Probabilistic Graphical Models
Spring 2011
Overview A graphical model is a probabilistic model, where the conditional dependencies between the random variables is specified via a graph. Graphical models provide a flexible framework for modeling large collection of variables with complex interactions, as evidenced by their wide domain of application, including for example machine learning, computer vision, speech and computational biology. This course will provide a comprehensive survey of learning and inference methods in graphical models, including variational methods, primal-dual methods and sampling techniques. |
General information Lecture: M-W-F 1:30-2:20pm Grading: exercises (50 %) + exam (50 %) Book: Probabilistic graphical models: principles and techniques. Daphne Koller and Nir Friedman - MIT Press (2009) EXAM: Friday June 10 at 10am. Will last for approximately 2.5h. |
Syllabus
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Schedule
Lecture | Date | Topic | Slides |
Instructor | Readings | Assignments |
1 | March 28 | Introduction | lecture1.pdf | Tamir | Chapter 2 |
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2 | March 30 | Bayesian Networks I | lecture2.pdf | Tamir | Chapter 3 |
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3 | April 1 | Bayesian Networks II | lecture3.pdf | Tamir | Chapter 3 |
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4 | April 4 | Undirected Graphical Models I
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lecture4.pdf | Raquel | Chapter 4 |
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5 | April 6 | NO CLASS
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- | - | - |
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6 | April 8 | Undirected Graphical Models II
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lecture5.pdf | Raquel | Chapter 4 |
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7 | April 11 | Chordal Graphs, CRFs
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lecture6.pdf | Raquel | Chapter 4 |
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8 | April 13 | Exponential Family
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lecture7.pdf | Tamir | Chapter 8 | ex1 corrected typo! due April 20 at 1:30pm |
9 | April 15 | NO CLASS: Snowbird
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- | - | - |
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10 | April 18 | Exact inference I: VE
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lecture8.pdf | Raquel | Chapter 9 |
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11 | April 20 | Exact inference II: VE
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lecture9.pdf | Raquel | Chapter 9 |
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12 | April 22 | Exact inference III: Conditioning
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lecture10.pdf | Raquel | Chapter 9 |
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13 | April 25 | Exact inference IV: Clique Trees
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lecture11.pdf | Raquel | Chapter 10 |
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14 | April 27 | Exact inference V: Message passing I
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lecture12.pdf | Raquel | Chapter 10 |
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15 | April 29 | Exact inference VI: Message passing II
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lecture13.pdf | Raquel | Chapter 10 |
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16 | May 2 | Inference via optimization I
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lecture14.pdf | Raquel | Chapter 11 |
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17 | May 4 | Inference via optimization II
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lecture15.pdf | Tamir | Chapter 11 |
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18 | May 6 | Inference via optimization III
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lecture16.pdf | Tamir | Chapter 11 | ex2 due May 13 at 1:30pm |
19 | May 9 | Inference via sampling I
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lecture17.pdf | Tamir | Chapter 12 |
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20 | May 11 | Inference via sampling II
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lecture18.pdf | Tamir | Chapter 12 |
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21 | May 13 | Inference via sampling III
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lecture19.pdf | Tamir | Chapter 12 |
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22 | May 16 | MAP estimation I
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lecture20.pdf | Tamir | Chapter 13 |
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23 | May 18 | MAP estimation II
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lecture21.pdf | Tamir | Chapter 13 |
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24 | May 20 | MAP estimation III
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lecture22.pdf | Tamir | Chapter 13 |
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25 | May 23 | Introduction to learning
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lecture23.pdf | Raquel | Chapter 16 | ex3 due May 30 at 1:30pm |
26 | May 25 | Learning I
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lecture24.pdf | Tamir | Notes |
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27 | May 25 | Learning II
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lecture27.pdf | Tamir | Notes |
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28 | May 27 | Learning III
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lecture27.pdf | Tamir | Notes |
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29 | May 30 | NO CLASS
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30 | June 1 | Learning IV
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lecture28.pdf | Tamir | Notes | ex4 due June 7 at 1:30pm |
31 | June 3 | Learning V
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lecture29.pdf | Tamir | Notes |
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