Ch 6: Learning & Reasoning in Artificial Intelligence

About This Chapter

Boost your understanding of reasoning and learning in artificial intelligence with this informative chapter. Our professional instructors make these concepts easy to understand to help you get caught up in class, prepare for a test or finish your homework with ease.

Learning & Reasoning in Artificial Intelligence - Chapter Summary

Learning and reasoning in artificial intelligence is clearly outlined in this engaging chapter. As you work through it, you'll encounter topics such as supervised and unsupervised learning in machine learning, support vector machines and probabilistic reasoning in artificial intelligence. Help from one of our experts is just a few clicks away if you need it, and it's easy to test yourself before you move on with the lesson quizzes and chapter test. Once you complete this chapter, you should be able to:

  • Differentiate between unsupervised and supervised learning in machine learning
  • Discuss natural language processing and deep learning
  • Explain entropy in machine learning
  • Outline nodes and the uses of decision networks in AI
  • Identify the applications of support vector machines (SMVs)
  • Detail the connection between artificial intelligence and probabilistic reasoning
  • Describe the impact of probabilistic reasoning over time
  • Explain speech recognition in artificial intelligence
  • Discuss AI computer vision and image classification
  • Describe how to use create a learning chatbot

11 Lessons in Chapter 6: Learning & Reasoning in Artificial Intelligence
Test your knowledge with a 30-question chapter practice test
Supervised Learning in Machine Learning

1. Supervised Learning in Machine Learning

This lesson covers supervised learning, more specifically regression and classification problems. We will see how to perform a linear regression to predict the prices of homes and how to use logistic regression in the analysis of breast tumor data.

Unsupervised Learning in Machine Learning

2. Unsupervised Learning in Machine Learning

Have you ever wondered how Netflix knows what show you might like to watch next? This lesson will take you through the definition of an unsupervised learning algorithm, how it works, and a real-world example.

Natural Language Processing & Deep Learning

3. Natural Language Processing & Deep Learning

Wouldn't it be great to have a world as in Sci-Fi movies where Captain Kirk enters and just says: ''Computer, what's going on? '' For this dream to come true, we need natural language processing, or a way for a machine to understand an ambiguous thing such as our language. Among the current approaches to do that, this lesson will discuss deep learning, together with some basic definitions of NLP and machine learning to contextualize it.

Entropy in Machine Learning

4. Entropy in Machine Learning

In this lesson, we'll take a look at entropy, how it relates to machine learning, and why it's important. At the end of the lesson, you should have a good understanding of these relevant topics.

Decision Networks in Artificial Intelligence: Nodes & Uses

5. Decision Networks in Artificial Intelligence: Nodes & Uses

In decision making under uncertainty, a graphical model known as a decision network is used to evaluate the feasibility of a decision. In this lesson, you will learn about decision networks, their structure and how an AI agent uses them in decision making.

Support Vector Machines (SVMs): Definitions & Applications

6. Support Vector Machines (SVMs): Definitions & Applications

In this lesson you will learn what SVMs are and how they are useful to retrieve patterns and relations between data. You will also learn how to implement an SVM for data classification.

Probabilistic Reasoning & Artificial Intelligence

7. Probabilistic Reasoning & Artificial Intelligence

In this lesson, we will describe probabilistic reasoning and its impact on artificial intelligence. You will also learn to apply the concept of a Bayesian network to represent uncertain knowledge and conditional independence.

Probabilistic Reasoning Over Time in AI

8. Probabilistic Reasoning Over Time in AI

In this lesson, you will be introduced to the concept of Probabilistic reasoning in AI. The concept of Bayesian network for representation of data will also be discussed.

Speech Recognition in Artificial Intelligence

9. Speech Recognition in Artificial Intelligence

In this lesson, you will learn how speech recognition works in artificial intelligent systems. You will also learn about language models and the working of speech recognizers.

Computer Vision & Image Classification in AI

10. Computer Vision & Image Classification in AI

This lesson will focus on what constitutes computer vision and image classification and how deep learning with neural networks allows us to achieve these goals.

Practical Application for Artificial Intelligence: Learning Chatbot

11. Practical Application for Artificial Intelligence: Learning Chatbot

In this practical application, you will create a C++ application that creates a learning chatbot! You will build, compile, run, and test your program.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
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