GPMS TRANSPORTAL APPLIED AI COURSES

• A key aspect of achieving a working AI is efficient Data, prepared well to achieve the outcome that is planned.
• Level - Basic - Choice - Specialisation
• Prerequisites - Introduction to Computer Vision or any introduction course.
• Assessments - Micro Assessment weekly, Full Assessment last week.

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Week 1 - What is Data and Types

Learning Outcome

Students should be able to understand how data is interpreted by computers of various types, and the crucial role data plays in building intelligence and understanding.

1.1 Data Types, and Role in Intelligence Creation
1.2 Refactoring or transforming Data to be more computer appropriate
1.3 Evaluating Data Readiness, and Correctness for various purposes

Practical Component

Practical Demonstrations on Impact of Data Usage of different types on various Use Cases.

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Week 2 - How is Data Collected Prepared

Learning Outcome

Here the focus is to ensure appropriate collection and preparation strategies.

2.1 Collection of Data Strategies - Generative Data, and more.
2.2 Labelling and Preparation of Various Data Formats.
2.3 Evaluating Correctness of Labelling.

Practical Component

The impact of incorrect labels on accuracies, and how to translate to higher accuracy with different labels.

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Week 3 - Integrating different forms of Data

Learning Outcome

How to integrate and blend various forms of differently prepared Data for creating larger more plausible data sets.

3.1 The effects of disjoint Data on outcomes.
3.2 Fixing the rift in algorithm intelligence through blending techniques.
3.3 Evaluating the effectiveness, and greater potential of accuracy improvement.

Practical Component
Practical Demonstrations showcasing the effects of various Data decisions showcasing the importance and elucidating the problem identifying skill.

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Week 4 - Assessing if Data is right for goal

Learning Outcome

Assessing Data Correctness for various purposes and accuracies.

4.1 Data need for various tasks, and type of label requirements.
4.2 Task based Data Preparation Techniques.
4.3 Assessing if Data Improvement can improve accuracy.

Practical Component
Practical Understanding of how much data is enough, and the ways of identifying Data need in algorithm accuracy improvement, and data sufficiency.

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Week 5-When Data is Not Enough

Learning Outcome

Creating Data to fit, and allow for better learning through guided data creation.

5.1 Creating Data of relevant forms through generative AI, strategies to ensure benefit.
5.2 Switching to lower complexity algorithms, and their Data needs and outcome issues.
5.3 Identifying cause of problem, and potential fix.

Practical Component
Practical Demonstrations of implications and solutions to less data based unacceptable results, and methods to improve them.

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Week 6-Project and Assessment

Learning Outcome

Here students work on a custom project of choice for data preparation for a particular use case, and follow the peer reviewed process of evaluation, strategy and outcome.

6.1 Data Selection, Preparation, Algorithm Selection, and Initial Training Day.
6.2 Initial Self Assessment, and Peer Assessment and review of outcomes.
6.3 Final Presentations and Scoring of outcome Achieved.

Practical Component
Practical Demonstrations of student made models at various stages, with fixes, and live demonstrations to a committee of peers or prospective recruiters.

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