Mathematics & Computer Science

Salisbury University

Lecture: Tu Th 11am-12:15pm HS 107

Lab: F 11-11:50am HS 143

Lecturer: Joe Anderson

Office: 128 Henson Hall

Office Hours: M 10am-12pm, W 9-10am, Th F 8-9am

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 |