CRN

15347

Distribution

E/G

Course No.

CMSC 141 Q Course

Title

Computer Science I

Professor

Rebecca Thomas

Schedule

Mon Wed 1:30 pm - 2:50 pm ALBEE 106
Lab: Fri 1:30 pm - 3:00 pm ALBEE 100
This course will introduce the notion of a computational process as well as the idea of a program as a director of such processes. The study of problem-solving techniques and algorithm development will prepare students to apply the syntax and structure of a programming language to a variety of problem statements. The course will include regular programming assignments as well as a programming project. Prerequisite: Eligibility for Q courses.



CRN

15052

Distribution

E/G

Course No.

CMSC 142 Q Course

Title

Computer Science II

Professor

Rebecca Thomas

Schedule

Mon Wed 8:30 am - 9:50 am ALBEE 106
Lab: Wed 10:00 am - 12:00 pm ALBEE 100
This course is a continuation of Computer Science 141. Elementary data structures, such as lists, records, and trees, will be discussed, as will the essentials of sorting algorithms and algorithm analysis. The inclusion of other topics such as error handling and other control features will be subject to instructor whim. Prerequisite: Computer Science 141 or its equivalent. Corequisite: Mathematics 111.



CRN

15053

Distribution

E/G

Course No.

CMSC 201 Q Course

Title

Computer Science III

Professor

Sven Anderson

Schedule

Mon Wed 10:00 am - 11:20 am ALBEE 106
Lab: Tu: 3:00 pm - 4:30 pm ALBEE 100
This course covers the implementation and use of advanced data structures such as stacks, queues, hash tables, binary search trees, sets, and graphs via an object-oriented programming language. Prerequisite: Computer Science 142.



CRN

15283

Distribution

E

Course No.

CMSC 220 Q Course

Title

Non-Standard Computation

Professor

Robert Cutler

Schedule

Th 4:00 pm - 6:00 pm HEG 300

2 Credits. This course explores the physical nature of computation. Standard computation is based on implementation of algorithms for digital computers. In this course we will examine alternate systems in which computation can be performed and the different laws of nature that apply to each. Topics to be examined include quantum computation, DNA or molecular computation, generalized time reversible computation, as well as emerging areas such as chaos computing, and cellular automata. Prerequisites: Math 111 and CS1 or permission of the instructor.



CRN

15378

Distribution

E/G

Course No.

CMSC 305 Q Course

Title

Design of Programming Languages

Professor

Robert McGrail

Schedule

Mon 10:00 am - 11:20 am HEG 201
Wed 10:00 am - 11:20 am HEG 106
Lab: Mon 3:00 pm - 4:30 pm ALBEE 100
This course will cover a selection of issues important to the design of programming languages including, but not limited to, type systems, procedure activation, parameter passing, data encapsulation, dynamic memory allocation, and concurrency. In addition, the functional, logic, and object-oriented programming paradigms will be presented as well as a brief history of high-level programming languages. Students will be expected to complete a major programming project in Standard ML of New Jersey as well as other programming assignments in Java or Prolog.



CRN

15372

Distribution

E

Course No.

CMSC 352 Q Course

Title

Biologically-Inspired Machine Learning

Professor

Sven Anderson

Schedule

Mon Wed 1:30 pm - 2:50 pm HEG 300
In this course, we will examine computation as a metaphor for understanding adaptive systems. We will study several biological systems and relate them to abstract models that incorporate elements of their data structures, information processing, and learning. Neuron models, neural networks, and evolutionary learning will be studied using mathematics and computer simulation. This course emphasizes information processing, pattern recognition, and associated computational abilities of artificial models, but takes an ethological approach to understanding how natural and artificial intelligent systems adapt to their environment. No background in biology is assumed. Prerequisites: Calculus I and Computer Science II. Background in statistics and linear algebra is also recommended.