Stanford University - Introduction to Artificial Intelligence
WEBRip | English | MP4 + PDF Guide | 640 x 360 | AVC ~250 kbps | 30 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | ~24 hours | 4.42 GB
Genre: eLearning Video / Science, Cybernetics, Probability
Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing. The objective of this class is to teach you modern AI. You learn about the basic techniques and tricks of the trade, at the same level we teach our Stanford students. We also aspire to excite you about the field of AI. Whether you are a seasoned professional, a college student, or a curious high school student - everyone can participate.


Course Introduction

Introduction to the Course.

Unit 1 Videos, Transcripts, and Quizzes

1 Introduction.
2 Intelligent Agents.
3 Applications of AI.
4 Terminology.
5 Checkers Answer.
6 Poker.
7 Poker Answer.
8 Robotic Car.
9 Robotic Car Answer.
10 AI and Uncertainty.
11 Examples of AI in Practice.
12 Chinese Translation Answer.
13 Chinese Translation 2 Answer.
14 Chinese Translation 3 Answer.
15 Congratulations.


Unit 2 Videos, Transcripts, and Quizzes

1 Introduction.
2 Route Finding Question.
3 Route Finding.
4 Tree Search.
5 Graph Search.
6 Graph Search Answer.
7 Graph Search Answer.
8 Graph Search Answer.
9 More Graph Search.
10 Graph Search Answer.
11 Graph Search Termination.
12 Uniform Cost Search.
13 Uniform Cost Search.
14 Uniform Cost Search.
15 Uniform Cost Search.
16 Uniform Cost Termination.
17 Uniform Cost Termination Answer.
18 Depth First Search.
19 Search Optimality Answer.
20 Storage Requirements, Completeness.
21 Completeness Answer.
22 More on Uniform Cost Search.
23 A Star Search.
23 A Star Search Answer.
24 A Star Second Question.
24 A Star Second Question Answer.
25 A Star Third Question.
25 A Star Third Question Answer.
26 A Star Fourth Question.
26 A Star Fourth Question Answer.
27 A Star Fifth Question.
27 A Star Fifth Question Answer Mandatory.
28 A Optimistic Heuristics.
29 State Spaces.
29 State Spaces Answer.
30 State Space Diagram and More Complexity.
30 State Space Diagram and More Complexity Answer.
31 Sliding Blocks Puzzle.
31 Sliding Blocks Puzzle Answer.
32 Where is the Intelligence.
33 What Can't Search Do.
34 Note on Implementation.


Homework 1 Videos, Transcripts, and Quizzes

1 Introduction.
2 Peg Solitaire.
3 Peg Solitaire Answer.
4 Loaded Coin.
5 Loaded Coin Answer.
6 Path Through Maze.
7 Path Through Maze Answer.
8 Search Tree.
9 Search Tree Answer.
10 Another Search Tree.
11 Another Search Tree Answer.
12 Search Network.
13 Search Network Answer.
14 A-Star Search.
15 A-Star Search Answer.
16 Congratulations!.

Unit 3 Videos, Transcripts, and Quizzes

Introduction.
Probabilities.
Answer.
Question.
Answer.
Question.
Answer.
Question.
Answer.
Question.
Answer.
Summary.
Dependence.
Answer.
What We Learned.
Weather Quiz.
Answer.
Question.
Answer.
Question.
Answer.
Cancer Quiz.
Answer and Cancer Test.
Answer.
Answer.
Question.
Answer.
Bayes Rule.
Bayes Rule Graphically.
Answer.
More Complex Bayes Networks.
Two Test Cancer Example.
Answer.
Question.
Answer.
Conditional Independence.
Answer.
Question.
Answer.
Absolute vs Conditional Independence.
Answer.
Different Type of Bayes Network.
Answer.
Explaining Away.
Answer.
Question.
Answer.
Question.
Answer.
Conclusion.
Blank.
Answer.
Question.
Answer.
Question.
Answer.
Value of the Network.
D-Separation.
Answer.
D-Separation Example.
Answer.
D-Separation General Definition.
Answer.
Congratulations.

Unit 4 Videos, Transcripts, and Quizzes

Probabilistic Inference.
Answer.
Enumeration.
Answer.
Speeding Up Enumeration.
Answer.
Second Question.
Second Answer.
Third Question.
Third Answer.
Fourth Question.
Fourth Answer.
Causal Direction.
Variable Elimination.
Answer.
More Variable Elimination.
Answer.
Even More Variable Elimination.
Answer.
Summary.
Approximate Inference Sampling.
Sampling Example.
Answer.
More Sampling.
Rejection Sampling.
Likelihood Weighting.
Answer.
Likelihood Weighting is Consistent.
Likelihood Weighting Problems.
Gibbs Sampling.
Monty Hall Problem.

Homework 2 Videos, Transcripts, and Quizzes

Congratulations!.
Introduction.
Question 1 Answer.
Question 1.
Question 2 Answer.
Question 2.
Question 3 Answer.
Question 3.
Question 4 Answer.
Question 4.
Question 5 Answer.
Question 5.
Question 6 Answer.
Question 6.

Unit 5 Videos, Transcripts, and Quizzes

Introduction.
What is Machine Learning.
Answer.
Stanley DARPA Grand Challenge.
Taxonomy.
Supervised Learning.
Occams Razor.
SPAM Detection.
Answer.
Question.
Answer.
Maximum Likelihood 1.
Answer.
Relationship to Bayes Networks.
Answer.
Question.
Answer.
Question.
Answer.
Question.
Answer and Laplace Smoothing.
Answer.
Question.
Answer.
Question.
Answer.
Summary Naive Bayes.
Advanced SPAM Filtering.
Digit Recognition.
Overfitting Prevention.
Classification vs Regression.
Answer.
Linear Regression.
Answer.
More Linear Regression.
Quadratic Loss.
Answer.
Problems with Linear Regression.
Answer.
Linear Regression and Complexity Control.
Minimizing Complicated Loss Functions.
Answer.
Question.
Answer.
Question.
Answer.
Gradient Descent Implementation.
Perceptron.
Answer and SVMs.
k Nearest Neighbors.
kNN Definition.
Answer.
k as Smoothing Parameter.
Problems with kNN.
Congratulations.

Unit 6 Videos, Transcripts, and Quizzes

Unsupervised Learning.
Answer.
Question.
Answer.
Terminology.
Google Street View and Clustering.
k-Means Clustering Example.
k-Means Algorithm.
Answer.
Question.
Answer.
Question.
Answer.
Expectation Maximization.
Gaussian Learning.
Maximum Likelihood.
Answer.
Question.
Answer.
Question.
Answer.
Gaussian Summary.
EM as Generalization of k-Means.
EM Algorithm.
Answer.
Question.
Answer.
Question.
Answer.
Choosing k.
Clustering Summary.
Dimensionality Reduction.
Answer.
Question.
Answer.
Linear Dimensionality Reduction.
Face Example.
Scan Example.
Piece-Wise Linear Projection.
Spectral Clustering.
Answer.
Spectral Clustering Algorithm.
Answer.
Question.
Answer.
Supervised vs Unsupervised Learning.

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