Lecture Notes

1. Lecture 1: Course Introduction (Slide)
2. Lecture 2: Review: Linear Algebra , Probability Distribution
3. Lecture 3: Simple Linear Regression
4. Lecture 4: Multiple Linear Regression
5. Lecture 5: Cost Function, Gradient Descent
6. Lecture 6: Gradient Descent, Polynomial Regression
7. Lecture 7: K Nearest Neighbors Regression, Kernel Regression
8. Lecture 8: Gaussian Process (Reading Material)
9. Lecture 9 & 10: Logistic Regression, K Nearest Neighbors Classification
10. Lecture 11: Support Vector Machine (SVM)
11. Lecture 12: Model Selection
12. Lecture 13: Regularization
13. Lecture 14: Dimensionality Reduction
14. Lecture 15: Decision Trees (Tutorial)
15. Lecture 16: Bagging, Boosting and Random Forests
16. Lecture 17: Clustering (K-means)
17. Lecture 18: Neural Networks (Basics)
18. Lecture 19: Convolution Neural Networks (Slide)
19. Lecture 20: Parameter Learning in Neural Networks (Slide, Back Propagation Reading Material)
20. Lecture 21: Recurrent Neural Networks
21. Lecture 22: Reinforcement Learning (Brief Introduction)
22. Lecture 23: Markov Decision Process

Code Samples

1. Google Colab Introduction
2. Python Basics
1. Python Practice 1
2. Python Practice 2
3. Video Tutorial(YouTube)
3. Linear Regression
1. Simple Version
2. Gradient Descent
3. Multiple Linear Regression
4. Lecture 4 Examples
4. Gaussian Process Regression
1. Simple Version
2. Detailed Version
5. Logistic Regression
6. KNN / K-means
1. Regression
2. Classification
7. Support Vector Machine (SVM)
8. Decision Tree
9. Principal Component Analysis (PCA)
1. PCA with Application Example
2. Reading Material
10. Sample Neural Network Code
1. From scratch Code (MLP)
2. Tensorflow Implimentation (MLP)

Homeworks

1. Homework 1
2. Homework 2
3. Homework 3
4. Homework 4
5. Homework 5

Project

1. Project Instructions
2. Mid-project Report Instructions
3. Final Project Report Instructions
4. Project Poster Presentation Template
5. Project Planning: Things to Look For
6. Sample Hands on Project

Other Necessary Materials

• Class Schedule
• Instructions for myUTSA apps
• Python Cheat Sheets
• Python Installation
• Recommended Textbook 
Tan, P. N., Steinbach, M., & Kumar, V. Introduction to Data Mining (2nd Edition). Pearson (January 4, 2018)., ISBN-13: 978-0133128901, ISBN-10: 0133128903.
• Reference books 
1. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer. (Available Online)
2. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. (Available Online)