Courses

AI Product Management Seminar (10238)

Abstract:

In today's rapidly evolving technological landscape, artificial intelligence (AI) plays a critical role in designing products and services in various industries. This seminar provides participants with a comprehensive understanding of the principles, challenges, and methodologies of product management specifically tailored to AI. During this course, we'll explore the intersection of technology, user experience, market trends, and business strategy, equipping you with the skills to effectively manage AI-driven products from concept to launch and beyond.Participants will engage in hands-on activities, case studies, and discussions to deepen their understanding of AI technologies, product development lifecycles, and the unique product management considerations that leverage machine learning and data science. Moreover, as a central component of the seminar, the students will submit a strategic roadmap for an AI-based product idea they choose to develop, synthesizing the knowledge and skills acquired during the course. By the end of the seminar, you will be equipped to lead product initiatives in the field of artificial intelligence, while addressing the complexities and ethics associated with this transformative field.

Seminar in Development Methodologies (10261)

Abstract:

(Software Development Process) , תוך שימוש בקריטריונים להערכת איכות תהליך הפיתוח, ומדדי איכות לשימוש בשלבים השונים של תהליך פיתוח התוכנה.

נושאי הלימוד:

• The Pain of Software Development – Dan Berry

• Waterfall Model - (Royce father & son)

• Incremental Models - Bazili

• The Spiral Model & Risk Management – Barry Boehm

• XP

• Agile

• Tropos

• Open Source

• RUP (Rational Unified Process)

• SW- CMMI (SEI)

• IEEE 1074 – Life Cycle Process

• MIL-STD-498 (DoD)

• ROPES (Douglass)

Ethics in Software Engineering (10825)

Abstract:

Introduction to the basic ideas in the philosophy of morality and ethics Challenges that life in the high-tech industry poses

The knowledge-rich industry exposes its workers to numerous moral challenges and ethical dilemmas:

Is it legitimate to work in a company without ethical principles?

What about companies that vehemently oppose Israel?

Is it ethical to work in a company involved in gambling?

And what would Aristotle and Kant say about all of this?

In each lesson, dilemmas from everyday life in the industry will be presented in studies and in general. Some of the examples are difficult to hear and contain triggers for prejudice, chauvinism, profiteering from murder, and more.

Introduction to Artificial Intelligence (19101)

Abstract:

The course provides a comprehensive foundation in the fundamentals of artificial intelligence. The first part focuses on Search and Planning concepts. The second part focuses on methods of Probabilistic Reasoning. The theoretical material is accompanied by examples.

Ethics in Data Science (19104)

Abstract:

Artificial Intelligence (AI) is evolving at an extremely rapid pace. We should expect to see significant changes in our society as AI systems become embedded in many aspects of our lives. This course will cover various philosophical and practical issues raised by the use of current and future AI systems. The questions we will address will examine different ethical angles through a variety of lenses: How do we align the goals of autonomous AI systems with our own goals?

Foundations of Machine Learning (65028)

Abstract:

This course introduces students to the fundamental principles of machine learning, focusing on understanding and applying various algorithmic models. Students will learn how to analyze and interpret complex datasets, build regression and classification models, and apply unsupervised learning techniques. Throughout the course, students will be assigned homework exercises to practice the concepts learned, and a final exam will be held

Ethics of AI and Regulation (65031)

Abstract:

Artificial intelligence (AI) is developing at an extremely fast pace. We should expect to see significant changes in our society as artificial intelligence systems are embedded in many aspects of our lives. This course will cover various philosophical and practical issues raised by the use of current and future AI systems. The questions we will deal with will examine different ethical angles through a variety of lenses: How do we align the goals of autonomous AI systems with our own? Is the future of artificial intelligence an existential threat to humanity? How do we prevent learning algorithms from acquiring morally objectionable biases? Should autonomous AI be used to kill in combat? What ethical rules should be used for artificial intelligence such as a self-driving car? Can artificial intelligence systems suffer moral damages?

Product Management Seminar in the World of Artificial Intelligence (80971)

Abstract:

The course explores the key challenges, opportunities, and considerations involved in managing AI-powered products. Throughout the seminar, students will analyze real-world case studies and examine the entire AI product lifecycle—from identifying user needs and defining value, through development, evaluation, and launch, to ongoing monitoring and improvement. The course will also address user experience, trust, data quality, performance measurement, ethics, transparency, and the societal implications of intelligent systems. Students will select a research-oriented or practical issue, analyze it critically, and produce a seminar paper integrating academic literature with insights from industry.

World Models vs. LLMs - AI's Next Fault Line (80972)

Abstract:

This course examines the potential evolution of artificial intelligence beyond large language models by exploring world models capable of representing environments, understanding causal relationships, predicting future states, and planning actions. Students will compare the capabilities, limitations, and operating principles of LLMs and world models and investigate how these approaches can be combined in advanced AI systems.

The course covers topics such as environment representation and learning, prediction and simulation, causality, planning, memory, learning through interaction, and AI agents. Through academic literature, case studies, and real-world applications, students will evaluate whether world models represent a fundamental shift in the development of artificial intelligence and examine their technological, business, ethical, and societal implications. As part of the seminar, students will select a research topic, analyze it critically, and produce a paper integrating academic literature with practical insights.

The Economics of Artificial Intelligence (80974)

Abstract:

This course examines the impact of artificial intelligence on the economy, organizations, labor markets, and business models. Students will explore how AI changes production, productivity, innovation, competition, and decision-making, as well as how economic value is created through data, models, computing infrastructure, and human capital.

Topics include the economics of data, the costs of developing and operating AI systems, economies of scale, network effects, digital platforms, automation, occupational change, inequality, and market concentration. The course will also examine pricing strategies and business models for AI products, together with their societal, regulatory, and environmental implications.

Through academic literature, data, and real-world case studies, students will analyze the effects of AI on firms, workers, consumers, and countries. As part of the seminar, students will select an economic issue related to AI, examine it critically, and produce a paper integrating economic theory, academic research, and empirical evidence.

Adaptive Interfaces and Personalized Experiences (80975)

Abstract:

This course examines the design, development, and evaluation of digital interfaces that adapt to users’ characteristics, behaviors, preferences, and contexts. Students will explore how AI- and data-driven systems can dynamically modify content, structure, interaction methods, and the level of assistance they provide.

The course integrates principles from human–computer interaction, user experience, machine learning, recommender systems, and behavioral design. Topics include user modeling, personalization strategies, context-aware interfaces, learning from feedback, experimentation, and evaluating the effects of adaptation on satisfaction, performance, and engagement.

Alongside the potential benefits, students will examine risks related to privacy, transparency, bias, manipulation, user autonomy, and information bubbles. Through academic literature, case studies, and a practical project, students will design an adaptive experience that balances user needs, business objectives, technological capabilities, and responsible-design principles.

Explainable and Interpretable AI (80976)

Abstract:

This course examines how artificial intelligence systems can be understood, interpreted, and explained. Students will distinguish between model interpretability—the ability to understand a model’s internal mechanisms—and explainability, which focuses on communicating meaningful and useful reasons for its outputs to people.

The course introduces methods for explaining model predictions and decisions, including feature importance, local and global explanations, counterfactual explanations, visualizations, and behavioral analysis. It also addresses the explainability of large language models and generative systems, as well as the relationships among explanations, trust, decision-making, accountability, and regulation.

Through academic literature, exercises, and case studies from healthcare, finance, employment, and public services, students will assess the quality and limitations of different explanations. In the course project, they will analyze an existing model, develop an explanation mechanism, and evaluate its suitability for different audiences.

Collective Intelligence: Humans, AI, and Groups (80977)

Abstract:

This course examines how individuals, groups, and artificial intelligence systems collaborate to solve problems, create knowledge, and make decisions. Students will investigate the conditions under which a group can outperform an individual and how AI can improve collective performance—or undermine it through bias, overreliance, reduced diversity of thought, and concentration of power.

The course combines perspectives from computer science, human–computer interaction, social psychology, organizational behavior, and decision theory. Topics include the wisdom of crowds, group decision-making, knowledge sharing, coordination, collaborative problem-solving, algorithmic leadership, hybrid teams, and multi-agent systems.

Through experiments, simulations, and case studies, students will investigate how role allocation, diversity of perspectives, communication structures, and the presentation of AI recommendations affect group performance. In the course project, students will design and evaluate a human–AI collaboration process addressing an engineering, organizational, or societal problem.