Copies of the classnotes are on the internet in PDF format as given below. The notes and supplements may contain hyperlinks to posted webpages; the links appear in red fonts. The "Proofs of Theorems" files were prepared in Beamer. The "Printout of Proofs" are printable PDF files of the Beamer slides without the pauses. These notes have not been classroom tested and may have typographical errors.

"Mathematics of Data Analytics" is not yet a class at ETSU, but it is related to the M.S. program in Applied Data Science (MSADS). Details on this program can be found on the Masters in Applied Data Science Program Overview webpage (accessed 3/24/2024).

Preface. Preface notes

1. Ranking.

• 1.1. Motivation: Google Problem. Section 1.1 notes
• 1.2. Results (stochastic matrices, linear programming, the duality theorems). (partial) Section 1.2 notes
• Supplement. Examples, Exercises, and Proofs from Section 1.2. Beamer file of Section 1.2 examples and proofs
• Supplement. Printouts of Examples, Exercises, and Proofs from Section 1.2. Print file of Section 1.2 examples and proofs
• 1.3. Case Study: Brand Loyalty.
• 1.4. Exercises.
• Study Guide 1.

2. Online Learning.

• 2.1. Motivation: Portfolio Selection.
• 2.2. Results.
• 2.3. Case Study: Expert Advice.
• 2.4. Exercises.
• Study Guide 2.

3. Recommendation Systems.

• 3.1. Motivation: Netflix Price.
• 3.2. Results.
• 3.3. Case Study: Latent Semantic Analysis.
• 3.4. Exercises.
• Study Guide 3.

4. Classification.

• 4.1. Motivation: Credit Investigation.
• 4.2. Results.
• 4.3. Case Study: Quality Control.
• 4.4. Exercises.
• Study Guide 4.

5. Clustering.

• 5.1. Motivation: DNA Sequencing.
• 5.2. Results.
• 5.3. Case Study: Topic Extraction.
• 5.4. Exercises.
• Study Guide 5.

6. Linear Regression.

• 6.1. Motivation: Econometric Analysis.
• 6.2. Results.
• 6.3. Case Study: Capital Asset Pricing.
• 6.4. Exercises.
• Study Guide 6.

7. Sparse Recovery.

• 7.1. Motivation: Variable Selection.
• 7.2. Results.
• 7.3. Case Study: Compressed Sensing.
• 7.4. Exercises.
• Study Guide 7.

8. Neural Networks.

• 8.1. Motivation: Nerve Cells.
• 8.2. Results.
• 8.3. Case Study: Spam Filtering.
• 8.4. Exercises.
• Study Guide 8.

9. Decision Trees.

• 9.1. Motivation: Titanic Survival.
• 9.2. Results.
• 9.3. Case Study: Chess Engine.
• 9.4. Exercises.
• Study Guide 9.

10. Solutions.

• 10.1. Ranking.
• 10.2. Online Learning.
• 10.3. Recommendation Systems.
• 10.4. Classification.
• 10.5. Clustering.
• 10.6. Linear Regression.
• 10.7. Sparse Recovery.
• 10.8. Neural Networks.
• 10.9. Decision Trees.

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