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Course Listing

Systems Development for Computational Science

APCOMP 207
2020 Fall

David Sondak
Tuesday, Thursday
12:00pm to 01:15pm

This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to fit tools and approaches to the problem being solved.

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Data Science 1: Introduction to Data Science

APCOMP 209A
2020 Fall

Pavlos Protopapas, Kevin A. Rader, Christopher Tanner
Monday, Wednesday, Friday
09:00am to 10:15am

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part one of a two part series.

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Data Science 1: Introduction to Data Science

APCOMP 209A
2020 Fall

Pavlos Protopapas, Kevin A. Rader, Christopher Tanner
Monday, Wednesday, Friday
03:00pm to 04:15pm

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part one of a two part series.

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Data Science 2: Advanced Topics in Data Science

APCOMP 209B
2021 Spring

Pavlos Protopapas, Mark Glickman, Christopher Tanner

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning. Part two of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part two of a two part series.

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Critical Thinking in Data Science

APCOMP 221
2021 Spring

Michael Smith

This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include algorithmic accountability and discriminatory algorithms, black box algorithms, data privacy and security, ethical frameworks; and experimental and product design. We will work through case studies in a variety of contexts including media, tech and sharing economy platforms; medicine and public health; data science for social good, and politics. We will look at the underlying machine learning algorithms, statistical models, code and data. Threads of history, philosophy, business models and strategy; and regulatory and policy issues will be woven throughout the course.

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Computational Design of Materials

APCOMP 275
2021 Spring

Boris Kozinsky

This course will teach theoretical background and practical applications of modern computational methods used to understand and design properties of advanced functional materials. Topics will include classical potentials and quantum first-principles energy models, density functional theory methods, Monte Carlo sampling and molecular dynamics simulations of phase transitions and free energies, fluctuations and transport properties, and machine learning approaches. Examples will be based on rational design of industrially relevant materials for energy conversion and storage, electronic and magnetic devices, and nanotechnology.

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Topics in Applied Computation: Advanced Practical Data Science

APCOMP 295
2020 Fall

Pavlos Protopapas
Tuesday, Thursday
10:30am to 11:45am

In this course we explore advanced practical data science practices. The course will be divided into three major topics:
1) How to scale a model from a prototype (often in jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about microservices and Kubernetes. Along the way, students will be exposed to Dask.
2) How to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This can be very important, given the vast compute and time resources required to develop neural network models on these problems and given the huge jumps in skill that these models can provide to related problems. In this part of the course we will examine various pre-existing models and techniques in transfer learning.
3) In the third part we will be introducing a number of intuitive visualization tools for investigating properties and diagnosing issues of models. We will be demonstrating a number of visualization tools ranging from the well established (like saliency maps) to recent ones that have appeared in https://distill.pub.

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Topics in Applied Computation: Advanced Practical Data Science

APCOMP 295
2020 Fall

Pavlos Protopapas
Tuesday, Thursday
06:00pm to 07:15pm

In this course we explore advanced practical data science practices. The course will be divided into three major topics:
1) How to scale a model from a prototype (often in jupyter notebooks) to the cloud. In this module, we cover virtual environments, containers, and virtual machines before learning about microservices and Kubernetes. Along the way, students will be exposed to Dask.
2) How to use existing models for transfer learning. Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. This can be very important, given the vast compute and time resources required to develop neural network models on these problems and given the huge jumps in skill that these models can provide to related problems. In this part of the course we will examine various pre-existing models and techniques in transfer learning.
3) In the third part we will be introducing a number of intuitive visualization tools for investigating properties and diagnosing issues of models. We will be demonstrating a number of visualization tools ranging from the well established (like saliency maps) to recent ones that have appeared in https://distill.pub.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2020 Fall

Christopher Tanner
Wednesday
12:00pm to 02:45pm

The CSE capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Computational Science and Engineering Capstone Project

APCOMP 297R
2021 Spring

Christopher Tanner

The CSE capstone course is intended to provide students with an opportunity to work in groups of 3-4 on a real-world project. Students will develop novel ideas while applying and enhancing skills they have acquired from their core courses and electives. By requiring students to complete a substantial and challenging collaborative project, the capstone course will prepare students for the professional world and ensure that they are trained to conduct research. There will be no additional homework. There will be several mini-lectures, focusing on supplemental skills such as technical writing, public speaking, reading research papers, using version control software, identifying biases, etc. Since the projects concern real-world projects, datasets will likely be messy, and there is a focus on effectively communicating your progress to both the staff and partner organization.

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Interdisciplinary Seminar in Applied Computation

APCOMP 298R
2020 Fall

Daniel Weinstock

This course, centered on the Institute for Applied Computation Science (IACS) seminar series, will provide broad exposure to cutting-edge topics, applications, and unifying concepts in Computational Science & Engineering. Students will read, present and discuss journal articles related to IACS talks, attend the seminars and meet with visiting speakers. Possible topics to be covered include scientific visualization, computational approaches to disease, mathematical neuroscience, computational archeology, and computational finance.

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Interdisciplinary Seminar in Applied Computation

APCOMP 298R
2021 Spring

Daniel Weinstock

This course, centered on the Institute for Applied Computation Science (IACS) seminar series, will provide broad exposure to cutting-edge topics, applications, and unifying concepts in Computational Science & Engineering. Students will read, present and discuss journal articles related to IACS talks, attend the seminars and meet with visiting speakers. Possible topics to be covered include scientific visualization, computational approaches to disease, mathematical neuroscience, computational archeology, and computational finance.

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Special Topics in Applied Computation

APCOMP 299R
2020 Fall

Daniel Weinstock

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

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Special Topics in Applied Computation

APCOMP 299R
2021 Spring

Daniel Weinstock

Supervision of experimental or theoretical research on acceptable applied computation problems and supervision of reading on topics not covered by regular courses of instruction.

Course Website