**Lab:**Monday, 3-4PM, Evans B6 (118)**Disc:**Wednesday, 3-4PM, Etcheverry 3107 (118A)**Office Hours:**Tuesday, 2-3PM (Jacobs 220); Thursday 12-1PM (Evans 426)**Anonymous Feedback:**Fill out this form to give me anonymous feedback on my teaching.

Week | |
---|---|

2 | slides, marked-up worksheet, video |

3 | slides, marked-up worksheet, video |

4 | N/A |

5 | marked-up worksheet, video, note on transformations |

6 | video, note on Kernel Density Estimation |

7 | note on PCA (raw) |

8 | slides, supplemental notebook (raw) |

9 | slides, marked-up worksheet, video |

10 | slides |

11 | slides, marked-up worksheet, video |

12 | N/A (midterm) |

13 | slides |

14 | N/A (Thanksgiving) |

15 | slides, marked-up worksheet |

**Review Slides:**

- Loss and Risk Midterm 1 Review
- Logistic Regression, Classification, Evaluating Classifiers Final Review

**Exam Walkthrough Videos:**

- Fall 2017 Midterm Walkthrough (FA18)
- Spring 2018 Final Walkthrough (FA18)
- Summer 2019 Midterm Walkthrough (SU19)
- Spring 2019 Midterm 1 Walkthrough (FA19)
- Spring 2019 Midterm 2 Walkthrough (FA19)

**Lectures:**

Here are some of the resources I created over the past few semesters for Data 100. I will re-link anything that becomes relevant under “Fall 2019”, so don’t worry about searching through this if you’re a current student.

**Discussion Resources:**

- Week 1: slides, marked-up worksheet
- Week 3: marked-up worksheet
- Week 5: marked-up worksheet
- Week 6: discussion slides, marked-up worksheet
- Week 10: notes

**Discussion Walkthrough Videos:**

- Discussion 6 Walkthrough (SP19)
- Discussion 7 Walkthrough (SP19)
- Discussion 8 Walkthrough (SP19)
- Discussion 9 Walkthrough (SP19)
- Discussion 12 Walkthrough (SP19)

**Review Slides**:

- Midterm 2 Review Slides (SP19)
- Final Review Slides (regression, BV, regularization) (exam mark-up) (SP19)
- Final Review Slides (classification, logistic regression) (SP19)
- Final Review Slides (sampling, bootstrapping, confidence intervals) (notebook) (worksheet) (SP19)

**Other Notes**:

- Connections between different approaches to linear regression
- Derivation of the Sigmoid Function
- Parameters Diverging to Infinity in Logistic Regression
- Walkthrough notebook on Kernel Density Estimation (raw)
- Notebook on Transformations (raw)
- Note on Eigenvalues vs. Singular Values