The prerequisites for this course are Data Structures, Discrete Mathematics, and Linear Algebra.
Machine Learning as a field has grown considerably over the past few decades. In this course, we will explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as TensorFlow and Keras. By the end of this course, you should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data.
The language for this course is Python 3.
WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LABS |
1 | Sep 03 | Introduction to Machine Learning
Reading:
| Tues: Thurs: Lab 1: K-nearest neighbors | |
Sep 05 | ||||
2 | Sep 10 | Decision Trees
Reading:
| Tues: Thurs: Lab 2: Decision trees | |
Sep 12 | ||||
3 | Sep 17 | Linear Regression
Reading:
| Tues: Thurs: Lab 3: Polynomial Regression | |
Sep 19 | Last day to drop (Sep 20) | |||
4 | Sep 24 | Probabilistic Models 1
Reading:
| Tues: Thurs: Lab 4: Naive Bayes | |
Sep 26 | ||||
5 | Oct 01 | Probabilistic Models 2
Reading:
| Tues: Thurs: Midterm 1 (in-lab and take-home) | |
Oct 03 | ||||
6 | Oct 08 | Evaluation Metrics
Reading:
| Tues: Thurs: Lab 5: Logistic Regression | |
Oct 10 | Last day to pass/fail (Oct 11) | |||
Oct 15 | Fall Break | |||
Oct 17 | ||||
7 | Oct 22 | Ensemble Methods
Reading:
| Tues: Thurs: Lab 6: Ensemble methods | |
Oct 24 | ||||
8 | Oct 29 | Support Vector Machines
Reading:
| Tues: Thurs: Lab 6: (cont.) | |
Oct 31 | ||||
9 | Nov 05 | SVMs (continued)
Reading:
| Tues: Thurs: Lab 7: Support vector machines | |
Nov 07 | ||||
10 | Nov 12 | No class (at a conference) | Topics in Deep Learning 1
Reading:
| Thurs: Lab 8: Neural Networks |
Nov 14 | ||||
11 | Nov 19 | Topics in Deep Learning 2
Reading:
| Tues: Thurs: Midterm 2 (in-lab and take-home) | |
Nov 21 | ||||
12 | Nov 26 | Unsupervised Learning 1
Reading:
| Tues: No lab: Thanksgiving | |
Nov 28 | Thanksgiving (no class) | |||
13 | Dec 03 | Unsupervised Learning 2
| Tues: Thurs: Tues: Thurs: Project: check-ins during lab | |
Dec 05 | ||||
14 | Dec 10 | Special Topic: Machine Learning and Ethics
Reading:
| ||
Dec 12 |