Lawrence B. Holder

Assistant Professor
Department of Computer Science and Engineering
University of Texas at Arlington

Teaching

Machine Learning

Description: This course presents a detailed investigation of current machine learning theory and methodologies. Introduces the background and basics of machine learning, including representation, inductive bias and performance evaluation. Analyzes and compares different machine learning methodologies, including statistical, connectionist, symbolic and optimization. Implementations of several methods will be provided for experimentation. This course will also introduce the PAC (probably-approximately correct) learning theory, which allows a more exact means for comparing methods and determining what is learnable. Current issues in machine learning research and alternative learning methods will also be examined as they relate to course topics.

Textbook: Shavlik and Dietterich, Readings in Machine Learning, Morgan Kaufmann, 1990.

Course Materials: A directory containing several semesters of Machine Learning course materials is accessible, including a syllabi, lecture outlines, homeworks and source code. A compressed, tar file of this entire directory is available as ml.tar.Z.

Artificial Intelligence I

Description: In contrast to traditional scientific programming, artificial intelligence (AI) applications require the manipulation of symbols. This course studies symbol manipulation languages and their application to AI. The course introduces the basics of functional programming in LISP and logic programming in PROLOG with emphasis on specific solutions to AI problems.

Textbooks: Winston, Artificial Intelligence, 3rd Ed., Addison-Wesley, 1992. Winston and Horn, LISP, 3rd Ed., Addison-Wesley, 1989. Clocksin and Mellish, Programming in Prolog, 3rd Ed., Springer-Verlag, 1987.

Course Materials: A directory containing AI I course materials is accessible, including a syllabus, homeworks and exams. A compressed, tar file of this entire directory is available as ai1.tar.Z.

Artificial Intelligence II

Description: This course describes the AI techniques necessary for an agent to act intelligently in the ``real'' world. Techniques include natural language processing, uncertainty reasoning, learning, vision and speech processing. Basic AI techniques (such as search, knowledge representation and planning) are reviewed in the context of learning the Prolog language which is used for implementing the more advanced techniques. Emphasis is on implementation and experimentation with the goal of building a robust intelligent agent.

Textbooks: Russell and Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 1995. Bratko, PROLOG Programming for Artificial Intelligence, 2nd Ed., Addison-Wesley, 1990.

Course Materials: A directory containing AI II course materials is accessible, including a syllabus, lecture notes, homeworks, exams and Prolog source code. A compressed, tar file of this entire directory is available as ai2.tar.Z.

Data Structures

Description: This course covers the design and analysis of algorithms with an emphasis on data structures. Linked lists, stacks, queues, trees, graphs, balanced trees, hash tables, associated recursive and non-recursive traversals, and sorting and searching algorithms are discussed. Techniques are discussed for analyzing lower bounds on problems and upper bounds on algorithms.

Textbook: Cormen, Leiserson and Rivest, Introduction to Algorithms, MIT Press and McGraw-Hill, 1990.

Course Materials: A directory containing several semesters of course materials is accessible, including syllabi, homeworks and exams. A compressed, tar file of this entire directory is available as ds1.tar.Z.

Design and Analysis of Algorithms (Advanced Data Structures)

Description: This course covers advanced techniques for analyzing upper bounds for algorithms and lower bounds for problems. Problem areas include sorting, data structures, graphs, dynamic programming, combinatorial algorithms, NP-completeness, and parallel models.