Software Development This course examines the process of developing larger-scale software systems. Laboratory assignments emphasize sound programming practices, tools that facilitate the development process, and teamwork.

Parallel Computing Most modern computer architectures are parallel at multiple scales. In this course students will learn to develop programs that can efficiently use those parallel resources to improve performance and solve ever larger problems. Through a project-based survey students will be introduced to parallel hardware (multicore processors, clusters, GPUs), memory models (shared vs. non-shared), locality, synchronization, and different parallel programming models (threads, MapReduce, message-passing, SIMT, and more). Programming assignments will be implemented in multiple languages. (CSCI 0202) 3hrs. lect./lab

Intensive Introduction to Computing In this course we will provide an introduction to the field of computer science, geared towards students with some prior computer science or programming experience, or a background in quantitative problem-solving (e.g., advanced math coursework). Students will learn a variety of algorithmic strategies, including iterative and recursive approaches, and how to implement those strategies as Python programs. We will study computational techniques utilized in the natural sciences, social sciences and other disciplines. Additional topics will include large-scale data analysis and the ethical issues introduced by computing technologies.

Artificial Intelligence Artificial Intelligence (AI) is the study of computational systems that exhibit rational behavior. Applications include strategic game playing, medical diagnosis, speech and handwriting recognition, Internet search, and robotics. Course topics include intelligent agent architectures, search, knowledge representation, logical reasoning, planning, reasoning under uncertainty, machine learning, and perception and action. We will also discuss the social implications of AI systems. This course fulfills the Responsible Computing requirement for the Computer Science major.

Bioinformatics Algorithms In this course we will explore and implement algorithmic solutions to modern biology questions. Students will be introduced to motivating biological questions—such as, “How do we compare DNA sequences?”—and then implement solutions to those problems using dynamic programming, graph, randomized, combinatorial and/or other algorithmic approaches. At the completion of the course students will be able to precisely define computational biology problems, design an algorithmic solution and implement that solution in software. No biology background is assumed, but students are expected to be able to implement sophisticated algorithms in Python. This course fulfills the Responsible Computing requirement for the Computer Science major.

Computing for the Sciences In this course we will provide an introduction to the field of computer science geared towards students interested in mathematics and the natural sciences. We will study problem-solving approaches and computational techniques utilized in a variety of domains including biology, chemistry, physics, and engineering. Students will learn how to program in Python and other languages, how to extract information from large data sets, and how to utilize a variety of tools employed in scientific computation. The course has no prerequisites and assumes no prior experience with programming or computer science.

Michael Linderman

Reviews and ratings for Michael Linderman at Middlebury College.Michael Linderman teaches Software Development, Parallel Computing, Intensive Intro to Computing and more.  Explore top rated instructors and find the best for you.Is Michael Linderman good ? Find out on MiddCourses.

Linderman is one of the best professors that I've had at Middlebury. He is an incredibly effective teacher and really cares about his students. The homeworks are like mini projects and require a decent amount of time. If this is something that you are interested in, I'd 100% recommend.

This class is very cool but definitely hard. Each week you have a pretty big homework project to work on, and a lot of the material you need to know was not in the lectures. You do a group project in the second half of the semester and you don't get much in-class time on it, which is tough since you need to put a lot of time into it

Professor Linderman is a very knowledgeable professor and explains material in a fairly digestible way. It was most helpful to go to office hours every week. The entire second half of the semester is focused on a group project making your own website. 