Pomona College

Fall 2023

Machine learning focuses on discovering patterns in and learning from data. This course is an introduction to the most common problems in machine learning and to the techniques used to tackle these problems. The course will focus not only on how and when to use particular approaches, but also the details of how those approaches work.

**instructor:** Dave Kauchak

**e-mail:** [first_name][last_name]@pomona.edu

**office hours:**

Monday, 3-4pm

Wednesday, 3-4pm

Thursday, 2:30-4pm

and by appointment

**mentors:** Eshaan Lumba, Saatvik Kher, Sam Malik

**hours:**

Thursday, 7-9pm

Friday, 6-8pm

Sunday, 7-9pm

**time: **T/Th 1:15-2:30pm

**location: ** Edmunds 114

**web page: **http://www.cs.pomona.edu/classes/cs158/

**textbook:**

- A Course in Machine Learning. Hal Daumé III. Available online.
- Other material, linked below

**Other information:**

- Administrative material
- Assignment submission: gradescope
- Discussion: slack

Date | Topic | Reading | Assignment | Due |
---|---|---|---|---|

8/29 | introduction (ppt) | Ch 1-2 | Assignment 1 (.tex) | 9/1 @ 5pm |

8/31 | decision trees (ppt) | Tan Ch 4.3-4.3.5 | ||

9/5 | geometric view of data (ppt) | Ch 3 (3.4 optional) | Assignment 2 (.tex) | 9/10 @ 11:59pm |

9/7 | perceptron (ppt) | Ch 4 | ||

9/12 | features (ppt) | Ch 5-5.4 | Assignment 3 (.tex) | 9/17 @ 11:59pm |

9/14 | evaluation (ppt) | Ch 5.5-5.9 | ||

9/19 | imbalanced data (ppt) | Ch 6-6.1 | Assignment 4 (.tex) | 9/24 @ 11:59pm |

9/21 | beyond binary classification (ppt) | Ch 6-6.3 | ||

9/26 | gradient descent (ppt) | Ch 7-7.5 (7.6 optional) | Assignment 5 (.tex) | 10/1 @ 11:59pm |

9/28 | regularization (ppt) | |||

10/3 | large margin classifiers (ppt) | Ch 7.7 | Assignment 6 (.tex) | 10/13 @ 11:59pm |

10/5 | SVM lab | |||

10/10 | probability basics (ppt) | Optional: Movallen pgs 7-23 | ||

10/12 | probabilistic models (ppt) | Ch 9-9.5 | ||

10/17 | Fall break | |||

10/19 | priors and logistic regression (ppt) | Ch 9.6-9.7 | Assignment 7 (.tex) | Part A: 10/22 @ 11:59 Part B: 10/29 @ 11:59pm |

10/24 | neural networks (ppt) | Ch 10 | ||

10/26 | backpropagation (ppt) | Optional: backprop example | ||

10/31 | deep learning (ppt) | word vectors | Assignment 8 (.tex) | 11/5 @ 11:59 |

11/2 | big data (ppt), hadoop | |||

11/7 | MapReduce | Assignment 9 (.tex) | 11/12 @ 11:59pm | |

11/9 | MapReduce conclusions, final project discussion | |||

11/14 | ensemble learning (ppt) | final project | ||

11/16 | k-means (ppt) | Ch 3.4, 15-15.1 | ||

11/21 | final project work session | |||

11/23 | Thanksgiving | |||

11/28 | clustering (ppt) | Ch 16 | ||

11/30 | ML ethics | paper 1, paper 2, article 1, article 2 | ||

12/5 | project presentations |

**Exam schedule:**

- Midterm 1: during the week of 10/2
- Midterm 2: during the week of 11/13
- Final: Due 12/13