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4434Combustion Kinetics
http://www.merlot.org/merlot/viewMaterial.htm?id=282434
A set (basically 4 1.5 hour lectures) of introductory lectures on combustion kinetics.The current set of lectures are: * Introductory Thermodynamics * Introductory Kinetics (including autocatalytic effects) * Simple Mechanisms * Larger Hydrocarbons and their combustionThis set of lectures (login, for example as guest, may be required into the Moodle system) is being expanded with supplementary material and quizzes through the Moodle course management environment.Computer Aided Synthesis Design
http://www.merlot.org/merlot/viewMaterial.htm?id=85180
One of the main emphases of this course will be to introduce the use of symbolic (i.e. non-numeric) methods in computers and chemistry. This course gives an overview of the Computer Aided Organic Synthesis (CAOS) systems that are presently available. The first part of the course will be an introcution to the significant systems. The second part of the course uses the framework of artificial intellegence to introduce some of the methods used in these systems.This is all the course materials and slides for the summer semester 95 lecture on Methods of Computer Aided Synthesis Design. One of the main emphases of this course will be to introduce the use of symbolic (i.e. non-numeric) methods in computers and chemistry. This course gives an overview of the Computer Aided Organic Synthesis (CAOS) systems that are presently available. The first part of the course will be an introcution to the significant systems. The second part of the course uses the framework of artificial intellegence to introduce some of the methods used in these systems.Knowledge Based Engineering: Intro to AI
http://www.merlot.org/merlot/viewMaterial.htm?id=85182
This course is basically an introduction to artificial intelligence and expert systems. The general theme of the course is methods of search. The expert system shell CLIPS is introduced not only to give an example of an expert system shell, but also as a programming medium (for the exercises) for illustrating the search methods introduced in traditional AI textbooks (i.e. A, A*, Decision Trees, AND/OR graphs). Depending on time, the numeric search techniques will also be introduced, Gradient, Monte-Carlo and Genetic Algorithms.This is all the course materials and slides for a lecture on Knowledge Based Engineering. This course is basically an introduction to artificial intelligence and expert systems. The general theme of the course is methods of search. The expert system shell CLIPS is introduced not only to give an example of an expert system shell, but also as a programming medium (for the exercises) for illustrating the search methods introduced in traditional AI textbooks (i.e. A, A*, Decision Trees, AND/OR graphs). Depending on time, the numeric search techniques will also be introduced, Gradient, Monte-Carlo and Genetic Algorithms. The software system PRODIGY is introduced as a medium for looking at planning problems. In addition, the software system OTTER will be used to illustrate resolution. As one sees the course gives emphasis on the practical exercises within the concepts introduced. The software systems are used to enable the calculation of non-trivial examples and to see, by example, the complexity the problems can take.PROLOG: Logic Programming
http://www.merlot.org/merlot/viewMaterial.htm?id=85184
This is an introduction to logic programming, specifically PROLOG. The introduction to PROLOG will follow the book of Clocksin and Mellish: Programming in Prolog. The remainder of the course will be on applications in AI and special topic talks on logic programming (such as CLP). This is an introduction to logic programming, specifically PROLOG. The introduction to PROLOG will follow the book of Clocksin and Mellish: Programming in Prolog. The remainder of the course will be on applications in AI and special topic talks on logic programming (such as CLP). There will be no final exam, but: * Exercises: With each lecture a corresponding set of execises involving the concepts of the lecture will be given. These assignments will be due (and discussed) in the next lecture (60%). * Special Topic Each student will be required to give a special topic talk and a (approx. 5 page) paper involving some area of logic programming (20%). * Project Several exercises will be combined to form a larger 'project' (20%)Machine Learning Semester Course
http://www.merlot.org/merlot/viewMaterial.htm?id=85178
The purpose of the course is to introduce a class of learning methods having more to do with (what I will call) predicate descriptions of the training examples. Neural nets are not covered because this is another course in this series and genetic algorithms are not covered (extensively) because discussion of this should be under optimization and search techniques. ID3 is used as the introduction to learning methods in general and specific problems effecting this and other learning methods (i.e. missing data, pruning) will be discussed within the framework of this method. Other methods will also be introduced: Mikalskis's AQ and Conceptual Clustering and the class of incremental concept formation algorithms, EPAM, UNIMEM and COBWEB (and maybe CLASSIT).