New York University
Public University • US
Showing 13 courses from New York University
MOST from a Conceptual Perspective
New York University (via Coursera)
This course is aimed at intervention scientists working in any area--including public health, education, criminal justice, and others—interested in learning about an innovative framework for conducting intervention research. This course will show you how to use the multiphase optimization strategy (MOST) to: streamline interventions by eliminating inactive components; identify the combination of components that offers the greatest effectiveness without exceeding a defined implementation budget; develop interventions for immediate scalability; look inside the “black box” to understand which intervention components work and which do not; and improve interventions programmatically over time. In this course you will relate the MOST framework to your research objectives; learn how MOST differs from the standard approach to intervention development and evaluation; learn how to complete the preparation and optimization phases of MOST; and become familiar with rigorous and highly efficient experimental designs that will enable you to examine the performance of individual intervention components.
Overview of Advanced Methods of Reinforcement Learning in Finance
New York University (via Coursera)
In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, discuss market modeling, Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.
MOST from a Methodological Perspective
New York University (via Coursera)
This course is aimed at intervention scientists working in any area--including public health, education, criminal justice, and others—interested in learning about an innovative framework for conducting intervention research. This course will show you how to use the multiphase optimization strategy (MOST) to: streamline interventions by eliminating inactive components; identify the combination of components that offers the greatest effectiveness without exceeding a defined implementation budget; develop interventions for immediate scalability; look inside the “black box” to understand which intervention components work and which do not; and improve interventions programmatically over time. In this course you will relate the MOST framework to your research objectives; learn how MOST differs from the standard approach to intervention development and evaluation; learn how to complete the preparation and optimization phases of MOST; and become familiar with rigorous and highly efficient experimental designs that will enable you to examine the performance of individual intervention components.
Guided Tour of Machine Learning in Finance
New York University (via Coursera)
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Fundamentals of Machine Learning in Finance
New York University (via Coursera)
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Introduction to Cyber Attacks
New York University (via Coursera)
This course provides learners with a baseline understanding of common cyber security threats, vulnerabilities, and risks. An overview of how basic cyber attacks are constructed and applied to real systems is also included. Examples include simple Unix kernel hacks, Internet worms, and Trojan horses in software utilities. Network attacks such as distributed denial of service (DDOS) and botnet- attacks are also described and illustrated using real examples from the past couple of decades. Familiar analytic models are outlined such as the confidentiality/integrity/availability (CIA) security threat framework, and examples are used to illustrate how these different types of threats can degrade real assets. The course also includes an introduction to basic cyber security risk analysis, with an overview of how threat-asset matrices can be used to prioritize risk decisions. Threats, vulnerabilities, and attacks are examined and mapped in the context of system security engineering methodologies.
Real-Time Cyber Threat Detection and Mitigation
New York University (via Coursera)
This course introduces real-time cyber security techniques and methods in the context of the TCP/IP protocol suites. Explanation of some basic TCP/IP security hacks is used to introduce the need for network security solutions such as stateless and stateful firewalls. Learners will be introduced to the techniques used to design and configure firewall solutions such as packet filters and proxies to protect enterprise assets. Perimeter solutions such as firewalls and intrusion prevention systems are shown to have significant drawbacks in common enterprise environments. The result of such weakness is shown to often exist as advanced persistent threats (APTs) from nation-state actors. Such attacks, as well as DDOS and third-party attacks, are shown to have potential solutions for modern enterprise.
Climate Change and Human Rights
New York University (via Coursera)
This course is primarily aimed at anyone interested in learning about the growing field of climate change and human rights. It will discuss the history of the field, key actors and cases, as well as emerging trends and takeaways. This course is taught by César Rodríguez-Garavito, Professor of Law and Faculty Director and Chair of the Center for Human Rights and Global Justice at NYU School of Law.
Cyber Attack Countermeasures
New York University (via Coursera)
This course introduces the basics of cyber defense starting with foundational models such as Bell-LaPadula and information flow frameworks. These underlying policy enforcements mechanisms help introduce basic functional protections, starting with authentication methods. Learners will be introduced to a series of different authentication solutions and protocols, including RSA SecureID and Kerberos, in the context of a canonical schema. The basics of cryptography are also introduced with attention to conventional block ciphers as well as public key cryptography. Important cryptographic techniques such as cipher block chaining and triple-DES are explained. Modern certification authority-based cryptographic support is also discussed and shown to provide basis for secure e-commerce using Secure Sockets Layer (SSL) schemes.
Reinforcement Learning in Finance
New York University (via Coursera)
This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. By the end of this course, students will be able to Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management. Practice on valuable examples such as famous Q-learning using financial problems. Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.
Richard Schechner's Introduction to Performance Studies
New York University (via Coursera)
Performance Studies: An Introduction explores the wide world of performance--from theatre, dance, and music to ritual, play, political campaigns, social media, and the performances of everyday life. Performance studies also ranges across cultures--Asia, Africa, the Caribbean, Europe, the Americas. And it spans historical periods from the art of the paleolithic caves to YouTube and the avantgarde. This course is devised by Richard Schechner, one of the pioneers of performance studies, in dialogue with more than a dozen expert scholars and artists. Performance Studies: An Introduction puts students in dialogue with the most important ideas, approaches, theories, and questions of this dynamic, new academic field. Learning Objectives By the end of this course, you should be able to: Articulate and analyze the major concepts of performance studies Identify and analyze performances within the “broad spectrum of performance”--from everyday life and social media to performance art and global spectacles such as the Olympics Comprehend key terms of performance studies, including is/as performance, restored behavior, ritual, play, make-belief/make-believe, performance in everyday life, the performative, and intercultural performance Produce collaborative work that demonstrates teamwork in applying ideas learned in the course Compare, analyze, and interpret performances of their own and other cultures Articulate how the major concepts of the course relate to their own experiences and worldviews Analyze and criticize in a constructive way the work of classmates The lessons present Schechner’s concept of performance studies along with online assignments. In the assignments, students apply what they are learning by composing short responses to materials, writing in their NYU Classes Forums, and by reviewing other students’ forum posts each week. Students choose either to work in groups of 3 to 5 on a term-long project maintaining a project portfolio in NYU Classes or to writ...
Engineering Health: Introduction to Yoga and Physiology
New York University (via Coursera)
This course gives you access to an exploration of physiological systems from the perspective of overall health and wellness. In particular, a focus on yoga, meditation and mindfulness as a therapeutic intervention in chronic illness and long term treatment. This course is intended for yoga practitioners and teachers, as well as college students and medical practitioners looking for a deeper understanding of the physiological benefits of yoga. The value of taking this course is to understand the impact that yoga can have on reducing stress, and aiding in healing or preventing physiological pathologies. Throughout this course, we will learn about different physiological systems and highlight yoga practices that can influence different systems and reduce pathology. Reading material will include analysis of scientific studies that have successfully utilized yoga practice as a tool for treatment of various illnesses such as: hypertension, stress, diabetes, insomnia, chronic pain and PTSD. In order to understanding these conditions, lectures will provide a complete understanding of the correlating physiological system. The weekly course assignment will include physiology lectures, a weekly yoga practice, suggested readings, and optional discussions for a total of 3-5 hours per week. The course will provide a tremendous amount of information and hands on experience for those interested in alternative health perspectives and a more in depth scientific understanding of this ancient healing method.
Enterprise and Infrastructure Security
New York University (via Coursera)
This course introduces a series of advanced and current topics in cyber security, many of which are especially relevant in modern enterprise and infrastructure settings. The basics of enterprise compliance frameworks are provided with introduction to NIST and PCI. Hybrid cloud architectures are shown to provide an opportunity to fix many of the security weaknesses in modern perimeter local area networks. Emerging security issues in blockchain, blinding algorithms, Internet of Things (IoT), and critical infrastructure protection are also described for learners in the context of cyber risk. Mobile security and cloud security hyper-resilience approaches are also introduced. The course completes with some practical advice for learners on how to plan careers in cyber security.