2 best courses on Edx and Udacity to learn Artificial Intelligence for FREE

2 best courses on Edx and Udacity to learn  Artificial Intelligence for FREE

1. edX: CS188.1x: Artificial Intelligence

provider University of California, Berkeley  with Dan Klein and Pieter Abbeel



Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces. AI lets you guide your phone with your voice and read foreign newspapers in English. Beyond today's applications, AI is at the core of many new technologies that will shape our future. From self-driving cars to household robots, advancements in AI help transform science fiction into real systems.

The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems.



To get this course click here : BerkeleyX: CS188.1x Artificial Intelligence

(Note : You must login in Edx and enroll in the course to see course content. )

2. Udacity: Artificial Intelligence for Robotics

provider Stanford University  with Sebastian Thrun

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

To get this course click here :Artificial Intelligence for Robotics