Quantum Computing In this course we will explore how quantum mechanics can be applied to problems in communications, algorithms, detection, and cryptography. We will learn how features such as entanglement, superposition, and no-cloning can sometimes give quantum systems an advantage over standard “classical” computers. We will also discuss the current situation and challenges facing experimental quantum computers, as well as the limits of quantum computing. No previous experience with quantum mechanics is required. (MATH 0200 or CSCI 0200) 3 hrs lect./disc.

Transforming Traditions How do traditions transform when they expand beyond the time and place of their origin? In considering this question we will interrogate the value of authenticity, consider concerns of appropriation, and examine the impacts of new perspectives on existing practices. Course material (readings, videos, and recordings) will focus on the Korean percussion genres pungmul and samulnori. However, students will draw on their own traditions, interests, and experiences for discussions and assignments. Weekly in-class drumming practices will culminate in a performance at the end of the semester. No prior musical experience is needed but must have a willingness to bang vigorously on a drum in a group.

Algorithms and Complexity This course focuses on the development of correct and efficient algorithmic solutions to computational problems, on the underlying data structures to support these algorithms, and on the social implications of algorithms. Topics include computational complexity, analysis of algorithms, proof of algorithm correctness, some advanced data structures, algorithmic techniques including greedy and dynamic programming, and the consequences of real-world applications of algorithms. The course complements the treatment of NP-completeness in CSCI 0301.

Mathematical Foundations of Computing In this course we will provide an introduction to the mathematical foundations of computer science, with an emphasis on formal reasoning. Topics will include propositional and predicate logic, sets, functions, and relations; basic number theory; mathematical induction and other proof methods; combinatorics, probability, and recurrence relations; graph theory; and models of computation.

Computational Complexity We will investigate the computational power of various resources. Examples include determining whether a computer with limited time or limited space can solve more difficult problems, attempting to characterize creativity as a resource, and comparing probabilistic and quantum computation. We will learn why we do not yet have answers to many of the most fundamental questions in computational complexity (like whether P=NP), and we will think critically about the value of studying these topics. Students enrolled in the College Writing (CW) section of the course will explore these ideas through writing, in particular, in three contexts that are critical for theoretical computer science: the proof (expert audience), the review paper (non-expert computer science audience), and the popular science article (educated public audience). (CSCI 0301).

Shelby Kimmel

Reviews and ratings for Shelby Kimmel at Middlebury College.Shelby Kimmel teaches Quantum Computing, Transforming Traditions, Algorithms and Complexity and more.  Explore top rated instructors and find the best for you.Is Shelby Kimmel good ? Find out on MiddCourses.

This was a tough class, conceptually. The homework was very difficult, but you are not graded on correctness which helps take some of the stress off. Even so, I often find myself not getting anything out of the homework because it is too difficult to find somewhere to start

Very engaging class with group work and intriguing teaching style. Content is very applicable to the real world, with a chance to explore connections in optional hw questions and the final project. Grading system is very fair, encouraging learning by allowing second chances at targets on exams and problem sets graded by effort/completion.

This course is literally a continuation of CS201. In algorithm & complexity, you will learn the three paradigms of algorithm -- greedy, dynamic programming, and divide-and-conquer; the complexity classes, reduction of NP-complete problems; some programming assignments in Java to code up these algorithms. Shelby makes it very manageable. She gives a lot of chances to retake problems you missed. To the contrary of what many people had said, I think taking 302 together with 301 is a good practice which helps you understand NP-completeness in a better sense.