1. AV
Autonomous Vehicles

3. AI ML AI & ML - Summary Supervised Learning Deep Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning

4. Supervised learning Computer program “AI” Learning algorithm Model
Learning process
Problem solving process
New Input data
(problems)
Known input data
“problems”
+ Known “answers”
Learning algorithm
Computer program “AI”
Model
(math)
given to
produces
embedded in
produces
An equation
A decision tree
A neural network …
New Output (answers)
Credit card fraud * Weather prediction * Car fault diagnosis * Face recognition

5. Your role: high level design, focusing on input output
New Input data
(problems)
What is the problem + answer pair?
What specific data describes a problem?
Do we know what a good solution looks like?
Do we have enough examples to teach a machine?
Known input data
“problems”
+ Known “answers”
How do we know that the system is accurate?
What if the system is wrong?
New Output (answers)

6. Testing supervised learning
Known input data
“problems”
+ Known “answers”
Set aside
Training set
Known input data
“problems”
+ Known “answers”
Known input data
“problems”
Model
(math)
Learning algorithm
Computer program
“New” output (answers)
Known “answers”
compare

7. Driving is different: acting in a dynamic environment
Car Sensor data (images, gps, etc.)
Dynamic environment
(traffic)
Acts on
OUTPUT: Car actions
(turn left, accelerate…)

8. AV have many sensors

9. Car sensor data: Lidar

10. Acting in a dynamic environment with reinforcement learning
Car Sensor data (images, gps, etc.)
Dynamic environment
(traffic)
Neural Net
Acts on
OUTPUT: Car actions
(turn left, accelerate…)
Learning algorithm
“Rewards”