Prerequisite: CMSC330. Restriction: permission of department.

Questions about the course should be directed to Andrej Rasevic at arasevic@cs.umd.edu

Prerequisite: CMSC330. Restriction: permission of department.

Questions about the course should be directed to Andrej Rasevic at arasevic@cs.umd.edu

Prerequisite: CMSC216. Restriction: permission of department.

This course will introduce students to important topics in distributed systems programming, programmed using the Go programming language. Topics include JSON over HTTP services, protocol buffers, gRPC, service discovery and load balancing, and Kubernetes. Coursework will involve programming problems and exam-based assessments.

Questions about the course should be directed to Andrej Rasevic at arasevic@cs.umd.edu

Prerequisite: Minimum grade of C- in CMSC131 or permission of instructor; and permission of CMNS-Computer Science department.

This course provides an introduction to the techniques that avoid the accumulation of "technical debt" by using effective methods from the start of the creation of a software project. Emphasis will be on design decisions, rather than algorithmic analysis. There are an infinite number of ways to implement an algorithm, and always more than one algorithm that will solve, or assist in solving, a particular problem. Good code is not strictly a function of which algorithm you use or even how you implement that algorithm. Good code is easy to understand, easy to maintain, easy to adapt, and easy to debug. Creating good code relies on knowing and using "best practices," defined as "commercial or professional procedures that are accepted or prescribed as being correct or most effective." Note that this definition does not state "accepted or prescribed as being correct" by whom. Thus, what constitutes a best practice is not necessarily a closed question. We will look at some practices that are widely accepted, and a few where debate goes on.

Prerequisite: Minimum grade of C- in CMSC132. Intermediate Python programming experience and basic familiarity with general APIs and Large Language Models (LLMs) is required. Please contact the instructor if you have any questions about whether you meet these prerequisites.

Course description: In this course we will learn how to leverage LLM applications. We will cover tools like Langchain, Gradio, streamlit, weights and biases, etc.

Quantum Boot Camp is an introduction to quantum technology via the study of key quantum devices and circuits. We use the polarizing optical beam splitter as an iconic device to study quantum random number generation, quantum money and coin tossing, quantum cryptographic key distribution and quantum logic gates. Students will acquire skills in reading original technical literature by examples of quantum circuit analysis. Prerequisites: basic calculus and linear algebra.