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FinTech: Shaping the Financial World
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CC BY-NC-SA
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This course about financial technology, or FinTech, is for students wishing to explore the ways in which new technologies are disrupting the financial services industry—driving material change in business models, products, applications and customer user interface. Amongst the significant technological trends affecting financial services into the 2020’s, the class will explore AI, deep learning, blockchain technology and open APIs. Students will gain an understanding of the key technologies, market structure, participants, regulation and the dynamics of change being brought about by FinTech.

Subject:
Applied Science
Business and Communication
Computer Science
Engineering
Finance
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Gensler, Gary
Date Added:
02/01/2020
Introduction to Deep Learning
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CC BY-NC-SA
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This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary.

Subject:
Applied Science
Computer Science
Engineering
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Amini, Alexander
Soleimany, Ava
Date Added:
01/01/2020
Introduction to text-mining for Humanists and Social Scientists
Unrestricted Use
CC BY
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This workshop aims to help students and teachers of Humanities and Social Science learn the basics of text-mining using Python. It is meant as an introduction to the use of computational techniques for analysing data for Humanists and Social Scientists. It contains a "Jupyter Notebook", which is basically a website where students will be taught how to write and execute code that will help them solve research problems that Humanists and Social scientists face. Additionally, this lesson also contains a video that demonstrates how to use that website. The total expected time to use this resouce is around 2 hours. 

Subject:
Arts and Humanities
Computer Science
Social Science
Material Type:
Activity/Lab
Author:
Anuj Gupta
Date Added:
11/22/2022
Natural Language Processing Project
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CC BY-NC-SA
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In this archive there are two activities/assignments suitable for use in a CS0 or Intro course which uses Python.

In the first activity, students are asked to "fill in the code" in a series of short programs that compute a similarity metric (cosine similarity) for text documents. This involves string tokenization, and frequency counting using Python string methods and datatypes.

https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Proj1?viewer=share/

In the second activity (taken directly from Think Python 2e) students use a pronunciation dictionary to solve a riddle involving homophones.

https://cocalc.com/share/bde99afd-76c8-493d-9608-db9019bcd346/171/Dicts2?viewer=share/

This OER material was produced as a result of the CS04ALL CUNY OER project

Subject:
Applied Science
Computer Science
Material Type:
Activity/Lab
Lecture Notes
Provider:
CUNY Academic Works
Provider Set:
John Jay College of Criminal Justice
Author:
Hunter R Johnson
Date Added:
06/04/2019
Natural Language and the Computer Representation of Knowledge
Conditional Remix & Share Permitted
CC BY-NC-SA
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6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Subject:
Applied Science
Arts and Humanities
Computer Science
Engineering
Life Science
Linguistics
Physical Science
Social Science
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Berwick, Robert
Date Added:
02/01/2003