Insights By Scott Ernst

Project Autodidact

Project Details: https://insightsbyse.com/projectautodidact/

Scott Ernst Bio: https://insightsbyse.com/aboutscotternst/

Project Contact: InsightsBySE@protonmail.com

Progress Report Scope (S01-M03-D01-AllParts)

Stage 1 of 4: Review of Mathematics, Probability, and Statistics

Module 3 of 3: Linear Algebra, Calculus, and Applications

Day 1 of 5: Vectors and Linear Algebra Applications

Parts 1 through 10: See below

Summary Of Goals Achieved

1. Reviewed similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability a scalar, vector, and matrix
2. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of vector addition and subtraction
3. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of scalar multiplication
4. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of scalar division
5. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for vector addition and subtraction
6. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar multiplication
7. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar division
8. Reviewed similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability of a dot product (aka scalar product) and cross product (aka vector product)
9. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of a dot product
10. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of a cross product
11. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a dot product
12. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a cross product
13. Reviewed similarities and differences between the definition, concepts, notation, terminology, components, properties, and applicability of vector magnitude and direction
14. Reviewed definition, concepts, notation, terminology, components, properties, and applicability of a vector’s magnitude and direction
15. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a vector’s magnitude and direction
16. Reviewed similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability of linear span, linear dependence, and linear independence
17. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for determining linear span, linear dependence, and linear independence
18. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for applying vectors to data transformations
19. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for applying vectors to data transformations
20. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for using vectors for feature engineering
21. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for using vectors for feature engineering
22. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for analyzing orthogonality in a dataset
23. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for using the Gram-Schmidt process for analyzing and transforming features in a dataset to achieve orthogonality
24. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for analyzing orthogonality in a dataset
25. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for using the Gram-Schmidt process for analyzing and transforming features in a dataset to achieve orthogonality
26. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for scalar projection, unit vector, and vector projection
27. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar projection, unit vector, and vector projection
28. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for applying vectors to clustering algorithms, including distance metrics and vector similarity
29. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for applying vectors to clustering algorithms, including distance metrics and vector similarity
30. Reviewed definition, concepts, notation, terminology, components, properties, and applicability for interpreting vectors in machine learning contexts, including feature space and vector operations
31. Reviewed how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for interpreting vectors in machine learning contexts, including feature space and vector operations

Part 1 of 10

Goal 1 Statement: Review similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability a scalar, vector, and matrix

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of vector addition and subtraction

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Goal 3 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of scalar multiplication

Goal 3 Plan: Read source materials

Goal 3 Work Product: None

Goal 3 Result: Completed

Goal 4 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of scalar division

Goal 4 Plan: Read source materials

Goal 4 Work Product: None

Goal 4 Result: Completed

Goal 5 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for vector addition and subtraction

Goal 5 Plan: Read source materials

Goal 5 Work Product: None

Goal 5 Result: Completed

Goal 6 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar multiplication

Goal 6 Plan: Read source materials

Goal 6 Work Product: None

Goal 6 Result: Completed

Goal 7 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar division

Goal 7 Plan: Read source materials

Goal 7 Work Product: None

Goal 7 Result: Completed

Part 2 of 10

Goal 1 Statement: Review similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability of a dot product (aka scalar product) and cross product (aka vector product)

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of a dot product

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Goal 3 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of a cross product

Goal 3 Plan: Read source materials

Goal 3 Work Product: None

Goal 3 Result: Completed

Goal 4 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a dot product

Goal 4 Plan: Read source materials

Goal 4 Work Product: None

Goal 4 Result: Completed

Goal 5 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a cross product

Goal 5 Plan: Read source materials

Goal 5 Work Product: None

Goal 5 Result: Completed

Part 3 of 10

Goal 1 Statement: Review similarities and differences between the definition, concepts, notation, terminology, components, properties, and applicability of vector magnitude and direction

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability of a vector’s magnitude and direction

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Goal 3 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for a vector’s magnitude and direction

Goal 3 Plan: Read source materials

Goal 3 Work Product: None

Goal 3 Result: Completed

Part 4 of 10

Goal 1 Statement: Review similarities and differences among the definition, concepts, notation, terminology, components, properties, and applicability of linear span, linear dependence, and linear independence

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for determining linear span, linear dependence, and linear independence

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Part 5 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for applying vectors to data transformations

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for performing the tasks and calculations for applying vectors to data transformations

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Part 6 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for using vectors for feature engineering

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for using vectors for feature engineering

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Part 7 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for analyzing orthogonality in a dataset

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for using the Gram-Schmidt process for analyzing and transforming features in a dataset to achieve orthogonality

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Goal 3 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for analyzing orthogonality in a dataset

Goal 3 Plan: Read source materials

Goal 3 Work Product: None

Goal 3 Result: Completed

Goal 4 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for using the Gram-Schmidt process for analyzing and transforming features in a dataset to achieve orthogonality

Goal 4 Plan: Read source materials

Goal 4 Work Product: None

Goal 4 Result: Completed

Part 8 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for scalar projection, unit vector, and vector projection

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for scalar projection, unit vector, and vector projection

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Part 9 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for applying vectors to clustering algorithms, including distance metrics and vector similarity

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for applying vectors to clustering algorithms, including distance metrics and vector similarity

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed

Part 10 of 10

Goal 1 Statement: Review definition, concepts, notation, terminology, components, properties, and applicability for interpreting vectors in machine learning contexts, including feature space and vector operations

Goal 1 Plan: Read source materials

Goal 1 Work Product: None

Goal 1 Result: Completed

Goal 2 Statement: Review how Python, Julia, R, SQL, and other computer programming languages and applications are utilized for interpreting vectors in machine learning contexts, including feature space and vector operations

Goal 2 Plan: Read source materials

Goal 2 Work Product: None

Goal 2 Result: Completed