Curriculum & Courses

The course covers the essentials of AI in data analysis, it teaches participants to use AI tools for uncovering trends and making informed decisions. The course emphasizes practical applications, data strategy management, and addresses ethical and privacy concerns in data-driven AI. Through real-world case studies and hands-on projects, students will learn to integrate AI insights with strategic business planning, overcoming organizational challenges and effectively communicating the value of AI-driven decisions. This course prepares students to lead data-centric initiatives in their organizations, leveraging AI for enhanced decision-making and competitive business advantage.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This comprehensive course explores the rapidly evolving AI-driven business landscape. It combines essential leadership strategies with in-depth market analysis to prepare students for the challenges and opportunities presented by technological advancements. The course covers the impact of AI on leadership, emphasizing adaptable leadership styles for tech environments. It teaches managing diverse workforces, leading digital transformation, and developing problem-solving and decision-making skills for the tech industry. It also includes analyzing AI market trends, interpreting industry reports, and conducting competitive analysis to understand strategies and dynamics of different market players.

This course offers a comprehensive overview of the essential practices of securing AI systems and protecting data. The first part of the course focuses on the security threats unique to AI, including adversarial attacks, data poisoning, and model theft. Students will learn about the techniques used to exploit AI systems and the strategies to defend against these threats. This includes best practices in securing AI data, algorithms, and infrastructures. The second part of the course examines the core concepts of privacy, including data confidentiality, integrity, and the rights of individuals regarding their personal information. Students will explore issues such as data surveillance, profiling, and the potential for AI to infringe on personal privacy. Students will learn about various scenarios where AI's data processing capabilities might conflict with privacy norms and expectations. Practical strategies for preserving privacy in AI systems form a key part of the curriculum. Students will explore techniques such as anonymization, data minimization, and secure data storage.

This course provides a comprehensive approach at designing and architecting AI systems, focusing on scalability and efficiency. It covers AI architecture, including layers, modules, and interfaces, and guides students in selecting appropriate algorithms and models for specific use cases. The course addresses the challenges of scaling AI systems to handle large data volumes and complexity and emphasizes performance optimization. Advanced topics include distributed computing, cloud technologies, and edge computing for improved AI system performance. Students will also learn about deploying AI systems in different environments and integrating AI with existing IT infrastructure, tackling compatibility issues and ensuring seamless data integration.

This course explores the intersection of artificial intelligence and user experience design, focusing on creating intuitive, ethical, and user-centered AI systems. Students will examine how AI transforms digital interactions, design workflows, and decision-making processes, with emphasis on human-AI collaboration, accessibility, and transparency. Through hands-on projects and case studies, students will learn to design and evaluate AI-driven products that balance technical innovation with user needs, fostering trust and usability in complex systems.

The Capstone course is the culmination of the student's academic and professional experience in the Artificial Intelligence Management program. Over the course of the semester, students will be asked to apply the knowledge gained during the program to a project. With a focus on AI integration, students will incorporate the skills necessary for analyzing key issues, thinking creatively, and making sound decisions in order to develop an AI strategy, solution design, and implementation plan. Projects will address global and local challenges as well as issues of accessibility and inclusiveness related to artificial intelligence. Students will have the option to either undertake a self-directed project of their choosing or engage in a simulated scenario that replicates the real-world tasks and responsibilities of AI managers.

This is a core requirement of the degree. Students must earn a grade of "B" or better.

The Capstone course is the culmination of the student's academic and professional experience in the Artificial Intelligence Management program. Over the course of the semester, students will be asked to apply the knowledge gained during the program to a project. With a focus on AI integration, students will incorporate the skills necessary for analyzing key issues, thinking creatively, and making sound decisions in order to develop an AI strategy, solution design, and implementation plan. Projects will address global and local challenges as well as issues of accessibility and inclusiveness related to artificial intelligence. Students will have the option to either undertake a self-directed project of their choosing or engage in a simulated scenario that replicates the real-world tasks and responsibilities of AI managers.

This is a core requirement of the degree. Students must earn a grade of "B" or better.

Communication Strategies for Digital Transformation explores the challenges of leading and communicating organizational change in an era increasingly dominated by Artificial Intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing the issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

Communication Strategies for Digital Transformation explores the challenges of leading and communicating organizational change in an era increasingly dominated by Artificial Intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing the issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

This course offers a comprehensive overview of agile digital solution development, with a special focus on Artificial Intelligence (AI) applications. It delves into Agile, Lean, and design thinking methodologies, underscoring the importance of developing AI solutions that deliver exceptional user experience (UX) and user interface (UI). Students will actively engage in the entire process of AI solution development, from conducting targeted user and market research specific to AI, to designing AI-driven user experiences. They will also learn to prototype AI components, create intuitive AI-powered interfaces, and enhance user interactions using AI. This hands-on approach ensures that students not only understand theoretical frameworks but also acquire practical skills in crafting user-centric AI solutions.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

This is a core requirement of the degree. A grade of B or better is required.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

This is a core requirement of the degree. A grade of B or better is required.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course covers the essentials of AI in data analysis, it teaches participants to use AI tools for uncovering trends and making informed decisions. The course emphasizes practical applications, data strategy management, and addresses ethical and privacy concerns in data-driven AI. Through real-world case studies and hands-on projects, students will learn to integrate AI insights with strategic business planning, overcoming organizational challenges and effectively communicating the value of AI-driven decisions. This course prepares students to lead data-centric initiatives in their organizations, leveraging AI for enhanced decision-making and competitive business advantage.

The course provides a deep dive into the landscape of available AI solutions, exploring various models and platforms that have been developed for industry-specific applications. Students will learn about the strengths and weaknesses of these off-the-shelf AI solutions, gaining insight into how they can be effectively selected and adapted. A core focus of the course is on the process of tailoring AI models. This includes understanding the technical aspects of modifying existing AI algorithms, as well as aligning these modifications with the strategic goals and operational requirements of the organization. Students will engage with topics such as data preparation, model retraining, and fine-tuning AI systems for enhanced performance and accuracy. This covers the technical, operational, and organizational challenges of embedding AI solutions into existing business processes and systems. Key areas of discussion include compatibility assessment, integration planning, and change management strategies to ensure smooth adoption and minimal disruption.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This course explores the fundamental principles of Artificial Intelligence (AI), focusing on critical aspects like the explainability and transparency of AI decision-making processes. Students will examine the security risks and privacy concerns associated with AI, understanding the technical hurdles in ensuring strict governance. The course guides students through the complexity of regulatory and legal frameworks that are shaping the incorporation of AI across different industries. A significant part of the course is dedicated to learning about established standards and best practices in the field, providing the knowledge necessary to develop and implement effective AI governance models. The course emphasizes the importance of considering both global and regional perspectives, highlighting the varied strategies and policies in AI deployment worldwide.

This comprehensive course explores the rapidly evolving AI-driven business landscape. It combines essential leadership strategies with in-depth market analysis to prepare students for the challenges and opportunities presented by technological advancements. The course covers the impact of AI on leadership, emphasizing adaptable leadership styles for tech environments. It teaches managing diverse workforces, leading digital transformation, and developing problem-solving and decision-making skills for the tech industry. It also includes analyzing AI market trends, interpreting industry reports, and conducting competitive analysis to understand strategies and dynamics of different market players.

This comprehensive course explores the rapidly evolving AI-driven business landscape. It combines essential leadership strategies with in-depth market analysis to prepare students for the challenges and opportunities presented by technological advancements. The course covers the impact of AI on leadership, emphasizing adaptable leadership styles for tech environments. It teaches managing diverse workforces, leading digital transformation, and developing problem-solving and decision-making skills for the tech industry. It also includes analyzing AI market trends, interpreting industry reports, and conducting competitive analysis to understand strategies and dynamics of different market players.

This course offers a comprehensive overview of the essential practices of securing AI systems and protecting data. The first part of the course focuses on the security threats unique to AI, including adversarial attacks, data poisoning, and model theft. Students will learn about the techniques used to exploit AI systems and the strategies to defend against these threats. This includes best practices in securing AI data, algorithms, and infrastructures. The second part of the course examines the core concepts of privacy, including data confidentiality, integrity, and the rights of individuals regarding their personal information. Students will explore issues such as data surveillance, profiling, and the potential for AI to infringe on personal privacy. Students will learn about various scenarios where AI's data processing capabilities might conflict with privacy norms and expectations. Practical strategies for preserving privacy in AI systems form a key part of the curriculum. Students will explore techniques such as anonymization, data minimization, and secure data storage.

This course offers a comprehensive overview of the essential practices of securing AI systems and protecting data. The first part of the course focuses on the security threats unique to AI, including adversarial attacks, data poisoning, and model theft. Students will learn about the techniques used to exploit AI systems and the strategies to defend against these threats. This includes best practices in securing AI data, algorithms, and infrastructures. The second part of the course examines the core concepts of privacy, including data confidentiality, integrity, and the rights of individuals regarding their personal information. Students will explore issues such as data surveillance, profiling, and the potential for AI to infringe on personal privacy. Students will learn about various scenarios where AI's data processing capabilities might conflict with privacy norms and expectations. Practical strategies for preserving privacy in AI systems form a key part of the curriculum. Students will explore techniques such as anonymization, data minimization, and secure data storage.

This course provides a comprehensive approach at designing and architecting AI systems, focusing on scalability and efficiency. It covers AI architecture, including layers, modules, and interfaces, and guides students in selecting appropriate algorithms and models for specific use cases. The course addresses the challenges of scaling AI systems to handle large data volumes and complexity and emphasizes performance optimization. Advanced topics include distributed computing, cloud technologies, and edge computing for improved AI system performance. Students will also learn about deploying AI systems in different environments and integrating AI with existing IT infrastructure, tackling compatibility issues and ensuring seamless data integration.

This course explores the intersection of artificial intelligence and user experience design, focusing on creating intuitive, ethical, and user-centered AI systems. Students will examine how AI transforms digital interactions, design workflows, and decision-making processes, with emphasis on human-AI collaboration, accessibility, and transparency. Through hands-on projects and case studies, students will learn to design and evaluate AI-driven products that balance technical innovation with user needs, fostering trust and usability in complex systems.

The Capstone course is the culmination of the student's academic and professional experience in the Artificial Intelligence Management program. Over the course of the semester, students will be asked to apply the knowledge gained during the program to a project. With a focus on AI integration, students will incorporate the skills necessary for analyzing key issues, thinking creatively, and making sound decisions in order to develop an AI strategy, solution design, and implementation plan. Projects will address global and local challenges as well as issues of accessibility and inclusiveness related to artificial intelligence. Students will have the option to either undertake a self-directed project of their choosing or engage in a simulated scenario that replicates the real-world tasks and responsibilities of AI managers.

The Capstone course is the culmination of the student's academic and professional experience in the Artificial Intelligence Management program. Over the course of the semester, students will be asked to apply the knowledge gained during the program to a project. With a focus on AI integration, students will incorporate the skills necessary for analyzing key issues, thinking creatively, and making sound decisions in order to develop an AI strategy, solution design, and implementation plan. Projects will address global and local challenges as well as issues of accessibility and inclusiveness related to artificial intelligence. Students will have the option to either undertake a self-directed project of their choosing or engage in a simulated scenario that replicates the real-world tasks and responsibilities of AI managers.

Communication Strategies for Digital Transformation explores the challenges of leading and communicating organizational change in an era increasingly dominated by Artificial Intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing the issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

Communication Strategies for Digital Transformation explores the challenges of leading and communicating organizational change in an era increasingly dominated by Artificial Intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing the issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

Communication Strategies for Digital Transformation explores the challenges of leading and communicating organizational change in an era increasingly dominated by Artificial Intelligence. It recognizes that successful digital transformation transcends mere technological implementation, deeply impacting organizational culture and human dynamics. Students learn how to develop a communication strategy and implement a communication plan addressing the issues related to AI and organizational change. The course examines multiple communication approaches as well as tools and techniques that are appropriate for different audiences.

This course provides an overview of generative AI, defining its role and capabilities in creating novel content. The course focuses on the practical applications of generative AI in various creative domains. Students will explore how AI is used to generate visual art, create music, design products, and even write creative literature. The course will showcase a range of case studies and examples where generative AI has been successfully implemented, offering insights into its transformative potential. Students will also engage with the creative process behind generative AI. This involves understanding the human-AI collaboration in creative work, exploring how artists and designers can guide AI to enhance creativity rather than replace it. Discussions will include the ethical considerations and implications of AI-generated content, particularly regarding originality, copyright, and authorship.

The course introduces students to the fundamentals of data engineering. Students will learn about the lifecycle of data, from its initial collection to its final usage in AI models, including the various challenges and best practices at each stage. Students will explore various methods for acquiring data, whether structured or unstructured, from diverse sources. This includes understanding the nuances of data quality, integrity, and the importance of ethical data collection practices. Students will learn about different data storage solutions like databases, data warehouses, and data lakes, learning how to choose and implement the right storage strategy based on the specific needs of AI applications. Finally, students will gain hands-on experience with tools and frameworks used for processing large datasets. This includes learning about batch and real-time data processing, ETL (Extract, Transform, Load) operations, and data pipeline design and optimization.

This course offers a comprehensive overview of agile digital solution development, with a special focus on Artificial Intelligence (AI) applications. It delves into Agile, Lean, and design thinking methodologies, underscoring the importance of developing AI solutions that deliver exceptional user experience (UX) and user interface (UI). Students will actively engage in the entire process of AI solution development, from conducting targeted user and market research specific to AI, to designing AI-driven user experiences. They will also learn to prototype AI components, create intuitive AI-powered interfaces, and enhance user interactions using AI. This hands-on approach ensures that students not only understand theoretical frameworks but also acquire practical skills in crafting user-centric AI solutions.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

This is a core requirement of the degree. Students must earn a grade of "B" or better.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

Artificial Intelligence is directly related to ethics in the sense that it impacts the wellbeing of individuals and communities through the creation of disruptive technology. The development and application of AI can generate new industry values and wealth, but can also create or exacerbate ethical concerns across various sectors. One critical area of ethical consideration is the potential for AI to reinforce or introduce biases, which can lead to unfair outcomes and perpetuate inequalities. This course explores the ethical responsibilities and core values associated with technological innovations, with a special focus on understanding and mitigating AI biases. Students will learn to articulate ethical frameworks in relation to key industry concepts such as inclusiveness, fairness, reliability, and accountability. Additionally, the course will address the digital divide and persistent disparities in diversity and access to this technology. Finally, the course will examine fundamental philosophical elements, examining the distinction between humans and machines and exploring questions of existential risk, consciousness, intentionality, and moral agent in the context of AI.

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

This course examines how to combine advanced AI technology management with effective business strategies. It focuses on building an innovative mindset in organizations and includes strategic planning as a key part. Students will learn to align AI projects with company goals, covering areas like market study, resources allocation, risks management. The course integrates fundamental principles of Lean and Agile project management. This includes understanding how to apply Lean methodologies for efficient workflow and resource optimization, and Agile practices for adaptive planning, evolutionary development, and flexible responses to change. Restrictions: Include

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.

The course provides an overview of the capabilities of the latest AI techniques, setting the stage for a deeper exploration of this cutting-edge field. The course provides a historical context of AI development and its trajectory. Students will gain a solid grounding in the various domains of AI, including machine learning, natural language processing, robotics, and more. Students will be introduced to fundamental concepts and techniques essential for understanding AI. Throughout the course, students will be exposed to various applications, providing them with a clear perspective on both the potential and the current limitations of these technologies. A significant focus will be on analyzing the interplay between human decision-making and AI systems, emphasizing the strengths and weaknesses of both.