Part 1: Introduction

1. Working definitions: AI, Machine Learning, Deep Learning,  Data Science & Big Data 
2. State of AI: summarizing major analysts’ statistics & predictions
3. Summarizing AI misinformation
4. Effects on the job market
5. Today’s AI use  cases
   • Where it works well
   • Where it doesn’t work well
6. What do high profile uses have in common?
7. Addressing legitimate concerns & risks

Case study break: We will introduce the class to three real-world use cases – one in finance, one in health science, and one in general operations. In small groups, you will discuss implications of the cases and see if you and your peers can spot any parallel opportunities in your own business.

Part 2: The Big Data Prerequisite

1. Evaluating your big data practice
2. State of tools – understanding intelligent big data stacks
   • Visualization and Analytics
   • Computing
   • Storage
   • Distribution and Data Warehousing
3. Strategically restructuring enterprise data architecture for AI
4. Unifying data engineering practices
5. Datasets as learning data
6. Defeating Bias in your Datasets
7. Optimizing Information Analysis
8. Utilizing the IoT to amass a large amount of data

Part 3: Implementing Machine Learning

1. Examine pillars of a practicing AI team
   • Business case
   • Domain expertise
   • Data science
   • Algorithms
   • Application integration
2. Bettering Machine Learning Model Management
3. State of tools – understanding intelligent machine learning stacks
4. Machine Learning Methods and Algorithms
   • Decision Trees
   • Support Vector Machines
   • Regression
   • Naïve Bayes Classification
   • Hidden Markov Models
   • Random Forest
   • Recurrent Neural Networks
   • Convolutional Neural Networks
5. Developing Validation Sets
6. Developing Training Sets
7. Accelerating Training
8. Encoding Domain Expertise in Machine Learning
9. Automating Data Science
10. Deep Learning

Example: TensorFlow – We will take a look at Google’s TensorFlow as a tool for integrating machine learning features.  We’ll come away from the exercise with an understanding of the programming skills needed to leverage TensorFlow and the impacts of normal application workflow.

Part 4: Creating Concrete Value

1. Opportunities for automation
2. Understanding automation vs. job displacement vs. job creation
3. Finding hidden opportunities through improved forecasting
4. Production and operations
5. Adding AI to the Supply Chain
6. Marketing and Sales Applications
   • Predict Customer Behavior
   • Target Customers Efficiently
   • Manage Leads
   • AI-powered content creation
7. Enhancing UX and UI
8. Next-Generation Workforce Management
9. Explaining Results

Use case breakout: Scoring the criteria for three potential applications. In groups, we’ll evaluate application use cases for machine learning: Medical imaging, electronic medical records, and genomics. We’ll grade each use case based on a scorecard for the following:

• Quantity of data
• Quality of data
• ML techniques

Part 5: Machine intelligence as part of the customer experience

1. IoT and the role of machine learning
2. Projects based on customer & user needs
3. Handling customer inquiries with AI
4. Creating empathy-driven customer facing actions
5. Narrowing down intent
6. AI as part of your channel strategy

Part 6: Machine Intelligence & Cybersecurity

1. How can ML help with security?
   • Advance cyber security analytics
   • Developing defensive strategies
   • Automating repetitive security tasks
   • Close zero-day vulnerabilities
2. How are attackers leveraging ML and AI?
3. Building up trust towards automated security decisions and actions
4. Automated application monitoring as a security layer
5. Identifying Vulnerabilities
6. Automating Red Team/Blue Team Testing Scenarios
7. Modeling AI after previous security breaches
8. Automating and streamlining Incident Responses
9. How use deep learning AI to detect and prevent malware and APTs
10. Using natural language processing
11. Fraud detection
12. Reducing compliance testing & cost

Part 7: Filling the Internal Capability Gap

1. Assessing your technological and business processes
2. Building your AI and machine learning toolchain
3. Hiring the right talent
4. Developing talent
5. How to make AI more accessible to people who are not data scientists
6. Launching pilot projects

Part 8: Conclusion and Charting Your Course

1. Review
2. Charting Your Course
   • Establishing a timeline
3. Open Discussion