The prerequisite for this course is CS35. 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 | Jan 21 | MLK Day - NO CLASS | Introduction to Machine Learning
Reading:
| Wed: Fri: Lab 1: K-nearest neighbors |
Jan 23 | ||||
Jan 25 | ||||
2 | Jan 28 | Decision Trees
Reading:
| Mon: Mon: Wed: Fri: Lab 2: Decision trees | |
Jan 30 | Out of town - NO CLASS | |||
Feb 01 | Out of town - NO CLASS Drop/add ends | |||
3 | Feb 04 | Linear Regression
Reading:
| ||
Feb 06 | ||||
Feb 08 | ||||
4 | Feb 11 | Probabilistic Models 1
Reading:
| Mon: Wed: Fri: Lab 3: Regression | |
Feb 13 | ||||
Feb 15 | ||||
5 | Feb 18 | Probabilistic Models 2
Reading:
| Mon: Wed: Fri: Lab 4: Probabilistic Models | |
Feb 20 | ||||
Feb 22 | ||||
6 | Feb 25 | Evaluation Metrics
Reading:
| Mon: Wed: Fri: In-lab Midterm 1 | |
Feb 27 | ||||
Mar 01 | ||||
7 | Mar 04 | Ensemble Methods
Reading:
| Mon: Wed: Fri: Lab 4: (cont) | |
Mar 06 | ||||
Mar 08 | ||||
Mar 11 | Spring Break | |||
Mar 13 | ||||
Mar 15 | ||||
8 | Mar 18 | Support Vector Machines
Reading:
| Mon: Wed: Fri: Lab 5: Ensemble methods | |
Mar 20 | ||||
Mar 22 | ||||
9 | Mar 25 | SVMs (continued)
Reading:
| Mon: Wed: Fri: Lab 6: Support vector machines | |
Mar 27 | ||||
Mar 29 | CR/NC/W Deadline | |||
10 | Apr 01 | Topics in Deep Learning 1
Reading:
| Mon: Wed: Fri: Lab 6: (cont) | |
Apr 03 | ||||
Apr 05 | ||||
11 | Apr 08 | Topics in Deep Learning 2
Reading:
| Mon: Wed: Fri: Lab 7: Neural Networks | |
Apr 10 | ||||
Apr 12 | ||||
12 | Apr 15 | Unsupervised Learning
Reading:
| Mon: Wed: Fri: Project: Proposal | |
Apr 17 | ||||
Apr 19 | ||||
13 | Apr 22 | Midterm Review and Special Topics
| Mon: Wed: Fri: In-lab Midterm 2 | |
Apr 24 | ||||
Apr 26 | ||||
14 | Apr 29 | Special Topic: Machine Learning and Ethics
Reading:
| Mon: Wed: Fri: Project: Presentation | |
May 01 | ||||
May 03 |