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.

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.

Applied Data Science: Exploration, Prediction, and Generative Artificial Intelligence From predictive models to image and language generators, data science, machine learning, and generative artificial intelligence (AI) rely on computational methods for learning patterns in large, complex, datasets. In this hands-on course, we will apply multiple approaches for learning from data, including exploratory analysis, prediction, and generation. Students will describe social impacts of data-intensive technologies; manage and explore data sets; build predictive models; and programmatically apply generative models to real-world problems and data. This course is designed for students with introductory programming or data science preparation seeking to build foundational understanding and practical skills in machine learning and generative AI. This course fulfills the Responsible Computing requirement for the Computer Science major.

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

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.

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 Intensive Intro to Computing, Bioinformatics Algorithms, Applied Data Science and more.  Explore top rated instructors and find the best for you.Is Michael Linderman good ? Find out on MiddCourses.

Great class -- Linderman is awesome. Was very hard as expected but I came away with a foundational understanding of difficult concepts and the final project has become part of my resume / cv work and has helped a ton

Bioinformatics was a difficult course but valuable for my programming experience. It was especially useful taken in conjunction with Algorithms and Complexity, as the topics often overlapped. Professor Linderman is super nice and accommodating, though lectures can be difficult to understand at first. I would highly recommend going to office hours as that is where his teaching shines.

This is the most interesting CS class I have taken at Midd. You don’t need a bio background. The exams are hard and its target based grading, but the content is interesting. ML does a great job with his content delivery.