations of undirected models, approximate inference and learning, structure discovery and current applications.
| Slides | |
| 0:02 | Intelligent Computers |
| 1:45 | Core vs. Probabilistic AI |
| 6:28 | The Power of Learning |
| 7:32 | Uncertainty and Artificial Intelligence (UAI) |
| 8:10 | Other Names for UAI |
| 11:05 | Applications of Probabilistic Learning |
| 12:07 | Related Areas of Study |
| 12:12 | Canonical Tasks |
| 16:29 | Representation |
| 20:20 | Using random variables to represent the world |
| 21:39 | Structure of Learning Machines |
| 28:17 | Loss Functions for Tuning Parameters |
| 28:22 | Training vs. Testing |
| 31:33 | Sampling Assumption |
| 34:53 | Generalization and Overfitting |
| 38:01 | Capacity: Complexity of Hypothesis Space |
| 41:23 | Inductive Bias |
| 43:22 | Formal Setup |
| 48:26 | General Objective Functions |
| 50:29 | Probabilistic Approach |
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Part 1
0:52:06
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Part 2
0:38:51
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Part 3
1:02:28
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Part 4
0:40:07
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Part 5
1:02:34
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Part 6
0:42:36
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Part 7
0:57:53
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Part 8
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Mr. Sam Roweis lectures are very good.
he presents the theory in an interesting and a non-boring way.
very useful
great...It open my eyes!
for any case, i would like to thank you very much.
it is very interesting and helpful.
i would like to download the videos presentations about SVM but i couldn't, because i need them in my research.
can you help me to get them.
so cool! thanks...
It is a useful lectures, My best regards to Prof. Sam
Lectures by Sam are really helpful for variety of audience doing either courses or research. His style is very very effective but some time I feel that he needs to repeat some point especially when it is really important one.
Thanks
His presentation is nice and I personally have been benefited from his lecture. Now things to me are quite easier than my previous feelings. Thanks
He is a very good lecturer and its great tof ind these things on video lectures which usually we donot get in avrsities
Sam Roweis lectures are well organized. They present the key challenges and ideas of machine learning and graphical models in a clear cut and concise manner. I love maths myself, but I believe that introductory lectures should focus on conveying new ideas in an easy to grasp way before journeying into the mathematical details -no matter how interesting these might be. In my opinion, Sam Roweis accomplish this in his lectures. I greatly appreciate that I have had the opportunity to follow these lectures online and recommend them to anyone with an interest in machine learning.
An excellent introduction. I have no real understanding of mathematics. However, the clear manner in which graphical models really help clarify the association of the learning strategies and their mathematical formulations.
Thanks professor Sam Roweis
Kevin
Holy Christ could you please use a standard video format that everyone's computer can play PLEASE?!?! Just use YouTube, jeez.