Spring 1998 Course information page
Have a great summer!
The Spring 1998 session of 15-299 is complete. The course materials
will be kept on this Web site until next year. Here are pointers
to resources that were used throughout the semester:
- Course Number: 15-299
- Credit: 12 units
- Lectures: Tues/Thurs 10:30-11:50, 2210 Doherty Hall
- Instructor: Bruce Maggs
- Office hours: Thurs. 2-3pm, 4123 Wean Hall (x8-7654)
- Email him at bmm+@cs
- Teaching assistants:
- Rob Miller
- Recitation section `A', 10:30-11:30 Mon. in 203 Student Center
- Office hours: Sun. 3-4pm, 5103 Wean Hall (x8-7571)
- Email him at rcm@cs
- Adam Kalai
- Recitation section `B', 11:30-12:20 Mon. in 203 Student Center
- Office hours: Mon. 3-4pm, 7113 Wean Hall (x8-7123)
- Email him at akalai@cs
- Hal Burch
- Recitation section `C', 12:30-1:20 Mon. in 203 Student Center
- Recitation section `D', 1:30-2:20 Mon. in 203 Student Center
- Office hours: Fri. 3-4pm, 7110 Wean Hall (x8-7670)
- Email him at hburch@cs
- Doug Beeferman
- Office hours: Thurs. 1-2pm, 5101 Wean Hall (x8-8139)
- Email him at dougb@cs
- Course secretary: Dorothy Zaborowski
- Office: 4116 Wean Hall (x8-3779)
- Email her at daz+@cs
- Bruce Maggs is
an Associate Professor in the Computer Science Department. His
research interests include parallel architectures and algorithms.
- Rob Miller is
a third-year graduate student in the Computer Science Department. His
research interests are in programming languages and user interfaces.
- Adam Kalai is
a second-year graduate student in the Computer Science Department.
His research interests include computer graphics, virtual
reality, and theoretical computer science.
- Hal Burch is
a first-year graduate student in the Computer Science Department.
His research interests include algorithms and scientific computing.
- Doug Beeferman is
a third-year graduate student in the Computer Science Department.
His research interests include natural language modeling and
- Counting: Learn how to count without counting.
- Induction: Recognize it in all its guises.
- Exemplification: Find a sense in which you can try out a problem
or solution on small examples.
- Abstraction: Abstract away the inessential features of a problem.
- Modularity: Decompose a complex problem into simpler subproblems.
- Representation: Understand the relationships between different
possible representations of the same information or idea.
- Refinement: The best solutions come from a process of repeatedly
refining and inventing alternative solutions.
- Toolbox: Build up your vocabulary of abstract structures.
- Optimization: Understand which improvements are worth it.
- Probabilistic methods: Flipping a coin can be surprisingly helpful!
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