Grace Mansion First floor, 25, Infantry Road Bangalore
9:30 am - 6:00 pm
Toll free: 1800-258-2010
[email protected]
Grace Mansion First floor, 25, Infantry Road Bangalore
9:30 am - 6:00 pm
Toll free: 1800-258-2010
E-PashuHaat Transportal

GPMS TRANSPORTAL APPLIED AI COURSES

AI

Course 25: Artificial Intelligence for Managers

Duration - 72 Hours
  • Initial Course for Students focused on Managing Artificial Intelligence
  • Prerequisites - None, No Coding knowledge necessary, will be built along with the course.
  • Assessments - Micro Assessment after each Bi-weekly segment, Full Assessment after Midterm, and Completion.
    • Week 1 & 2 - The Role of Artificial Intelligence in a Business
    Learning Outcome

    Students should be able to understand key aspects of Artificial Intelligence relating to various businesses, and the role of Artificial Intelligence in making businesses succeed.

    1.1 Difference between technology and Artificial Intelligence - Examples
    1.2 Computer Vision, Image Processing, Generative AI with applications.
    1.3 The vision for businesses of the future - AI first businesses.
    1.4. Key terminology of AI.

    Practical Component
    Practical Demonstrations on zones of practical use cases, and ways they've been applied.
    Week 3 & 4 - Planning an Artificial Intelligence Strategy
    Learning Outcome

    Students should be able to understand the end to end planning, and execution of an Artificial Intelligence transformation, and management strategy.

    1.5 The readiness factor.
    1.6 Pyramid method of effective adoption, and development.
    1.7 Choosing between Build, Purchase, or External Development
    1.8 Acceptance Criteria, and Assessment Team.

    Practical Component
    Practical Demonstrations on zones of practical use cases, and ways they've been applied.
    Week 5 & 6 - Identifying an Artificial Intelligence System
    Learning Outcome

    Students should be able to understand and identify whether a system is an AI system through the Dataviv Strategy, and uncover the underlying aspects of the system. They will also be able to reason about why Artificial Intelligence was needed in this situation, and how is it better than other primitive approaches.

    1.9   Key Components of An Artificial Intelligence
    1.10 Contrasting the same with Coding, RPA, and other software.
    1.11 The Advantage of Artificial Intelligence over others.

    Practical Componenet
    Practical Demonstrations on zones coding related performance, and AI related performance and key differences.
    Week 7 & 8 - Evaluating Readiness for Artificial Intelligence
    Learning Outcome

    Students should be able to understand why everyone says Garbage in and Garbage Out, but also understand that solely data is not the key to intelligence, the right intelligence capture is quintessential as well. They will learn hands on how to prepare data for various capture formats, and be adept at basis data preparation decision making.

    1.12 Is my data collection as per requirements.
    1.13 Team and Adoption readiness.
    1.14 Data Security, and protocols.
    1.15 Success Criteria

    Practical Component
    Practical Demonstrations on zones of practical use cases, and ways they've been applied.
    Week 9 & 10 - Assessing an Artificial Intelligence System
    Learning Outcome

    Here students after learning the underlying importance of edge cases, key elements of an Artificial Intelligence, and how we can effectively assess if it is conducting its tasks appropriately, along with need for upgrade and lifetime assessment

    1.16 Edge Cases, and Areas of fault (Impact)
    1.17 Speed (Real Time), and efficacy (Accuracy) of response.
    1.18 Angle, Lighting, and training data dependency
    1.19 Extensibility and usability

    Practical Component
    Practical Demonstrations on zones of how these assessments of various models happens.
    Week 11 & 12 - Rectification and Improvement of Faults
    Learning Outcome

    Here students will be able to understand various faults that occur in systems, and will be able to assess and conduct proper and appropriate fixes.

    1.20 Improve Quantum of Data to Improve Accuracy
    1.21 Retrain to improve accuracy
    1.22 Choose a different model architecture or approach.
    1.23 Input Data versus training data mismatch.
    1.24 Upgradation or self improvement.

    Practical Component
    Practical Demonstrations on zones of how applying these changes affects outcome of models.
    Combined Learning Outcome Achieved

    Students will be able to understand and identify Artificial Intelligence from the lens of a manager, and operator to understand key areas of work, failure, and use cases to identify when, where, and how to incorporate artificial intelligence to solve problems from a birds eye view. It combines management principles with practical learning to ensure they understand the working, are able to guide teams, and ensure readiness.