Sample Curriculum:
Epistemology, Scientific Method, and Automated Discovery
Epistemology, Scientific Methodology, and Automated learning and discovery
is one of the major research thrusts of the department, with active involvement
from Nobel Laureate Herb Simon, Teddy
Seidenfeld, Clark Glymour, Peter
Spirtes, Richard Scheines, Kevin
Kelly, and Horacio Arlo-Costa. The department's
breadth of coverage in this area is unique among Philosophy departments
in the nation, running the gamut from cognitive models of learning and
problem solving, automated discovery of causal models, foundations of statistical
inference, belief revision theory, and formal learning theory. The faculty's
overlapping interests in this area place students in a unique position
to make novel theoretical and practical contributions to traditional epistemological
issues.
The department's broad commitment to this research area is buttressed
by its close association with the university's Department of Statistics,
the Center for Applied Learning and Discovery and by the university's nationally
preeminent Computer Science programs in artificial intelligence and machine
learning.
The TETRAD project for causal inference from statistical data has earned
many research grants providing the opportunity for students to do genuine
research on a project in computatioanl epistemology with real world applications.
Defended Ph.D. Dissertations in this area of concentration
August 1996, Christopher Meek, Selecting Graphical
Models: Causal and Statistical Modeling
August 1996,Thomas Richardson, Feedback Models:
Interpretation and Discovery
Currently at: Department of Statistics, University of Washington
August 1997, Oliver Schulte, Hard Choices in
Scientific Inquiry
Currently at: Department of Philosophy, University of Edmonton
This sample curriculum is offered to illustrate how the core
requirements of the program can be met by a student interested in Epistemology,
Scientific Method, and Automated Discovery. Students are encouraged to
design programs attuned to their own interests.
Fall First Year
- Research Seminar: Introduces students to research topics in the
department.
- Minds, Machines and Knowledge: Implications of the computational
model of mind for epistemology, and vice-versa
- Logic and Computability: Syntax and semantics of first-order logic.
- Elective: e.g., Cognitive Processes and Problem Solving: A survey
of the "Human Problem Solving" paradigm in cognitive psychology
(taught by Prof. Herb Simon, jointly appointed in Psychology, Computer
Science, and Philosophy).
Spring First Year
- Probability and Artificial Intelligence: An introduction
to contemporary probabilistic methods in AI, including neural nets and
Bayes nets.
- Computability and Incompleteness: Godel's theorems, computability,
and their epistemological significance.
- Elective: e.g., Probability and Mathematical Statistics I:
- Elective: e.g., Introduction to Parallel Distributed Processing: Overview
of network models, including perception, memory, language, knowledge representation
and learning.
Fall Second Year
- Elective: e.g., Philosophy of Mind:Contemporary topics in philosophy
of mind, especially issues in the logic of belief.
- Elective: e.g., Artificial Intelligence:
- Elective: e.g., Seminar on Epistemology: Contemporary issues in
the theory of knowledge, including new issues in the logic of knowledge
representation and learnability.
- Elective: e.g., Probability and Mathematical Statistics II:
Spring Second Year
- Elective: e.g., Research symposium: Students present their master's
thesis projects.
- Elective: e.g., Recursion and hierarchies:Uncomputability with applications
to learning
- Elective: e.g., Seminar on the Philosophy of Science: Contemporary
issues in the logic of confirmation, explanation, realism, causation, and
in particular sciences.
- Elective: e.g., Seminar on philosophy and methodology
Fall Third Year
- Directed Reading
- Dissertation Research
- Elective: e.g., Topics in the Philosophy of Science: Classic issues
in the philosophy of science from a novel perspective.
- Elective: e.g., Machine Learning: This course covers the theory
and practice of machine learning from a variety of perspectives.
Spring Third Year
- Directed Reading
- Dissertation Research
- Elective: e.g., Seminar on Foundations of Statistics
- Elective: e.g., Logic in Artificial Intelligence: nonmonotonic logic,
conditionals and belief revision.