Mathematics & Computer Science
Salisbury University
Homework 0 - Come to office hours and say hello. Due 3 September.
Project 1 - Due: 24 October
Project 2 - Due: 15 December
Exam 1: 7 October
Exam 2: 11 November
Final Exam (Presentation): 15 December 10:45am-1:15pm
| Week | Topics | Readings | Files |
|---|---|---|---|
| 1 | Introduction to Python, Jupyter, data processing | Grus Chapter 2 |
PythonIntro.ipynb Numpy.ipynb Lab 1 |
| 2 | Functions, OOP, Plotting | Grus Chapter 3 |
PandasAndPlotting.ipynb Functions.ipynb housing.csv |
| 3 | Data loading, manipulation, and presentation | Grus Chapter 3, Tufte Ch. 6-7 |
Lab 2 Class Notes Adults.ipynb Lab1Notes.ipynb |
| 4 | Advanced data manipulation | Pandas documentation for: pivot, groupby, sort_values | |
| 5 | One-dimensional statistics, probability intro | Grus Ch. 4, 5 Numpy documentation |
OneDimensionalStatistics.ipynb stats.py Iris.ipynb |
| 6 | One-dimensional statistics, Exam 1 | Grus Ch. 6 |
Project 1 Posted Exam1Review.ipynb |
| 7 | Probability, events, random Variables, continuous distributions | Grus Ch. 6, 7 |
Lab 3 Probability.ipynb |
| 8 | Estimators, Independence, conditional probability | Grus Ch. 6,7 |
Probability.ipynb |
| 9 | Hypothesis testing, confidence intervals | Grush Ch 7 |
Lab 4 |
| 10 | Introduction to Machine learning | Grush Ch 7 | Lab 5 |
| 11 | Visualization with Machine Learning, Exam 2 | Grus Ch 11 | MLUsage.ipynb |
| 12 | Gradient descent, simple linear regression, perceptrons | Grus Ch 13, 14 | |
| 13 | Neural networks, backpropagation, SHAP values | Grus Ch 18 | Lab 6 |