Week | Lecture Topic |
Module I: Introduction and Exploratory Analysis | |
1 8/27 |
Course Introduction
An Introduction to AI and Its History
|
2 9/3 |
Statistical Thinking
|
Module II: Supervised Machine Learning | |
3 9/10 |
EXAM I (Module I)
Introduction to Supervised Learning
|
4 9/17 |
Linear Regression
|
5 9/24 |
Logistic Regression
|
6 10/1 |
Support Vector Machines
|
Module III: Unsupervised Machine Learning | |
7 10/8 |
EXAM III (Module II)
Introduction to Unsupervised Learning
|
Module IV: Deep Learning | |
9 10/15 |
Introduction to Deep Learning:
|
9 10/22 |
EXAM IV (Module III)
Neural Networks: Representation
|
10 10/29 |
Neural Networks: Learning
|
11 11/5 |
Deep Reinforcement Learning
|
PART IV: Independent Study | |
12 11/12 |
Linear Classifiers in Python
|
13 11/19 |
Linear Classifiers in Python
|
14 11/26** |
**No class. Happy Thanks Given!! |
15 12/3 |
EXAM V (Module IV)
Case Study: School Budgeting with Machine Learning in Python |
16 12/10** |
**No class. Reading Day! |
17 12/17 |
Machine Learning Fundamentals with Python | Exam |