Network Science Many social, ecological, and technological systems are networked – their structure arises from connections between many individual components. Network science is the scientific study of connected systems using tools from mathematics, computer science, physics, and beyond. In this course, we will study measurements of centrality, hierarchy, and segregation in networks; random graph models; algorithms for network data mining; models of agents interacting on networks; and network data visualization. Students will write mathematical arguments; perform simulation experiments; implement analysis techniques; read contemporary research papers; navigate existing software packages for network analysis; and complete a substantial group project.

Machine Learning Machine learning algorithms detect patterns in data and use these patterns to make decisions. This course introduces the theory and practice of machine learning. Tasks considered may include classification, regression, clustering, dimensionality reduction, text embedding, and reinforcement learning. Applications may include predictive analytics, data visualization, pattern recognition, and strategic game-playing. We will also discuss the social implications of automated decision systems. This course fulfills the Responsible Computing requirement for the Computer Science major. (Not open to students who have already taken CSCI 1051.)

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. (CSCI 0145 or CSCI 0146 or CSCI 0150)

Senior Thesis The senior thesis is required for all CSCI majors who wish to be considered for high and highest departmental honors, and is recommended for students interested in pursuing graduate study in computer science. Students will spend the semester researching and writing, and developing and experimenting as appropriate for their topic. All students will be expected to report on their work in the form of a written thesis, a poster, and an oral presentation at the end of the semester. In addition, throughout the semester, students will meet as a group to discuss research and writing, and will be expected to attend talks in the Computer Science lecture series. Before approval to join the class is granted, students are expected to have chosen a thesis adviser from the CSCI faculty, and determined a thesis topic with the guidance and approval of that adviser.

Introduction to Computing In this course we will provide a broad introductory overview of the discipline of computer science, with no prerequisites or assumed prior knowledge of computers or programming. A significant component of the course is an introduction to algorithmic concepts and to programming using Python; programming assignments will explore algorithmic strategies such as selection, iteration, divide-and-conquer, and recursion, as well as introducing the Python programming language. Additional topics will include: the structure and organization of computers, the Internet and World Wide Web, abstraction as a means of managing complexity, social and ethical computing issues, and the question "What is computation?" (Juniors and Seniors by waiver) (formerly CSCI 0101) 3 hr. lect./1 hr. lab

Phil Chodrow

Reviews and ratings for Phil Chodrow at Middlebury College.Phil Chodrow teaches Network Science, Machine Learning, Math Foundations of Computing and more.  Explore top rated instructors and find the best for you.Is Phil Chodrow good ? Find out on MiddCourses.

Phil is a really nice guy, and he is very enthusiastic in teaching. However, the flipped learning for the course was hard to adjust to. He would give readings for an online textbook and YouTube videos for us to do warm ups that he would go over the next day. I would've preferred actual class instruction rather than going over the warm ups.

Prof Phil is a great teacher but I didn’t enjoy the flipped classroom style. It was hard to get motivated to go class because I felt like I knew most of the material. I liked how the learning targets accumulated throughout the tests.

I loved this course. It was math heavy but I enjoyed that. Professor Chodrow has a really effective teaching style. There was homework but it wasn't just busy work. I felt like all the work I was doing was contributing to my learning. 