20th August 2019 Probability theory https://bitbucket.org/mfumagal/ statistical_inference Matteo Fumagalli Intended Learning Outcomes By the end of this session, you will be able to: Describe the principles of set theory and set operations Illustrate the axiomatic foundations of probability theory and appropriate counting methods Identify dependence and indepedence of events Show the utility of distribution functions […]
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20th August 2019 Introduction to machine learning and neural networks applied to biological data https://bitbucket.org/mfumagal/ statistical_inference Matteo Fumagalli Intended Learning Outcomes By the end of this session, you will be able to: Describe the three key components of a classifier: score function, loss function, optimisation Identify the elements of a neural networks, including neurons and
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Molecular Ecology Resources (2010) 10, 821–830 doi: 10.1111/j.1755-0998.2010.02873.x METHODOLOGICAL ADVANCES – INFERENCE OF SPATIAL STRUCTURE Quantifying population structure using the F-model OSCAR E. GAGGIOTTI* and MATTHIEU FOLL† *Laboratoire d’Ecologie Alpine, UMR CNRS 5553, Universite ́ Joseph Fourier, BP 53, 38041 GRENOBLE, France, †CMPG, Institute of Ecology and Evolution, University of Berne, 3012 Berne, Switzerland Abstract
Rule Based Systems and Expert Systems Consider the statement “Everyone in LPH who is not a manager and does not like poetry is a cook” from A1 Chapter 8 in Clause Form: ~in(X,lph) v manager (X) v likes(X,poetry) v cook(X) Limitations of Clause Form Rules Logically this is equivalent to the original
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Reasoning With Uncertainty “So far as the laws of mathematics refer to reality they are not certain. And so far as they are certain, they do not refer to reality.” – Albert Einstein Text Chapter 5 and 9: 9.2 only, to the end of 9.2.2 Logical Implication Logical Implication First order logic gives
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Introduction to Natural Language Understanding One of the earliest efforts in AI was to develop systems that could understand natural language; e.g. the Eliza system (incorporating structured and opportunistic approaches) Well-respected researchers stated that machines would be able to understand natural language within 15 years Sections 6.3, 7.1,7.2, Chapter 15 (omitting 15.3,15.4)
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Search in AI and “hope for the best,” or else develop Chapters 3,4,&6 in your Text vicinity. If you follow this later course, Now What Do I do? How Can an agent decide what to do? In our initial discussion, I’m going to use the term AGENT Think back to our water-jugs problem
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Prolog The syntax will look very familiar now that you’ve seen clause form, but it embodies most of what you can say in FOL. many books and articles on Prolog, if you’re interested in doing more than you see here – Also Comp 3440 Prolog is available free for most platforms – sbprolog
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Undergraduate Texts in Mathematics Editors S. Axler F.W. Gehring K.A. Ribet Saber Elaydi An Introduction to Difference Equations Third Edition Saber Elaydi Department of Mathematics Trinity University San Antonio, Texas 78212 USA Editorial Board S. Axler Mathematics Department San Francisco State University San Francisco, CA 94132 USA F.W. Gehring Mathematics Department East Hall University of
Assignment: Reinforcement Learning and Deep Learning Contents Part 1: Q-learning (Snake) Provided Snake Environment Q-learning Agent Debug Convenience Part 2: Deep Learning (MNIST Fashion) Background Neural Network Implementation Details Testing What to Report Warning/Hints Deliverables Report Checklist Part 1: Q-learning (Snake) Snake is a famous video game originated in the 1976 arcade game Blockade. The