This course introduces students to the fundamentals of artificial intelligence and how it is used in business. Students will learn about technologies like machine learning, neural networks, and natural language processing. The course shows how these tools are changing the way businesses operate, make decisions, and compete. Through real-world examples, students will discover how AI can help solve business problems and create new opportunities.
At the end of this course, students will be able to:
- Explain foundational concepts of AI, machine learning, and neural networks, and articulate their significance in the modern business landscape.
- Analyze real-world business scenarios to identify opportunities for AI integration, such as process automation, customer insights, or predictive analytics.
- Compare and contrast different machine learning models and algorithms, and justify the selection of appropriate models for specific use cases.
- Demonstrate an understanding of how AI systems utilize big data, including methods for classification, clustering, and decision-making support.
- Describe the components and functioning of artificial neural networks, and evaluate their application in areas like speech recognition or text analysis (e.g., NLP).
- Assess ethical and legal implications of AI technologies, including data privacy, algorithmic bias, and transparency in automated decision-making.
- Apply AI and data science principles to solve business problems using tools, flowcharts, and frameworks introduced in the course.
- Collaborate and communicate effectively with technical teams, leveraging AI knowledge to support cross-functional decision-making and IT initiatives.
1. Key concepts and terminology related to Artificial Intelligence (AI), Machine Learning (ML), neural networks, and data science within the context of modern business environments.
2. Common business applications of AI.
3. Various machine learning approaches (e.g., supervised, unsupervised, reinforcement learning) and assess their suitability for specific business problems.
4. Structure and function of artificial neural networks, and analyze their role in solving complex data-driven challenges, including image and language processing.
5. AI and big data, including how AI methods are used for data classification, clustering, and enhancing decision support systems.
6. Ethical, legal, and operational considerations in the implementation of AI technologies, including issues of bias, transparency, and data privacy.
7. AI concepts in real-world business scenarios, using case studies and practical exercises to analyze problems and propose solutions.
8. Applying foundational AI knowledge to communicate needs, interpret results, and support AI-related projects.