I taught a semester-long machine learning elective honors course for the first time at Oregon Episcopal School in the fall of 2019. Here was the syllabus:
| Date | Rotation Day | Topic | Homework |
|---|---|---|---|
| Sept 9 | F | What is Machine Learning? | |
| Sept 10 | E | Pandas | Download Anaconda and Watch Andrew Ng intro videos. Write discussion post. |
| Sept 11 | D | Pandas | Pandas Basics Assignment – Stop before Missing Data |
| Sept 13 | B | Pandas | Pandas Basics Assignment - Finish |
| Sept 16 | A | Pandas | Pandas Pregnancy Problem Set |
| Sept 17 | F | Pandas | Rollercoaster Project |
| Sept 18 | E | Pandas | Rollercoaster Project |
| Sept 19 | D | GitHub & Blog | Rollercoaster Project |
| Sept 23 | B | Linear Regression | Get GitHub Repo and Jekyll Blog Working & Read two ML ethics articles |
| Sept 24 | A | Linear Regression | Finish Linear Regression Problem Set |
| Sept 25 | F | Gradient Descent | Watch GD videos |
| Sept 26 | E | Gradient Descent | Python Iteration HW |
| Sept 27 | D | Gradient Descent | Gradient Descent Algorithm HW |
| No HW Weekend | |||
| Oct 1 | B | Multiple Regression | Gradient Descent Stochastic Gradient Descent Assignments |
| Oct 2 | A | Multiple Regression | In class: Multiple Regression Notebook and semilog |
| Oct 3 | F | Multiple Regression | Hw: Semilog exercise and Categorical Variabes Notebook, In class: Syphilis Lab link1 link2 |
| Oct 4 | E | Regularization | Syphilis Lab Due |
| Oct 7 | D | Regularization | Cars Problem Set |
| Oct 9 | B | Regression Project | Test Train Split And Polynomial Regression Notebooks |
| Octoberim 10-14 no classes | |||
| Oct 15 | F | Regression Project | Regularization, Cross-Validation Notebooks Regression Project |
| Oct 16 | E | Regression Project | Regression Project |
| Oct 17 | D | Regression Project | Regression Project |
| Oct 21 | B | Regression Project | Regression Project |
| Oct 22 | A | Classification | Regression Project |
| Oct 23 | F | Classification | Naïve Bayes Assignment |
| Oct 24 | E | Classification | Naïve Bayes Problem Set |
| Oct 25 | D | Classification | Logistic Regression Assignment |
| Oct 28 | C | Classification | Error Classification Assignment |
| Oct 29 | B | Classification | K Nearest Neighbors Assignment |
| Oct 31 | A | Classification | Classification Project: College Analysis |
| Nov 1 – Q1 ends | F | Classification | Classification Project: College Analysis |
| Nov 4 | E | Classification | Classification Project: College Analysis |
| Nov 5 | D | Classification | Classification Project: College Analysis |
| Nov 7 | B | Classification | Classification Project: College Analysis |
| Nov 8 | A | Classification | Classification Project: College Analysis |
| Nov 11 | F | Classification | Classification Project: College Analysis |
| Nov 12 | E | Classification | Classification Project: College Analysis |
| Nov 13 | D | Unsupervised Learning | K Means Clustering Assignment |
| Nov 15 | B | Unsupervised Learning | K Means Clustering Problem Set |
| Nov 18 | A | Linear Algebra | Linear Algebra Packet through Page 4 |
| Nov 19 | F | Linear Algebra | Linear Algebra HW through Page 8 |
| Nov 20 | E | 21-1 No school | |
| Dec 2 | F | Linear Algebra | Linear Algebra HW finish through page 16 |
| Dec 3 | E | Linear Algebra | Linear Algebra HW finish through pg. 19 |
| Dec 4 | D | Linear Algebra | Linear Algebra HW finish through pg. 20 |
| Dec 6 | B | Linear Algebra | Linear Algebra HW finish through pg. 24, In class: finish through pg. 28 |
| Dec 9 | A | Linear Algebra | Take Home Test Due |
| Dec 10 | F | Linear Algebra | Finish Linear Algebra packet & Image Compression notebook |
| Dec 11 | E | Recommender Systems | Curse of Dimensionality & PCA Intro Notebook 0 |
| Dec 12 | D | Recommender Systems | Recommender Systems Notebook 1 |
| Dec 16 | B | Recommender Systems | Netflix Recommender Movie Problem Set Notebook 2 |
| Dec 17 | A | Natural Language Processing | NLP Intro Notebook 1 |
| Dec 18 | F | Natural Language Processing | NLP Applications Notebook 2 |
| Dec 19 | E | Natural Language Processing | Celebrity Gossip & D3 Visualization Notebook 3 |
| Dec 20 | D | Winter Break | |
| Jan 7 | B | Final Project | Final Project |
| Jan 8 | A | Final Project | Final Project |
| Jan 9 | F | Final Project | Final Project |
| Jan 10 | E | Final Project | Final Project |
| Jan 13 | D | Final Project | Final Project |
| Jan 15 | B | Final Project | Final Project |
| Jan 16 | A | Final Project | Final Project |
| Jan 17 | F | 20 MLK day, 21-24 Assessments, 27 no school |
Here were the materials that I drew heavily from:
- Andrew Ng's Machine Learning Course
- Python Data Science Handbook by Jake VanderPlas
- Python for Data Analysis by Wes McKinney
- Machine Learning For Hackers by Drew Conway & John White
- Life is Linear by Tim Chartier for the linear algebra projects unit
Some websites that I always find helpful - besides Stack Overflow obviously :) - are: