Artificial Intelligence: Overview & Business Applications

July 8 - 31, 2024, four sessions per week (Monday to Thursday)➡️ Remote and on-campus sessions available

Course Summary

This course will cover the essentials of AI with a specific emphasis on business applications. Students will work on practical examples to understand business data, explore AI performance and quality metrics, and tackle the management challenges of AI development and deployment. First days will focus on authoring and publishing documents. Subsequent days will bring in more data munging and visualization. 


Dates: July 8 - 31, 2024, four sessions per week (Monday to Thursday)Time: 18:30 - 19:50 EEST (Kyiv time)Duration: 4 weeks



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


Remote and on-campus sessions available


Credits: 3 ECTS credits for attending all classes and completing assignments, including the final project.Certification: Participants will receive a certificate, with special mentions for exceptional work.

The curriculum includes:

  • 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 - The Interplay Between Business Data and Algorithm Choice

  • Sesion 5:

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

  • Sesion 6:

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

  • Sesion 7:

    AI and Decision Theory (1) with Examples

  • Sesion 8:

    AI and Decision Theory (2) with Examples: Optimization

  • Sesion 9:

    Modern AI Technology

  • Sesion 10:

    From Traditional NLP to Large Language Models

  • Sesion 11:

    Well-Rounded AI - The Key to Understanding AI Business Solutions

  • Sesion 12:

    AI Ethics Challenges

  • Sesion 13:

    Practical Challenges in Managing AI Development & Deployment

  • Sesion 14:

    Working with LLMs using LLM application development platforms: technology and Business challenges

  • Sesion 15:

    Course conclusion (a few student presentations). Additional Topics as Requested - TBD

Each session will include brief assignments, and for the final project, students will create an individual product demonstrating their proficiency with the tools and concepts introduced during 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 Excel) is necessary. We will use the KNIME Analytics Platform for lectures and assignments. Previous exposure to KNIME is not assumed: training will be provided. Text element

Tuition fee for the course is 5000 uah - all received funds will be doubled and sent to “Achilles” battalion*Course is free for KSE students*

Registration Deadline: Please fill out the registration form by Friday, July 28, 17:00 EEST (Kyiv time)

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.