Course Info
Artificial Intelligence (AI) has rapidly evolved from its nascent stages to a driving force of the modern technological landscape. This course provides executives with an insightful overview of AI’s origins, its current applications, and future potential. Understanding the difference between Artificial Narrow Intelligence and General Intelligence is crucial for grasping AI capabilities. Executives will learn how predictive and prescriptive analytics shape decision-making processes.
The recent surge in AI development can be attributed to advancements in Big Data, Cloud, and Mobile Computing. Machine Learning and Deep Learning form the backbone of AI functionalities such as image and speech recognition, NLP, and data pattern recognition. In business, AI’s application spans across various domains including logistics, customer service, robotics, and IoT, revolutionizing traditional operations.
Executives are guided through model creation using various algorithms and validation techniques essential for reliable predictions and classifications. The importance of data visualization in interpreting complex results is emphasized. Real-world case studies illustrate how AI is integrated into Manufacturing, Finance, and Healthcare sectors while highlighting ethical considerations and strategies for future-proofing businesses.
What Will I Learn From This Course?
Understand the historical context and evolution of Artificial Intelligence.
Recognize the impact of Big Data, cloud computing, and mobile technology on AI advancements.
Identify the differences between narrow and general AI, as well as predictive versus prescriptive models.
Prepare for the ethical implications of AI while building strategies to integrate it within your organization.
Learn how to effectively visualize data to communicate findings and inform decision-making processes.
Apply AI frameworks to real-world business scenarios like logistics management and customer service.
Gain proficiency in machine learning and deep learning concepts such as clustering, NLP, optimization.
Develop skills in model creation using regression analysis, classification algorithms, and clustering methods.
Target Audience
Engineers & Executives
Methodology
- Lecture
- Slides
- Case Studies
- Labs
- Group Discussion
Course Outline for This Programme
- Origins of Artificial Intelligence and a brief history of the AI revolution
- The AI landscape
- AI and Digital Transformation
- Artificial Narrow Intelligence vs. Artificial General Intelligence
- Predictive vs. Prescriptive AI
- The role of Big Data
- Cloud Computing and AI
- Mobile Computing and AI
- Image Recognition
- Speech Recognition
- Search
- Clustering
- NLP
- Optimization
- Prediction
- Understanding Data – Pattern Recognition
- Applying the AI framework
- Employee on-boarding
- Invoice processing
- Payments
- Logistics and SCM
- Customer Service and Ticketing
- Robotics
- IoT
- Prediction: linear regression, nonparametric regression
- Forecasting: ARIMA and RNN’s.
- Classification: logistic regression, decision trees, SVM’s.
- Clustering: k-means, hierarchical clustering.
- Supervised vs. unsupervised vs. semi-supervised learning.
- Dimension reduction: principal components
- Languages and environments (e.g. R, Python) and standards (PMML).
- Practical and effective visualization: beyond bar charts.
- Finding the unexpected: the role of visualization in exploratory analysis
- Communicating findings: the role of visualization in communicating Data Science outputs.
- Standard tools: R, Tableau, D3
- How AI is enhancing customer engagement
- How AI is optimizing business processes – BPA
- How AI is generating insights – pattern recognition
- Developing the AI Business Case
- Understanding Change Management
- Developing your AI Road Map
- Creating your AI Strategy
- Build vs Buy vs Platform
- Creating your first AI product
- Understanding Data Training and Tuning
- The challenge of poor data and unintended bias
- Becoming over-dependent on AI
- Identify which areas of your business are suitable for AI
- The role of high-level management in enabling AI decisions
- Creating a data-driven business and the infrastructure to deliver it
- The ethics of AI
- Case studies in Manufacturing, Finance and Healthcare
- Future-proofing your Business
- The Next opportunities for AI
