Masters Society Mac OS

Download New Mac Os
Masters Society Mac Os Catalina

Halo Master Chief Collection Mac OS X is FREE for everyone. Just pass the anti-spam verification and get this incredible collection of Halo games on your computer. As a tip, Halo 3 is the most acclaimed title. If you are not a fan who wants to play the story from the beginning, you should try this title. Typing Master For Mac free download - Typing Trainer, WavePad Masters Edition for Mac, Mavis Beacon Teaches Typing 2011, and many more programs. ‎Read reviews, compare customer ratings, see screenshots, and learn more about The Secret Society: Hidden Way. Download The Secret Society: Hidden Way for macOS 10.11 or later and enjoy it on your Mac. The Clinic is an experiential project-based course where students work in teams as data scientists with real-world clients under the supervision of instructors. Students will be tasked with producing key deliverables, such as data analysis, open source software, as. MacOS (formerly 'Mac OS X' and later 'OS X') is a line of open core graphical operating systems developed, marketed, and sold by Apple Inc., the latest of which is pre-loaded on all currently shipping Macintosh computers. MacOS is the successor to the original classic Mac OS, which had been Apple's primary operating system since 1984.

Below you will find core and elective classes offered by our Computation faculty in the Autumn, Winter, and Spring quarters. We will continue to add relevant course descriptions for the 2020-2021 academic year as they are made available. Please note that course offerings, instructors, dates, and times are all subject to change.

Students are also welcome to pursue graduate courses in the Toyota Technological Institute, the Department of Computer Science, the Booth School of Business, the Harris School of Public Policy, the Committee on Computational Neuroscience, and elsewhere in the Division of Social Sciences, if they meet the relevant prerequisites, and after assuring that all curricular requirements in our Computation program are met.

Autumn Quarter

MACS 30000 - Perspectives on Computational Analysis

Benjamin Soltoff

Massive digital traces of human behavior and ubiquitous computation have both extended and altered classical social science inquiry. This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. We will reexamine the scientific method in the social sciences in context of both theory development and testing, exploring how computation and digital data enables new answers to classic investigations, the posing of novel questions, and new ethical challenges and opportunities. Students will review fundamental research designs such as observational studies and experiments, statistical summaries, visualization of data, and how computational opportunities can enhance them. The focus of the course is on exploring the wide range of contemporary approaches to computational social science, with practical programming assignments to train with these approaches.

MACS 30121 - Computer Science with Social Science Applications - 1

Jon Clindaniel

This course is the first in a three-quarter sequence that teaches computational thinking and skills. The course will cover abstraction and decomposition, simple modeling, basic algorithms, and programming in Python. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. In recent offerings, students have written programs to simulate a model of housing segregation, determine the number of machines needed at a polling place, and analyze tweets from presidential debates.

MACS 30123 - Large-Scale Computing for the Social Sciences

Jon Clindaniel

Computational social scientists increasingly need to grapple with data that is either too big for a single machine and/or code that is too resource intensive to process on a single machine. In this course, students will learn how to effectively scale their computational methods beyond their local machines. The focus of the course will be social scientific applications, ranging from training machine learning models on large economic time series to processing and analyzing social media data in real-time. Students will be introduced to several large-scale computing frameworks such as MPI, MapReduce, Spark, and OpenCL, with a special emphasis on employing these frameworks using cloud resources and the Python programming language.

MACS 30124 - Computational Analysis of Social Processes

Jon Clindaniel

How does the human social and cultural world develop and change? The focus of this course is on introducing computational methods for studying the evolution of phenomena over time, alongside relevant theories for interpreting these processes from fields such as History, Anthropology, and Sociology. Students will gain hands-on experience using the Python programming language to harness a diverse set of digital data sources, ranging from satellite images to social media posts. Additionally, they will learn to employ computational approaches, such as simulation and dynamic topic modeling, to study social processes over a variety of different time scales: from the short term (changes in social media network structures over the course of the past week), to longer term (the evolution of English language discourse over the past 100 years), to deep time scales (long-term settlement pattern dynamics over the past 10,000 years).

MACS 30300 - Civic Data & Technology Clinic

TBD

The Civic Data and Technology Clinic at the University of Chicago partners with public interest organizations to leverage data science research and technology to address pressing social and environmental challenges. The Clinic also provides students with exposure to real-world projects and problems that transcend the conventional classroom experience including: 1) working with imperfect datasets, applying models and algorithms to real-world data, and navigating security and privacy issues; and 2) communicating results to a diverse set of stakeholders (e.g., industry, public interest, government agencies), and translating information into actionable insights, policy briefs and software prototypes. The Clinic is an experiential project-based course where students work in teams as data scientists with real-world clients under the supervision of instructors. Students will be tasked with producing key deliverables, such as data analysis, open source software, as well as final client presentations, and reports.

MACS 30500 - Computing for the Social Sciences

Benjamin Soltoff

This is an applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to social science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.

MACS 35000 - MA Research Commitment

James Evans

Student Initiated research and writing for the MA research component. Open only to MACSS students.

Back to Top

Winter Quarter

MACS 30100 - Perspectives on Computational Modeling

Philip Waggoner

Statistical learning methods are an important tool for computational social scientists seeking to conduct inference and prediction using observable data. This course presents a generic definition of a model in the social sciences, as well as a survey of a wide range of different types of statistical models used. While there is substantial overlap with the field of machine learning, this course focuses on methods emerging from statistics applied to supervised and unsupervised modeling, in service of the goals of inference, classification, and prediction. Students develop an understanding of the theory underlying these methods, apply the methods in an open source computing environment, and interpret model results. As a survey course, students will be exposed to a wide range of modeling strategies, with the expectation that they will later pursue deeper study into specific areas we cover.

MACS 30122 - Computer Science with Social Science Applications - 2

Jon Clindaniel

This course is the second in a sequence that teaches computational thinking and skills to students from a variety of Social Science disciplines. The course will cover topics in (1) data representation, (2) relational databases, (3) data cleaning and presentation, (4) shell scripting, (5) data structures, such as graphs, hash tables, and heaps. Applications and datasets from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. In recent offerings, students have written a course search engine and a system to do speaker identification. Students will program in Python and complete a group programming project.

MACS 30500 - Computing for the Social Sciences

Benjamin Soltoff

MACS 33002 - Introduction to Machine Learning

Philip Waggoner

This course will introduce students to the foundations of machine learning. We will cover everything needed for getting up and running with any computational research project from a machine learning perspective, including the key machine learning tasks: classification, regression, and dimension reduction. Students will learn about the fundamental mathematical concepts underlying machine learning algorithms, but this course will equally focus on the practical use of machine learning algorithms using open source computing. Through a final class group project, students will apply the learned concepts to addressing real social problems. We will cover a variety of topics, including: hypothesis spaces, resampling, feature engineering, model training, testing & tuning, supervised vs. unsupervised vs. competitive learning, regularization, tree-based methods, and several other algorithms contributing to a solid foundation of inferential machine learning for social science inquiry.

MACS 35000 - MA Research Commitment

James Evans

Student Initiated research and writing for the MA research component. Open only to MACSS students.

MACS 40100 - Big Data & Society

Benjamin Soltoff

The massive explosion of information produced by computers and sophisticated computational methods capable of harnessing this data to generate inferences has led to an increasingly data-driven society. Businesses, governments, and individuals seek to leverage this data to develop and market products, formulate policy, and improve the human condition. Computational approaches to decision making have become increasingly prevalent in domains such as criminal justice, education, employment, finance, and politics. While decision making based on data mining and algorithms has the capacity to improve society, critics argue that these approaches strengthen socioeconomic class divisions, constitute an invasion of privacy, or violate the civil rights of minority groups. This course will survey some of the major uses of big data in society and assess the potential ethical, moral, and legal implications of these models.

MACS 40400 - Computation and the Identification of Cultural Patterns

Jon Clindaniel

Culture is increasingly becoming digital, making it more and more necessary for those in both academia and industry to use computational strategies to effectively identify, understand, and (in the case of industry) capitalize on emerging cultural patterns. In this course, students will explore interdisciplinary approaches for defining and mobilizing the concept of “culture” in their computational analyses, drawing on relevant literature from the fields of Anthropology, Psychology, and Sociology. Additionally, they will receive hands-on experience applying computational approaches to identify and analyze a wide range of cultural patterns using the Python programming language. For instance, students will learn to predict emerging cultural movements using social media data, identify the latest fashion trends, and even decipher ancient symbols using archaeological databases.

MACS 51000 - Introduction to Causal Inference

Guanglei Hong, Kazuo Yamaguchi, and Fang Yang

This course is designed for graduate students and advanced undergraduate students from the social sciences, education, public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for the course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting (IPTW), marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-section and panel data, and fixed effects model. Intermediate Statistics or equivalent such as STAT 224 is a prerequisite. This course is a pre-requisite for “Advanced Topics in Causal Inference” and “Mediation, moderation, and spillover effects.”

Back to Top

Spring Quarter

Download New Mac Os

MACS 30123 - Large-Scale Computing for the Social Sciences

Jon Clindaniel

MACS 30200 - Perspectives on Computational Research

Jon Clindaniel & Philip Waggoner

This course focuses on applying computational methods to conducting social scientific research through a student-developed research project. Students will identify a research question of their own interest that involves a direct reference to social scientific theory, use of data, and a significant computational component. The students will collect data, develop, apply, and interpret statistical learning models, and generate a fully reproducible research paper. We will identify how computational methods can be used throughout the research process, from data collection and tidying, to exploration, visualization and modeling, to the final communication of results. The course will include modules on theoretical and practical considerations, including topics such as epistemological questions about research design, writing and critiquing papers, and additional computational tools for analysis.

MACS 30250 - Perspectives on Computational Research for Economics

TBD

This course focuses on scaling up computational approaches to social science analysis and modeling with big data in context of opportunities afforded by high performance and cloud computing. We will begin by exploring various data structures encountered in social science research, how to deal with large or complex data storage and streaming data, and how to factor considerations of computational complexity into their analyses. We will also study social science applications of parallel computing, both on stand-alone machines and in supercomputing environments, to carry out complex computations. Students will learn to carry out parallel I/O and parallel computation on their own machines and on a cluster. We will also address API construction and access, and explore cloud configurations for social science research designs. We will also help students construct web-based outward facing data, analysis and visualization portals. Students will efficiently gather, structure, perform and present analysis on large-scale social science data. This course will be specifically tailored to students concentrating in Economics.

MACS 31300 - AI Applications in the Social Sciences

Brooke Luetgert

Masters Society Mac Os Catalina

Artificial Intelligence (AI) describes algorithms constructed to reason in uncertain environments. This course provides an introduction to AI applications in the social sciences. Driven by the rapid increase in accessible big data documenting social behavior, AI has been applied to: increase effective diagnosis and prediction under different conditions, improve our understanding of human interaction, and increase the effectiveness of data management in different social and human services. Random forests and neural networks are among the most frequent AI methods used for prediction, while natural language processing and computer vision contribute to understanding decision-making and improving service provision. We begin with careful consideration for what AI can achieve and where current limitations exist by looking at a variety of real-world applications. We will focus on three core sections: search, representation, and uncertainty. In each section, we will explore major approaches, representational techniques and core algorithms. We will examine the trade-offs between model structure and the algorithmic constraints that this structure implies. The course is driven by hands-on exercises with AI algorithms written in Python. At the end of the term, you should be able to apply and tweak these algorithms to accommodate your own data and research interests.

MACS 35000 - MA Research Commitment

James Evans

Student Initiated research and writing for the MA research component. Open only to MACSS students.

MACS 40800 - Unsupervised Machine Learning

Philip Waggoner

A full understanding of data structure is not always possible, nor are tidy labeled data always available to researchers. With an applied focus, this course will cover prominent unsupervised machine learning techniques such as clustering, partitioning, dimension reduction, and deep learning for discovering latent, non-random structure in data. Further, mechanics involved in unsupervised machine learning will also be covered, such as measuring distance, visualization, and methods of validation. Where appropriate, we will also cover best practices in functional programming.

MACS XXXXX - Thinking with Deep Learning for Complex Social & Cultural Data Analysis

James Evans

A deluge of digital content is generated daily by web-based platforms and sensors that capture digital traces of human communication and connection, and complex states of society, culture, economy, and the world. Emerging deep learning methods enable the integration of these complex data into unified social and cultural “spaces” that enable new answers to classic social and cultural questions, and also pose novel questions. This book takes the position that the power of deep learning is unleashed by thinking with deep learning to reformulate and solve problems traditional statistical and machine learning methods cannot address. These include fusing diverse data like text, images, tabular and network data into integrated and comprehensive “digital doubles” of the scenarios you want to model, the generation of promising recommendations, and the creation of AI assistants that radically augment a social or cultural analyst’s imagination and intelligence. From the perspective of deep learning, everything can be viewed as data—novels, field notes, photographs, lists of transactions, networks of interaction, theories, epistemic styles—and our treatment examines how to configure deep learning architectures and multi-modal data pipelines to improve the capacity of representations, the accuracy of complex predictions, and the relevance of insights to substantial social and cultural questions.

The course uses Python and the widely popular PyData ecosystem to demonstrate all motivating examples and includes working code, accompanying exercises, relevant datasets and additional analytics and visualization that facilitate social and cultural interpretation and communication. The course will be based on a practical book (“Thinking with Deep Learning”) that the instructor and TA are writing for O’Reilly. Familiarity with Python is required.
This class is for anyone wishing to analyse textual, network, image or arbitrary structured and unstructured data, especially in concert with one another to solve complex social and cultural analysis problems (e.g., characterize a culture; predict next year’s ideology). We note that jobs involving deep learning are the highest paying jobs in AI related fields, and industry trends suggest that by 2022, one in five workers engaged in mostly nonroutine tasks will rely on AI to do a job. Deep learning represents the dominant paradigm of modern artificial intelligence.
By the end of this class the student will understand:

How Deep Neural methods can be used to analyze complex data
How critical questions can be addressed by massive data integration
Mathematical intuition and computational methods to link, analyze and interpret structured and unstructured data.
How creating “digital doubles” with integrated data enable the simulation and design of desired decisions, solutions and outcomes
How to navigate the PyData ecosystem to build strategic infrastructure to solve critical problems

And the student will be able to:

Find, scrape and clean messy data into the form a neural network requires
Organize and leverage data from diverse sources
Sample data effectively
Annotate data for model interpretability
Encode expectations and predict surprise
Construct deep learning methods to represent text, networks and tables, images and audio and integrate diverse data
Design the appropriate deep learning architecture
Create digital doubles enabling simulation of critical phenomena
Answer questions and make decisions from integrated data
Do all of this in the rich PyData ecosystem with code examples for every chapter