Chapter Six Discrete Probability Distributions六章离散概率分布
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Chapter Six Discrete Probability Distributions六章离散概率分布
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3秒自动关闭窗口Lecture Schedule
Lectures are held on Mondays and Wednesdays from 12:00-1:20 pm in DH 1212.
Anouncements
Monday,Jan 12
Lecture 1 (Eric): Introduction to GM
Wenbo Liu,Venkata KrishnaPillutla
Required (no reading summary):
Jordan Textbook, Ch 2 (will distribute shortly)
Koller and Friedman Textbook,
Wednesday,Jan 14
Lecture 2 (Eric): Directed GMs: Bayesian Networks
Yi Cheng,Cong Lu
Required (please bring your reading summary):
Jordan Textbook, Ch 2 (Section 2.2 - end)
Koller and Friedman Textbook,
Monday,Jan 19
No Lecture due to MLK day.
Wednesday,Jan 21
Lecture 3 (Eric): Representation of Undirected GM
Karima Ma,Manu Reddy Nannuri
Required (please bring your reading summary):
A. Fischer and C. Igel,
S. Gould et al.,
C. K. Chow and C. N. Liu,
B. A. Cipra,
Monday,Jan 26
Lecture 4 (Eric):
Maximum likelihood parameter estimation
Bayesian inference
Regularization
How Jing,Xiaoqiu Huang
Required (please bring your reading summary):
Jordan Textbook, Ch. 8
T. Park and G. Casella,
R. Tibshirani,
Tuesday,Jan 27
Lecture 5 (Eric):
Generalized linear model and sufficient statistics
Learning fully observed directed GMs
Uttara Ananthakrishnan,Lujie(Karen) Chen,Mallory Nobles
Required (please bring your reading summary):
Jordan Textbook, Ch. 9, Sec. 9.1-9.2
Koller and Friedman Textbook, Ch. 17
Assignment 1 is out. Due on Feb 13 at 12 noon
Monday,Feb 2
Lecture 6 (Yaoliang): Learning fully observed undirected GM.
Satwik Kottur,William Herlands,Maria De Arteaga
Required (please bring your reading summary):
Jordan Textbook, Ch. 9, Sec. 9.3-9.5
J. Lee and T. Hastie,
H. Wallach,
Wednesday,Feb 4
Lecture 7 (Seunghak): Exact Inference:
Elimination and Message Passing
The Sum Product Algorithm
Vipul Singh,Xiao Liu,Vrushali Fangal
Required (please bring your reading summary):
Jordan Textbook, Ch. 3
Jordan Textbook, Ch. 4
Koller and Friedman Textbook, Ch. 9
Koller and Friedman Textbook, Ch. 10
Monday,Feb 9
Lecture 8 (Eric): Learning Partially observed models:
The EM algorithm
Aurick Qiao,Hao Zhang,Bing Liu
Required (please bring your reading summary):
Jordan Textbook, Ch. 10
A. Dempster et al.,
Wednesday,Feb 11
Lecture 9 (Bin Zhao): Case Study with Popular GMs I:
HMM vs CRF
CRFs for computer vision
Emmanouil Antonios Platanios,Mariya Toneva,Jeya Balaji Balasubramanian
Required (please bring your reading summary):
J. Lafferty et al.,
F. Sha and F. Pereira,
S. Kumar and M. Hebert.
Xuming He, Richard Zemel, and Miguel Carreira-Perpinan.
Monday,Feb 16
Lecture 10 (Eric):
Multivariate Gaussian
Gene network
Yan Xia,Dexter Min Hyung Lee
Required (please bring your reading summary):
J. Friedman et al.,
M. Kolar et al.,
A. Dempster,
N. Meinshausen and P. Buhlmann,
M. Kolar et al.,
Wednesday,Feb 18
Lecture 11 (Eric):
Factor Analysis
State Space Models
Topic/Trend Tracking
Yilin He,Udbhav Prasad
Required (please bring your reading summary):
Jordan Textbook, Ch. 14
Jordan Textbook, Ch. 15
G. Welch and G. Bishop,
Assignment 2 is out
Monday,Feb 23
Lecture 12 (Eric): Variational inference I:
Loopy Belief Propagation
Evan Shapiro,Eric Lei,Fattaneh Jabbari
Required (please bring your reading summary):
Yedidia et al.,
Wainwright and Jordan, , Sec. 4.1
Murphy et al.,
Wednesday,Feb 25
Lecture 13 (Willie): Variational inference II:
Mean field
Yuntian Deng,Zhiting Hu,Ronghuo Zheng
Required (please bring your reading summary):
Xing et al.,
Wainwright and Jordan, , Sec. 5.1-5.3
Monday,Mar 2
Lecture 14 (Eric): Theory of variational inference
Abhinav Maurya,Joey Robinson,Qian Wan
Required (please bring your reading summary):
M. Wainwright and M. Jordan,
M. Wainwright and M. Jordan, , Sec. 3 and Sec. 4
Assignment 2 due
Wednesday,Mar 4
Lecture 15 (Eric): Case study with Popular GMs II:
Topic models
Xinyu Miao,Yun Ni,Linglin Huang
Required (please bring your reading summary):
D. Blei et al.,
T. Griffiths and M. Steyvers,
A. Ahmed and E. P. Xing, .
Monday,Mar 9
No Lecture due to CMU spring break.
Wednesday,Mar 11
No Lecture due to CMU spring break.
Monday,Mar 16
Lecture 16 (Eric):
Monte Carlo: Basic concept
Importance sampling
Particle filtering
Sequential Monte Carlo
Jonathan deWerd,Jay Yoon Lee,Aaron Li
, Ch. 29.1 - 29.3
Jordan Textbook, Ch. 21
D. Mackay,
Wednesday,Mar 18
Lecture 17 (Andrew): MCMC
Pitfalls of Monte Carlo
Markov chains
Metropolis Hastings
Gibbs Sampling
Slice Sampling
Hamiltonian Monte Carlo
Simulated Annealing and Parallel Tempering
Heran Lin,Bin Deng,Yun Huang
, pp. 20-55
, Ch. 29, 30.
C. Bishop, Pattern Recognition and Machine Learning (PRML), Ch. 11
R. Neal, , Annals of Statistics, 2003
C. Geyer, , Statistical Science 7(4): 473-492. 1992.
J. Geweke, Getting it right: joint distribution tests of posterior simulators, JASA 99(467): 799-804, 2004.
Monday,Mar 23
Lecture 18 (Avinava): Dirichlet Process and Dirichlet Process Mixtures
Ji Oh Yoo,Ying Zhang,Chi Liu
Y. W. Teh,
Y. W. Teh, , MLSS 2007.
Wednesday,Mar 25
Lecture 19 (Avinava): Indian Buffet Process
Rishav Das,Adam Brodie,Hemank Lamba
Thomas L. Griffiths and Zoubin Ghahramani, , JMLR 2011.
Midway report due at 4pm, Mar 26;
Assignment 3 is out
Monday,Mar 30
Lecture 20 (Andrew): Gaussian Processes
Probabilistic modelling
Bayesian model selection and nonparametric models
Linear basis models
Gaussian processes and kernels
Haohan Wang,Yuetao Xu,Jisu Kim
C. Rasmussen and C. Williams, , Preface + Ch. 2.2-2.4
, 2014, Ch. 1, 2.
C. Rasmussen and C. Williams, , Ch. 3, 5
D. MacKay, , 1998.
Wednesday,Apr 1
Lecture 21 (Andrew): Advanced Gaussian Processes
Brief review
Kernel construction and derivations
Scalable methods
Konstantin Genin,Yutong Zheng
C. Rasmussen and C. Williams, , Ch. 4, 5
J. Candela and C.E. Rasmussen, , JMLR 2005.
L&zaro-Gredilla et al., , JMLR 2011.
A. G. Wilson and R. P. Adams, , ICML 2013.
Monday,Apr 6
Lecture 22 (Yaoliang): Optimization and GMs
Sparse coding vs LDA
Yu-Xiang Wang,Su Zhou
Wednesday,Apr 8
Lecture 23 (Yaoliang): Max-margin learning of GMs
Xun Zheng,Wei Yu,Lee Gao
Monday,Apr 13
Lecture 24 (Eric): Regularized Bayesian learning of GMs
Rose C. Kanjirathinkal,Yiming Gu
Assignment 3 due
Wednesday,Apr 15
Lecture 25 (Eric): Spectrum GMs
Guillermo Andres Cidre,Abelino Jimenez
Monday,Apr 20
Lecture 26 (Eric): Deep neural networks and GMs
Harry Gifford,Pradeep Karuturi
Wednesday,Apr 22
Lecture 27 (Seunghak): Case study with popular GM III:
TBD (e.g., structured regression for genome association)
Elizabeth Silver,Hyun Ah Song
Monday,Apr 27
Lecture 28 (Avinava): Big Learning:
Distributed MCMC
Hakim Sidahmed,Aman Gupta
Wednesday,Apr 29
Lecture 29 (Yaoliang): Big Learning:
Distributed Optimization (e.g., GGM or SIOL)
Taiyuan Zhang,Vrushali Fangal
& 2009 Eric Xing @ School of Computer Science, Carnegie Mellon University503 Service Temporarily Unavailable
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