Course Title: Management of Artificial Intelligence Techniques
Course Description:
This course offers a comprehensive exploration of Artificial Intelligence (AI) techniques and their strategic applications within organizations. Through a case analysis approach, students will gain insights into the practical adoption and management of AI technologies, including machine learning, natural language processing, computer vision, and large language models.
The course emphasizes:
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AI Fundamentals and Techniques: Understanding core AI concepts and tools, their potential, and limitations.
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Case Analysis Methodology: Investigating real-world scenarios to evaluate the implementation, challenges, and benefits of AI in diverse industries.
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Artificial Intelligence in Action: Examining the transformative impact of tools like GPT, Bayes networks, and deep Bayesian learning and their application in decision-making, customer engagement, and process optimization.
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Ethical and Strategic Considerations: Addressing ethical challenges, data privacy concerns, and strategies for sustainable AI integration.
Students will engage in hands-on activities, group discussions, and project work to develop a robust understanding of how AI can drive innovation, enhance efficiency, and solve complex organizational challenges. By the end of the course, participants will be equipped to critically assess AI opportunities and lead initiatives that harness the power of AI to achieve organizational goals.
Week 1: Introduction to Artificial Intelligence
- Overview of AI: History, Trends, and Applications
- Key Concepts: Machine Learning, NLP, Computer Vision, and Expert Systems
- Introduction to Case Analysis Framework
Week 2: AI in Business and Industry
- AI Use Cases Across Sectors: Finance, Healthcare, Manufacturing, and Retail
- Identifying AI Opportunities: Strategic Alignment and Business Value
- Case Study: AI-Driven Customer Experience
Week 3: Machine Learning Techniques
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
- Algorithms: Decision Trees, Neural Networks, Clustering
- Case Study: Predictive Analytics in E-commerce
Week 4: Natural Language Processing (NLP)
- Fundamentals: Tokenization, Parsing, Sentiment Analysis
- Large Language Models: Applications and Trends
- Case Study: NLP in Chatbots and Virtual Assistants
Week 5: Computer Vision Applications
- Image Recognition, Object Detection, and Video Analysis
- AI in Quality Control and Surveillance Systems
- Case Study: Computer Vision in Healthcare Diagnostics
Week 6: Ethics and Governance in AI
- Ethical Challenges: Bias, Fairness, and Transparency
- Regulatory Frameworks and Guidelines
- Case Study: Ethical Dilemmas in AI Implementation
Week 7: AI Integration and Implementation
- Building AI Teams: Roles and Skills
- Change Management in AI Adoption
- Case Study: Implementing AI in Large-Scale Organizations
Week 8: Data Management and AI
- Importance of Data Quality, Privacy, and Security
- Data Governance Frameworks for AI Projects
- Case Study: Overcoming Data Challenges in AI Implementation
Week 9: AI Tools and Platforms
- Overview of Popular AI Platforms: TensorFlow, PyTorch, Azure AI
- Selecting the Right Tools for Organizational Needs
- Workshop: Hands-On Exploration of AI Tools
Week 10: Evaluating AI Performance
- Metrics and KPIs for AI Effectiveness
- Continuous Learning and Model Optimization
- Case Study: Performance Assessment of Predictive Models
Week 11: AI and Organizational Strategy
- Aligning AI Projects with Strategic Goals
- Long-Term Planning for AI Integration
- Case Study: Strategic Use of AI in Supply Chain Management
Week 12: Emerging Trends in AI
- AI Innovations: Generative AI, Edge Computing, and Autonomous Systems
- The Future of Work with AI
- Case Study: The Business Impact of Emerging AI Technologies
Week 13: Group Projects and Presentations
- Developing an AI Adoption Plan for a Hypothetical Organization
- In-Class Presentations: Peer Review and Feedback
- Preparation for Final Exam
Week 14: Review and Discussion
- Comprehensive Review of Key Topics
- Addressing Questions and Challenges
- Best Practices in AI Management and Strategy
Week 15: Final Exam
- Comprehensive Assessment Covering All Course Topics
- Format: Case Analysis, Short Answers, and Multiple Choice
Course Evaluation
- Participation: 10%
- Assignments: 20%
- Group Project: 25%
- Final Exam: 45%
Additional Notes
- Weekly readings, assignments, and case studies will be provided.
- Active participation in discussions and workshops is expected.
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