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.
Introduction to Computer Science and Programming Using Python
Course Website. 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.
Thinking as computation : a first course / Hector J. Levesque - Details - Trove
Part two of a two part series. 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.
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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.
In this Course, we shall familiarize with the main computational methods which permit to simulate and analyze the behavior of a wide range of problems involving fluids, solids, soft matter, electromagnetic and quantum systems, as well as the dynamics of some biological and social systems. Both regular and complex geometrical grids will be discussed through Finite Differences, Volumes and Elements, respectively.
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- Inquiry into Design and Computation | The Penn State Stuckeman School;
In Part II we shall discuss mesoscale technique based on the two basic mesoscale descriptions: probability distribution functions, as governed by Boltzmann and Fokker-Planck kinetic equations, and stochastic particle dynamics Langevin equations. The lattice Boltzmann method will be discussed in great detail, with applications to fluids and soft matter problems. In addition, we shall provide the opportunity of hands-on on a multi scale codes for X extreme simulations at the interface between physics and molecular biology.
An introduction to Physics-Aware Machine Learning will also be presented. 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.
Special order items
Examples will be based on rational design of industrially relevant materials for energy conversion and storage, electronic and magnetic devices, and nanotechnology. Computer simulations are recognized as an essential part of scientific and engineering pursuits.
Their predictive power will play an ever more important role in scientific discoveries, national competitiveness, and in solving societal problems. Search form.penvenicmy.tk
Philosophy, Computing, and Artificial Intelligence
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Course duration:. August, Course coordinator:.
Related Thinking as Computation: A First Course
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