Games and Adversarial Search AIMA 5.1-5.2 CMPSC 442 Week 4, Meeting 11, Two Segments Outline 1. Games as Search 2. Adversarial Search: Minimax Outline, Wk 4, Mtg 11 2 Games and Adversarial Search AIMA 5.1-5.3 CMPSC 442 Week 4, Meeting 11, Segment 1 of 2: Games as Search Games as Search ● Carryover from previous […]
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Bayesian Networks AIMA 13.1-13.2 CMPSC 442 Week 8, Meeting 23, 3 Segments Outline ● Syntax and Semantics of Bayesian Networks ● Conditional Probability Tables in a Bayesian Network ● More on Bayesian Networks 2Outline, Wk 8, Mtg 23 Bayesian Networks AIMA 13.1-13.2 CMPSC 442 Week 8, Meeting 23, Segment 1 of 3: Syntax and Semantics
Introduction to Machine Learning: Learning from Examples AIMA 19.1 – 19.4 CMPSC 442 Week 11, Meeting 33, Three Segments Outline ● Supervised Learning ● Decision Trees ● Model Selection and Optimization 2Outline, Wk 11, Mtg 33 Introduction to Machine Learning: Learning from Examples AIMA 19.1 – 19.4 CMPSC 442 Week 11, Meeting 33, Segment 1
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Formalizing Hidden Markov Models AIMA 14.3; Jurafsky & Martin, Draft 3rd ed., Appendix A CMPSC 442 Week 9, Meeting 26, Three Segments Outline ● Markov Chains for Language Modeling ● Formalizing a Hidden Markov Model ● Computing Likelihood of a sequence: Forward Algorithm 2Outline, Wk 9, Mtg 25 Formalizing Hidden Markov Models AIMA 14.3; Jurafsky
Lectures 6 and 7: Simulation Computational Finance 1 A model is an approximate mathematical description of real-world behaviors E.g. model describing gravity • 𝐹𝐹 = 𝐺𝐺 𝑚𝑚1𝑚𝑚2 𝑑𝑑2 and 𝐹𝐹 = 𝑚𝑚𝑚𝑚 imply 𝑚𝑚 = 𝐺𝐺 𝑚𝑚2 𝑑𝑑2 • The acceleration of an object towards another object (e.g. Earth) is proportional to the mass of
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Applying HMMs to Part-of-Speech (POS) Tagging AIMA 14.3; Jurafsky & Martin, Draft 3rd ed., Appendix A CMPSC 442 Week 9, Meeting 27, Three Segments Outline ● Decoding: the Viterbi Algorithm ● Part-of-Speech (POS) Problem ● Viterbi POS Tagger 2Outline, Wk 9, Mtg 25 Applying HMMs to Part-of-Speech (POS) Tagging AIMA 14.3; Jurafsky & Martin, Draft
Apha-Beta Pruning AIMA 5.2.3 CMPSC 442 Week 4, Meeting 12, Two Segments Outline ● Defining Alpha and Beta ● Alpha Beta Pruning Algorithm 2Outline, Wk 4, Mtg 12 Apha-Beta Pruning AIMA 5.2.3 CMPSC 442 Week 4, Meeting 12, Segment 1 of 2: Defining Alpha and Beta Motivation for Alpha-Beta Pruning ● Patrick Winston, computer scientist
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Problem 1. (30 Marks) Consider the following three methods of solving a particular problem (input size n): 1. You divide the problem into seven subproblems, each 17 the size of the original problem, solve each recursively, then combine the results with a cost 12n + 3 where n is the original problem size. 2. You
Lecture 7: Optimization Computational Finance 1 Functions 2 3 Functions: Concept 𝑓𝑓 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 • Execute some code using the inputs • Set the output variable(s) inputs output(s) • When calling a function, the caller doesn’t need to know what happens inside the function. • We just need to know how to call it and what
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Uninformed Search AIMA 3.3-3.4; Appendix A.1 CMPSC 442 Week 3, Meeting 7, Four Segments Outline: Uninformed Search 1. Evaluating Search Algorithms 2. Tree Search versus Graph Search 3. Four Algorithms (BFS, UCS aka BF, DFS, Iterative Deepening) 4. Comparison of Search Algorithms Outline, Wk 3, Mtg 7 2 Uninformed Search AIMA 3.3-3.4; Appendix A.1 CMPSC
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