Knowledge Representation II FOL Resolution & Prolog [AIMA 4G] Chapters 8 & 9 COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. Sebastian Sardina * The slides here are those from Ron Branchman and Hector Levesque KR book and Hector Levesque’s one for Thinking as Computation book (see inside). — Wominjeka! Welcome! I acknowledge, and invite you […]
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Bayesian Networks [AIMA 4G] Chapter 13.1-13.3, 13.5 [13.2.3, 13.2.4, 13.4] Artificial Intelligence COSC1127/1125 Semester 2, 2021 Prof. Sebastian Sardina * Many slides are based on those Kate Larson and Pascal Poupart, and some based on James Harland’s ones. Thanks to all them! Wominjeka! Week 10 Bayesian Networks Prof. Sebastian Sardina What are the chances that
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Adversarial Search [AIMA G4] Chapter 6 Sections 6.1-6.3 (6.4, 15.1-15.3) COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. Sebastian Sardina * Some slides are based on slides from James Harland and those from Fahiem Bacchus. — Wominjeka! Welcome! I acknowledge, and invite you all to acknowledge, the Traditional Owners (Wurundjeri people of the Kulin Nations) of
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The End + AI @ RMIT COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. Sebastian Sardina Wominjeka! Week 12 AI @ RMIT Prof. Sebastian Sardina @ Acknowledgement I acknowledge the Traditional Owners (Woiwurrung and Boonwurrung form the Kulin nations) of the land on which we have conducted the whole Artificial Intelligence 2021 course during this semester.
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Reasoning under Uncertainty [AIMA 4G] Chapter 12 COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. Sebastian Sardina * Most of the slides are adapted from those of Kate Larson and Pascal Poupart. There are also some based on James Harland’s ones. Thanks! Wominjeka! Week 7 Probabilities Prof. Sebastian Sardina ‘Treaty’, by Yothu Yindi 1991 Something from
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Automated Planning [AIMA 4G] Chapter 11 COSC1127/1125 Artificial Intelligence Semester 2, 2021 Prof. Sebastian Sardina * Some slides based on those from Hector Geffner and James Harland. Wominjeka! Week 6 Planning Prof. Sebastian Sardina Say something about the course so far at: 6226 5651 @ menti.com — Wominjeka! Welcome! I acknowledge, and invite you all
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1. Introduction and JUnit First Order Logic 1 8/31/21 1 Learning Objectives Differentiate among various logics Express first order logic (FOL) expressions Differentiate syntax vs. semantics of FOL Assess challenges of inference 2 Agenda: First Order Logic 3 Logics Syntax of First Order Logic Semantics of FOL Inference Various Logics 4 Russell & Norvig Example:
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Review of Course/Syllabus Introduction to Machine Learning Part 1 ‹#› Learning Objectives for This Class Distinguish between learning and non-learning in AI Know when to apply neural nets Comfortable with “genetic algorithm” ‹#› By covering the basics of various techniques, we will compare how they can be applied. We will restrict this to two major
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F71SM STATISTICAL METHODS 2 PROBABILITY 2.1 Introduction A random experiment is an experiment which is repeatable under identical conditions, and for which, at each repetition, the outcome is uncertain but is one of a known and describable set of possible outcomes. The sample space S is the set of possible outcomes. An event A is
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