Week | Lecture Topic |
PART I: Introduction | |
1 8/29 |
Course Introduction
An Introduction to AI and Its History
|
2 9/5** |
**No class. Classes Following Monday Schedule |
PART II: Machine Learning | |
3 9/12 |
Introduction to Supervised Learning
|
4 9/19 |
EXAM I (PART I and Supervised Learning)
Linear Regression
|
5
9/26 |
Logistic Regression
|
6 10/3 |
EXAM II (PART II Linear and Logistic Regression)
Naïve Bayes
|
7 10/10 |
Support Vector Machines
|
8
10/17 |
EXAM III (PART II Naïve Bayes and SVM)
Introduction to Unsupervised Learning
|
Part III: Deep Learning | |
9 10/24 |
Introduction to Deep Learning:
|
10 10/31 |
EXAM IV (PART II Unsupervised Learning and Part III Intro Deep Learning)
Neural Networks: Representation
|
11 11/7 |
Neural Networks: Learning
|
12 11/14 |
Deep Reinforcement Learning
|
PART IV: Final project (Autonomous Self Driving Car) | |
13 11/21 |
EXAM V (PART III Deep Learning and Deep Reinforcement Learning)
Project hours (Assembling the Car) |
14 11/28** | **No class. Happy Thanks Given!! |
15 12/5 |
Project hours (Training) |
16 12/12 |
Project hours (Adjustments) |
17 12/19 |
Final project presentation and Autonomous Vehicles Race (City Tech AV Cup) |