Artificial Intelligence: Overview & Business Applications KSE Online summer school

Description

From July 18 to July 28, Kyiv School of Economics will host an online summer school in “Artificial Intelligence: Overview & Business Applications”.

 

Instructor – Pini Ben-Or, Chief Science Officer at Aktana (a sales & marketing systems startup).

 

Classes will be held remotely.
Duration: two weeks on weekdays.
Time: 15:00-18:00 EEST (Kyiv time).
There is no tuition fee.

 

IF YOU WANT TO PARTICIPATE PRESS HERE.

Please, fill the form before Friday, July 8, 17:00 EEST (Kyiv time).

 

Summary: 

Students will emphasize on practical examples in business applications, understanding business data and business applications of AI, performance and quality metrics for AI systems, and on management challenges involved with AI development and deployment. First days will focus on authoring and publishing documents. Subsequent days will bring in more data munging and visualization. The course is comprised of 13 sessions:

 

Session 1: AI Foundations & Quick Introduction to Modern AI with Examples

Session 2: A Brief History of AI

Session 3: The State of AI in Academia vs. In Business and the Notion of Well-Rounded AI

Session 4: Examples of ML Models for Business Applications

Session 5: The Structure and Math of ML Algorithms (1) With Examples

Session 6: The Structure and Math of ML Algorithms (2) With Examples

Session 7: AI and Decision Theory (1) with Examples

Session 8: AI and Decision Theory (2) with Examples: Optimization

Session 9: Modern AI Technology

Session 10: NLP

Session 11: Well-Rounded AI

Session 12: AI Ethics Challenges

Session 13: Practical Challenges in Managing AI Development & Deployment; Course conclusion (a few student presentations).

 

Every session will include a couple of brief assignments.

 

For the final project, each student will create an individual product using reproducible workflows and tools introduced in the course. The deliverable can be a website, slide deck, article, or other product. The product should demonstrate basic facility in the use of these tools, and should integrate data and analysis in some way.

 

Students who already have a data set they are working with or a project in mind will be encouraged to use the course work process as a vehicle to advance that work. A default topic and dataset will be provided to students who don’t have their own.

 

Target audience: B.A., MBA and M.S. students focused on business, economics, or finance who are interested in business communications and/or data analysis.

 

Extensive programming experience or skills are not required. However, basic programming experience (such as involved in using Excel) is a must. We will use the KNIME Analytics Platform for lectures and assignments. Previous exposure to KNIME is not assumed: training will be provided. 

 

Schedule:

Monday, July 18 – Friday, July 22 15:00-18:00 (Kyiv time)

Monday, July 25 – Thursday, July 28 15:00-18:00 (Kyiv time)

 

What students will get upon the successful completion of the school:

– 3 ECTS credits will be awarded to students who attended all classes and pass all practical assignments including the final project.

– Authors of the top 3 student projects will receive 10% discounts for the first term at the KSE MA program in economics, business and finance, or social science.

Also, after the course students will receive a certificate, with  special mentions for participants doing exceptional work.

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About the instructor:

Pini Ben-Or

An experienced technology leader who as a Chief Science Officer oversees artificial intelligence (AI) and analytic innovation at Aktana (a sales & marketing systems startup). He has spent much of his career focused on improving business decisions using advanced analytics, optimization, business intelligence, and machine learning. In addition to being a data science expert, Pini enjoys and excels at building highly capable teams and nurturing a culture of innovation.

 

Prior to Aktana, Pini served as Global Head of Analytics at Actimize where he helped transform the company from reliance on rule systems and expert models to deploying fully agile machine-learning-based models for financial crime detection. Throughout his career Pini has introduced analytics innovations in the applications of machine learning, data management, operations optimization, marketing channel optimization, and business intelligence. He has multiple patents and patents pending, most recently in the area of machine-learning on network graph data.

 

Pini has a BSc in Physics, Mathematics, and Philosophy from The Hebrew University in Jerusalem, and a MA and MPhil in Philosophy from Columbia University in NY, where his research areas were Decision Theory, AI, and Philosophy of Physics.