subject
Intro

Artificial Intelligence for Robotics

 with  Sebastian Thrun

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Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.

This course is offered as part of the Georgia Tech Masters in Computer Science. The updated course includes a final project, where you must chase a runaway robot that is trying to escape!

Why Take This Course?
This course will teach you probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics.

At the end of the course, you will leverage what you learned by solving the problem of a runaway robot that you must chase and hunt down!

Syllabus

### Lesson 1: Localization

- Localization
- Total Probability
- Uniform Distribution
- Probability After Sense
- Normalize Distribution
- Phit and Pmiss
- Sum of Probabilities
- Sense Function
- Exact Motion
- Move Function
- Bayes Rule
- Theorem of Total Probability

### Lesson 2: Kalman Filters

- Gaussian Intro
- Variance Comparison
- Maximize Gaussian
- Measurement and Motion
- Parameter Update
- New Mean Variance
- Gaussian Motion
- Kalman Filter Code
- Kalman Prediction
- Kalman Filter Design
- Kalman Matrices

### Lesson 3: Particle Filters

- Slate Space
- Belief Modality
- Particle Filters
- Using Robot Class
- Robot World
- Robot Particles

### Lesson 4: Search

- Motion Planning
- Compute Cost
- Optimal Path
- First Search Program
- Expansion Grid
- Dynamic Programming
- Computing Value
- Optimal Policy

### Lesson 5: PID Control

- Robot Motion
- Smoothing Algorithm
- Path Smoothing
- Zero Data Weight
- Pid Control
- Proportional Control
- Implement P Controller
- Oscillations
- Pd Controller
- Systematic Bias
- Pid Implementation
- Parameter Optimization

### Lesson 6: SLAM (Simultaneous Localization and Mapping)

- Localization
- Planning
- Segmented Ste
- Fun with Parameters
- SLAM
- Graph SLAM
- Implementing Constraints
- Adding Landmarks
- Matrix Modification
- Untouched Fields
- Landmark Position
- Confident Measurements
- Implementing SLAM

### Runaway Robot Final Project
19 Student
reviews
Cost Free Online Course
Pace Self Paced
Institution Stanford University
Provider Udacity
Language English
Hours 6 hours a week
Calendar 8 weeks long
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

19 reviews

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4 years ago
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Anonymous completed this course.
Pretty good course. I did not finish it because it overlapped a lot with the first version that Sebastian Thrun did together with Peter Norvig. From the first several units I got an impression that the course is an aggregation of loosely connected topics (as if the authors tried to cover a lot more than they had time for), but nevertheless each topic is well explained.
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4 years ago
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Anonymous completed this course.
I only did the first chapter as I was looking for localization algorithms and I thought it was really useful.
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4 years ago
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Anonymous completed this course.
excellent, clear and easy to understand for people with some programming and math skills
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1 out of 1 people found the following review useful
2 years ago
Mal Minhas partially completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
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12 months ago
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a year ago
Alvaro Carrera partially completed this course.
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2 years ago
Rey Raul Coaguila completed this course and found the course difficulty to be easy.
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a year ago
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Vikram Pawar is taking this course right now.
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3 months ago
Julio Vizcaíno Molina partially completed this course.
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