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1:21:38

Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)

Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... Description Bayesian Netwo Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autu... 0:00 Introduction 0:22 Announcements 2:11 Pac-Man competition 4:54 Review: definition 6:06 Review: object tracking 7:50 Course plan 9:09 Review: probability Random variables: sunshine S € (0,1), rain R € {0,1} 15:50 Challenges Modeling: How to specify a joint distribution P(X1,...,x.) compactly? Bayesian networks (factor graphs to specify joint distributions) 28:48 Probabilistic inference (alarm) 30:45 Explaining away 37:02 Consistency of sub-Bayesian networks 45:34 Medical diagnosis 46:16 Summary so far 47:27 Roadmap 47:42 Probabilistic programs 50:40 Probabilistic program: example 52:32 Probabilistic inference: example Query: what are possible trajectories given evidence 55:28 Application: language modeling 56:15 Application: object tracking 57:58 Application: multiple object tracking 59:09 Application: document classification

5 months ago 82 views

1:23:44

Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019)

Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... Bayesian Networks 3 - Maximum Likelihood | Stanford CS221: AI (Autumn 2019) Stanford Online 215 Likes 15,346 Views 2020 Dec 17 For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Zlc5Iu Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autu... 0:00 Introduction 0:18 Announcements 2:00 Review: Bayesian network 2:57 Review: probabilistic inference 4:13 Where do parameters come from? 4:37 Roadmap 5:02 Learning task 6:29 Example: one variable 12:06 Example: v-structure 14:48 Example: inverted-v structure 20:28 Parameter sharing 21:35 Example: Naive Bayes 26:05 Example: HMMS 33:40 General case: learning algorithm 36:14 Maximum likelihood 41:05 Scenario 2 42:45 Regularization: Laplace smoothing 44:14 Example: two variables 49:09 Motivation 49:49 Maximum marginal likelihood 52:59 Expectation Maximization (EM)

5 months ago 41 views

1:11:48

Bayesian Networks 2 - Forward-Backward | Stanford CS221: AI (Autumn 2019)

Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... 0:00 Introduction 0:23 Review: Bayesian network 1:58 Review: probabilistic inference 8:48 Hidden Markov model inference 31:12 Lattice representation 34:16 Summary 35:38 Hidden Markov models 41:04 Review: beam search 44:49 Step 1: propose 46:04 weight 50:13 Step 3: resample 56:01 Application: object tracking 57:24 Particle filtering demo 59:36 Roadmap 59:39 Gibbs sampling

5 months ago 26 views

Factor Graphs 1 - Constraint Satisfaction Problems | Stanford CS221: AI (Autumn 2019)

1:21:16

1:17:50

Factor Graphs 2 - Conditional Independence | Stanford CS221: Artificial Intelligence (Autumn 2019)

For more information about Stanford’s Artificial Intelligence professional and graduate programs. Topics: Beam search, local search, conditional independence, variable elimination Reid Pryzant, PhD Candidate & Head Course Assistant 0:00 Introduction 0:34 Review: definition 1:39 Factor graph (example) 7:01 Review: backtracking search 8:42 Example: object tracking 8:59 Modeling object tracking 11:58 [Background] [Documentation] // Object tracking example 22:06 Beam search properties 36:12 Motivation 38:47 Non-independence 44:22 Conditioning: example 48:13 Markov blanket 50:20 Using conditional independence 57:04 Conditioning versus elimination

5 months ago 58 views

Game Playing 1 - Minimax, Alpha-beta Pruning _ Stanford CS221_ AI (Autumn 2019)