Class
No. |
Course ID |
Title |
Credits |
Type |
Instructor(s) |
Days:Times |
Location |
Permission
Required |
Dist |
Qtr |
| 3414 |
DTSC-115-01 |
Intro to Computer Science |
1.25 |
LEC |
Islam, Maminur |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 36 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-115-01 |
| |
Prerequisite: C- or better in Computer Science 110 or mathematics skills appropriate for enrolling in a calculus class. |
| |
This course provides an introduction to computer science from broad and diverse perspectives, through object-oriented problem-solving using the Java programming language. Throughout the course, recurring themes are abstraction and effective use of basic algorithmic constructs such as sequence, selection and iteration. The building blocks of object-oriented programming such as encapsulation, inheritance, polymorphism and generics are covered and reinforced with practical applications. Required weekly laboratory sessions deepen students' learning with hands-on opportunities to experiment with the concepts covered in the lectures. |
| 3415 |
DTSC-115-20 |
Intro to Computer Science |
1.25 |
LAB |
Islam, Maminur |
W: 1:30PM-4:10PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 18 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-115-20 |
| |
Prerequisite: C- or better in Computer Science 110 or mathematics skills appropriate for enrolling in a calculus class. |
| |
This course provides an introduction to computer science from broad and diverse perspectives, through object-oriented problem-solving using the Java programming language. Throughout the course, recurring themes are abstraction and effective use of basic algorithmic constructs such as sequence, selection and iteration. The building blocks of object-oriented programming such as encapsulation, inheritance, polymorphism and generics are covered and reinforced with practical applications. Required weekly laboratory sessions deepen students' learning with hands-on opportunities to experiment with the concepts covered in the lectures. |
| 3416 |
DTSC-115-21 |
Intro to Computer Science |
1.25 |
LAB |
Johnson, Jonathan |
R: 1:30PM-4:10PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 18 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-115-21 |
| |
Prerequisite: C- or better in Computer Science 110 or mathematics skills appropriate for enrolling in a calculus class. |
| |
This course provides an introduction to computer science from broad and diverse perspectives, through object-oriented problem-solving using the Java programming language. Throughout the course, recurring themes are abstraction and effective use of basic algorithmic constructs such as sequence, selection and iteration. The building blocks of object-oriented programming such as encapsulation, inheritance, polymorphism and generics are covered and reinforced with practical applications. Required weekly laboratory sessions deepen students' learning with hands-on opportunities to experiment with the concepts covered in the lectures. |
| 3422 |
DTSC-212-01 |
Probability |
1.00 |
LEC |
Churchill, Victor |
MWF: 11:00AM-11:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 30 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-212-01 |
| |
Prerequisite: C- or better in Mathematics 132 or 231, or instructor consent. |
| |
This foundational course provides an introduction to probability theory, covering key concepts and theorems essential for understanding randomness and uncertainty. Topics include discrete and continuous random variables, important densities and distribution functions, joint distributions and covariance, conditional probability and Bayes' theorem, the Law of Large Numbers, and the Central Limit Theorem. Additional topics may be included based on the instructor's focus as time permits. |
| 3417 |
DTSC-215-01 |
Data Structures & Algorithms |
1.25 |
LEC |
Miyazaki, Takunari |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 16 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-215-01 |
| |
Prerequisite: C- or better in Computer Science 115L. |
| |
A study of data structures and algorithms using a high-level programming language. The basic data structures (lists, stacks, queues, trees, and files) and basic algorithms (searching, sorting, and file management) will be introduced and implemented. Data and procedural abstraction, software design principles, and the analysis of the complexity of algorithms will be discussed. Details related to programming will be covered in a required weekly lab. |
| 3418 |
DTSC-215-20 |
Data Structures & Algorithms |
1.25 |
LAB |
Miyazaki, Takunari |
T: 1:30PM-4:10PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 16 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-215-20 |
| |
Prerequisite: C- or better in Computer Science 115L. |
| |
A study of data structures and algorithms using a high-level programming language. The basic data structures (lists, stacks, queues, trees, and files) and basic algorithms (searching, sorting, and file management) will be introduced and implemented. Data and procedural abstraction, software design principles, and the analysis of the complexity of algorithms will be discussed. Details related to programming will be covered in a required weekly lab. |
| 3423 |
DTSC-229-01 |
Applied Linear Algebra |
1.00 |
LEC |
Bartels, Richard |
MWF: 11:00AM-11:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 19 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-229-01 |
| |
Prerequisite: C- or better in Mathematics 132, 205, 231 or 253, or consent of instructor. |
| |
An introduction to linear algebra with an emphasis on practical applications and computation. Topics will be motivated by real-world examples from a variety of disciplines, for instance medical imaging, quantum states, Google’s PageRank, Markov chains, graphs and networks,difference equations, and ordinary and partial differential equations. Topics will include solvability and sensitivity of large systems, iterative methods, matrix norms and condition numbers, orthonormal bases and the Gram-Schmidt process, and spectral properties of linear operators. MATLAB will be used for coding throughout the course, although no previous experience is required. Students may not count both Mathematics 228 and Mathematics 229 for credit towards the Math major. |
| 3424 |
DTSC-229-02 |
Applied Linear Algebra |
1.00 |
LEC |
Skardal, Per Sebastian |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 19 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-229-02 |
| |
Prerequisite: C- or better in Mathematics 132, 205, 231 or 253, or consent of instructor. |
| |
An introduction to linear algebra with an emphasis on practical applications and computation. Topics will be motivated by real-world examples from a variety of disciplines, for instance medical imaging, quantum states, Google’s PageRank, Markov chains, graphs and networks,difference equations, and ordinary and partial differential equations. Topics will include solvability and sensitivity of large systems, iterative methods, matrix norms and condition numbers, orthonormal bases and the Gram-Schmidt process, and spectral properties of linear operators. MATLAB will be used for coding throughout the course, although no previous experience is required. Students may not count both Mathematics 228 and Mathematics 229 for credit towards the Math major. |
| 3425 |
DTSC-234-01 |
Differential Equations |
1.00 |
LEC |
Skardal, Per Sebastian |
MWF: 12:00PM-12:50PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 30 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-234-01 |
| |
Prerequisite: C- or better in Mathematics 132. |
| |
An introduction to the theory of ordinary differential equation and their applications. Topics will include analytical and qualitative methods for analyzing first-order differential equations, second-order differential equations, and systems of differential equations. Examples of analytical methods for finding solutions to differential equations include separation of variables, variation of parameters, and Laplace transforms. Examples of qualitative methods include equilibria, stability analysis, and bifurcation analysis, as well as phase portraits of both linear and nonlinear equations and systems. At the discretion of the Mathematics Department, section enrollments may be balanced. |
| 3426 |
DTSC-237-01 |
Math of Finance |
1.00 |
LEC |
Ma, Lina |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 19 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-237-01 |
| |
Prerequisite: C- or better in Mathematics 132. |
| |
This is an introductory course on the mathematics of financial products, with a focus on options. The main topics include: mechanics and properties of options, option pricing in binomial models, the Black-Scholes model, stochastic process, and the "Greeks". Equal emphasis is placed on proofs of formulas and the application of those formulas to pricing financial derivatives. Prerequisite: C- or better in Mathematics 132 and 207, or permission of instructor |
| 3427 |
DTSC-309-01 |
Numerical Analysis |
1.00 |
LEC |
Churchill, Victor |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 19 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-309-01 |
| |
Prerequisite: C- or better in Computer Science 115, MATH 132, and any mathematics course numbered 200 or higher. |
| |
Theory, development, and evaluation of algorithms for mathematical problem solving by computation. Topics will be chosen from the following: interpolation, function approximation, numerical integration and differentiation, numerical solution of nonlinear equations, systems of linear equations, and differential equations. Treatment of each topic will involve error analysis. |
| 3419 |
DTSC-352-01 |
Artificial Intelligence |
1.00 |
LEC |
Chakraborttii, Chandranil |
MW: 8:30AM-9:45AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
Also cross-referenced with PSYC |
Cross-listing: CPSC-352-01 |
| |
Prerequisite: C- or better in Computer Science 215L and Computer Science 203 (or concurrent enrollment in 203). |
| |
A study of basic principles and research methods in artificial intelligence. The course exposes students to selected topics in the field including pattern recognition, problem solving, theorem proving, knowledge representation, and natural language understanding by computers. The course will draw on recent advances made by cognitive scientists in each of these applications. Students are expected to study the theoretical background of an application. They will also complete several programming and simulation assignments during the semester. |
| 3420 |
DTSC-372-01 |
Database Fundamentals |
1.00 |
LEC |
Johnson, Jonathan |
TR: 10:50AM-12:05PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-372-01 |
| |
Prerequisite: C- or better in Computer Science 215L and Computer Science 203 (or concurrent enrollment in 203). |
| |
This course provides an introduction to the design and implementation of database systems. Topics include: the relational algebra and relational database models; SQL and other relational query languages; the implementation of database management systems, including indexing, concurrency control and transaction management. |
| 3421 |
DTSC-415-01 |
Special Topics: AI Integration |
1.00 |
LEC |
Kousen, Kenneth |
M: 1:30PM-4:10PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-415-01 |
| |
Prerequisite: C- or better in Computer Science 215. |
| |
Explore the integration of AI systems into software development, focusing on programmatic access to AI services. The course covers RESTful API interactions, local AI open-source models, and applications in chatbots, image generation, visualization, and audio/video creation. By blending theoretical foundations with implementation, the course equips students with the knowledge to leverage AI in modern software solutions. |