Class
No. |
Course ID |
Title |
Credits |
Type |
Instructor(s) |
Days:Times |
Location |
Permission
Required |
Dist |
Qtr |
| 2975 |
DTSC-115-01 |
Intro to Computer Science |
1.25 |
LEC |
Spezialetti, Madalene |
TR: 10:50AM-12:05PM |
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. |
| 2976 |
DTSC-115-20 |
Intro to Computer Science |
1.25 |
LAB |
Spezialetti, Madalene |
T: 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. |
| 2977 |
DTSC-115-21 |
Intro to Computer Science |
1.25 |
LAB |
Johnson, Jonathan |
W: 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. |
| 2980 |
DTSC-229-01 |
Applied Linear Algebra |
1.00 |
LEC |
Kuenzel, Kirsti |
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. |
| 2981 |
DTSC-229-02 |
Applied Linear Algebra |
1.00 |
LEC |
Watson, Lori |
TR: 9:25AM-10:40AM |
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. |
| 2982 |
DTSC-234-01 |
Differential Equations |
1.00 |
LEC |
Shen, Yue |
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. |
| 2983 |
DTSC-312-01 |
Statistical Learning |
1.00 |
LEC |
Green, Dylan |
MWF: 10:00AM-10:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-312-01 |
| |
Prerequisite: C- or better in Mathematics 212 and Mathematics 228 or Mathematics 229, or permission of instructor. |
| |
This course provides a comprehensive introduction to foundational and advanced techniques in estimation and modeling from a mathematical standpoint. Key topics include maximum likelihood estimation, Bayesian inference, Markov chain Monte Carlo (MCMC) sampling, linear and regularized regression, as well as nonlinear approaches such as neural networks. Additional topics may cover dimension reduction, dealing with noisy and limited data, data visualization, optimization, and approximation theorems. Through programming-based assignments in MATLAB or Python, students will apply theoretical concepts to real-world problems, gaining hands-on experience in data analysis and model building. |
| 2984 |
DTSC-334-01 |
Partial Differential Equations |
1.00 |
LEC |
Ma, Lina |
MWF: 11:00AM-11:50AM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: MATH-334-01 |
| |
Prerequisite: C- or better in Mathematics 231 and 234, or permission of instructor. |
| |
An introduction to partial differential equations and their applications. Topics will include physical laws, Fourier series, heat equations, wave equations, and other classical models. Students will learn to approach problems using both analytical and qualitative methods. The purpose of the course is to gain an understanding of how to construct mathematical models using real-life applications and to acquire the skills necessary to solve these problems appropriately. |
| 2979 |
DTSC-360-01 |
Deep Learning |
1.00 |
LEC |
Chakraborttii, Chandranil |
TR: 10:50AM-12:05PM |
TBA |
|
NUM
|
|
| |
Enrollment limited to 24 |
Waitlist available: Y |
Mode of Instruction: In Person |
|
| |
|
Cross-listing: CPSC-360-01 |
| |
Prerequisite: C- or better in Computer Science 215. |
| |
The course will introduce the students to the fundamentals aspects of artificial neural networks (ANN), convolution neural networks (CNN), recurrent neural networks (RNNs), generative adversarial networks (GAN), and reinforcement learning. The focus will be primarily on the application of deep learning to realworld problems, with some introduction to mathematical foundations. Application of neural network frameworks to natural language processing (NLP), time series, computer vision, security, and data generation problems will be discussed. Python will be the primary programming language for this course. The students will work in teams towards a semester-long project using Google Tensorflow and Keras. |