Thursday, June 20, 2019

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Machine Learning for the Social Sciences: Summer Course, Session D

Machine Learning for the Social Sciences: Summer Course, Session D

May 28, 2019 to July 05, 2019

Event Type: 

 

Join this summer course from ISERP and CU School of Professional Studies (SPS).

The course will run for the 6-week duration of the Columbia Summer Session D, from May 28th through July 5th, 2019.

QMSS S 5073 Machine Learning for Social Science is open to the public but requires registration with SPS prior to course registration. For more information on SPS application and registration, please visit their website and explore your options here. *Further registration information coming soon*

Michael Parrott

M W 4:00 pm-6:10 pm

Course Goals:

Social scientists need to fully engage with machine learning approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Every effort will be made to draw comparisons and contrasts between this machine learning approach and more traditional regression-based approaches in the social sciences. Emphasis will also be on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package, and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees, and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, non-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

More information here.

 
Data Analysis with Python: Summer Course, Session D

Data Analysis with Python: Summer Course, Session D

May 28, 2019 to July 05, 2019

Event Type: 

 

Join this summer course from ISERP and the CU School of Professional Studies (SPS).

The course will run for the 6-week duration of the Columbia Summer Session D, from May 28th through July 5th, 2019.

QMSS S5019 Data Analysis with Python is open to the public but requires registration with SPS prior to course registration. For more information on SPS application and registration, please visit their website and explore your options here. *Further registration information coming soon*

Course Goals:

QMSS S5019 Data Analysis with Python is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. The chief goal is to help students generate and interpret quantitative data in helpful and provocative ways. The hope is that by trying to measure the social world, students will see their thinking become clearer and their understandings of concepts grow more complex. They will also become competent at reading statistical results in social science publications and in other media. Only basic mathematics skills are assumed, but some more advanced math will be introduced as needed. For this course, a critical goal is to teach students how to manipulate and analyze data themselves using statistical software. We will focus almost exclusively on Python for this class (although, there will be a few cases where we will run R through Python because R can more readily do things than Python). There will be Python write-up assignments nearly every week, tied to hands-on data analysis lab sessions. These weekly assignments will be devoted to using Python to practice commands and to develop a paper using the General Social Survey, World Values Survey or another dataset of the student’s choosing. Credits: 3 points

Find more information here.

 
 
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