1. Introduction and JUnit Fuzzy Logic 1 1 Fuzzy Expert Systems: Learning Objectives Exploit fuzziness in data Create fuzzy rules Apply fuzzy rules Adapted from Savich 2 What Can We Expect Machines to Learn? 3 Much of what needs to be learned carries uncertainty. (e.g., X is a dining room because it contains a table […]
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1. Introduction and JUnit Rule-based Systems 1 Despite their success, technologies like neural nets and SVM’s are knowledge free—”dumb” in many ways. To illustrate this, suppose that your child answered “105” when asked “how much is 98 + 9?” But when you ask “why”, he answers … because I just happen to know that 97
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CSCI 4061 Introduction to Operating Systems Instructor: 2 Outline Threads Thread Definition Thread Usage Kernel vs. User Threads Thread Operations 3 Processes Process is a program in execution A process has a single “thread” of activity Single program counter, stack Code, data, heap, files, signals
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Turing Machines COMP1600 / COMP6260 Australian National University Semester 2, 2020 1/45 (1912–1954) 2/45 2/45 Turing’s Scientific contributions 1936 introduces Turing machines and the study of computability 1950. introduces the Turing test that turns AI into a concrete research problem current today: in June 2014, , a computer programme convinced 33% of judges that it
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Deterministic Finite Automata COMP1600 / COMP6260 Australian National University Semester 2, 2020 The Story So Far … Logic. language and proofs to speak about systems precisely useful to express properties and do proofs Functional Programs establish properties of functional programs main tool: (structural) induction Imperative Programs. again: focus on properties of programs main tool: Hoare
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Hoare Logic: Partial Correctness COMP1600 / COMP6260 Australian National University Semester 2, 2020 Programming Paradigms Functional. (Haskell, SML, OCaml, . . . ) main paradigm: functions that don’t rely on state main ingredient: recursion Imperative. (C, Java, Algol, (Visual) Basic, . . . ) main paradigm: operations that do manipulate state. main ingredient: loops 1/51
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COMP90038 Algorithms and Complexity Lecture 12: More Divide-and-Conquer Algorithms (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Divide and Conquer In the last lecture we studied the archetypal divide- and-conquer sorting algorithms: mergesort and quicksort. We also
CSCI 4061 Introduction to Operating Systems Instructor: General Information Lectures: Sec 1: Tu/Th 8.15 am-9.30 am Sec 10: Tu/Th 1pm-2.15pm Course Modality: Lecture, labs in-person (subject to change) Live streamed and recorded lectures, labs All office hours via zoom Course material and information: Via Canvas page
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COMP90038 Algorithms and Complexity Lecture 2: Review of Basic Concepts (with thanks to Harald Søndergaard) DMD 8.17 (Level 8, Doug McDonell Bldg) http://people.eng.unimelb.edu.au/tobym @tobycmurray 2 Copyright University of Melbourne 2016, provided under Creative Commons Attribution License Approaching a problem Can we cover this board with 31 • tiles of the following form? This is the
Markov Decision Processes [AIMA 4G] Chapter 15.1-15.3, 16.1-16.2 Artificial Intelligence COSC1127/1125 Semester 2, 2021 Prof. * Many slides are based on those of and , and . Thanks! Today… Some news… Project 2 marking is done! 1st contest feedback this week! Submit as per instruction. Check #170 Course THE happening next week Monday 9am –
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