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Thad Starner
Take Udacity's free AI Basics course and learn the basics of AI including classical search, probability, machine learning, logic and planning. Learn online with Udacity.
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UDACITY40
What's inside
Syllabus
1. Game Playing
2. Search
3. Simulated Annealing
4. Constraint Satisfaction
Read more
Syllabus
1. Game Playing
2. Search
3. Simulated Annealing
4. Constraint Satisfaction
Read more
5. Probability
6. Bayes Nets
7. Machine Learning
8. Pattern Recognition through Time
9. Logic and Planning
10. Planning under Uncertainty
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches classical search, which is standard in AI
Introduces probability, which is fundamental in AI
Introduces Bayes Nets, a key element in probabilistic reasoning
Covers machine learning, a trending topic in current AI research
Teaches pattern recognition through time, a key topic in time series analysis
Provides an overview of logic and planning, essential concepts in AI
Requires no prior knowledge in AI
Save this course
Save Artificial Intelligence to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Artificial Intelligence with these
activities:
Review probability and statistics
Show steps
Review the basics of probability and statistics to strengthen your understanding of key concepts covered in this class.
Browse courses on
Probability and Statistics
Show steps
-
Go through notes or textbooks to refresh your understanding of probability distributions, such as binomial, normal, and Poisson distributions.
-
Practice solving problems related to probability and statistics to assess your current level of understanding.
Follow tutorials on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by following guided tutorials that provide step-by-step instructions and examples.
Browse courses on
Machine Learning
Show steps
-
Search for online tutorials or courses that cover the specific machine learning algorithms discussed in the class.
-
Follow the tutorials, taking notes and practicing the implementation of the algorithms.
Solve practice problems on game playing
Show steps
Solidify your understanding of game playing techniques by solving practice problems that challenge you to apply different search algorithms.
Show steps
-
Find online resources or textbooks that provide practice problems on game playing using search algorithms.
-
Attempt to solve the problems, experimenting with different search techniques.
-
Review your solutions and identify areas where you can improve your problem-solving skills.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a machine learning model for a specific problem
Show steps
Challenge yourself by applying your knowledge of machine learning to a real-world problem by developing a model that addresses a specific issue or task.
Browse courses on
Machine Learning
Show steps
-
Identify a problem that can be addressed using machine learning.
-
Gather the necessary data and preprocess it for use in your model.
-
Select and train a suitable machine learning algorithm for your problem.
-
Evaluate the performance of your model and make adjustments as needed.
-
Present your findings in a report or presentation.
Contribute to open-source projects related to AI
Show steps
Gain practical experience and learn from others by contributing to open-source projects that are relevant to the field of artificial intelligence.
Browse courses on
AI
Show steps
-
Research and identify open-source projects that align with your interests and skill level.
-
Contact the project maintainers to express your interest in contributing.
-
Review the project's documentation and codebase to understand its structure and goals.
-
Suggest improvements or offer to implement new features, bug fixes, or documentation.
Mentor other students in AI-related topics
Show steps
Expand your understanding of the course material and enhance your communication skills by mentoring other students in areas where you excel.
Browse courses on
AI
Show steps
-
Assess your own understanding of the course material and identify areas where you can provide guidance to others.
-
Offer to help fellow students during study sessions or online forums.
-
Provide clear explanations, examples, and support to help others grasp the concepts.
Organize and review course notes, readings, and assignments
Show steps
Enhance your retention of course material by regularly reviewing and organizing your notes, readings, and assignments.
Browse courses on
Note-Taking
Show steps
-
Set aside time to review your notes and readings.
-
Summarize key concepts and ideas in your own words.
-
Create flashcards or other study aids to help you memorize important information.
Review probability and statistics
Show steps
Review the basics of probability and statistics to strengthen your understanding of key concepts covered in this class.
Browse courses on
Probability and Statistics
Show steps
Show steps
Show steps
- Go through notes or textbooks to refresh your understanding of probability distributions, such as binomial, normal, and Poisson distributions.
- Practice solving problems related to probability and statistics to assess your current level of understanding.
Follow tutorials on machine learning algorithms
Show steps
Reinforce your understanding of machine learning algorithms by following guided tutorials that provide step-by-step instructions and examples.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for online tutorials or courses that cover the specific machine learning algorithms discussed in the class.
- Follow the tutorials, taking notes and practicing the implementation of the algorithms.
Solve practice problems on game playing
Show steps
Solidify your understanding of game playing techniques by solving practice problems that challenge you to apply different search algorithms.
Show steps
Show steps
Show steps
- Find online resources or textbooks that provide practice problems on game playing using search algorithms.
- Attempt to solve the problems, experimenting with different search techniques.
- Review your solutions and identify areas where you can improve your problem-solving skills.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a machine learning model for a specific problem
Show steps
Challenge yourself by applying your knowledge of machine learning to a real-world problem by developing a model that addresses a specific issue or task.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem that can be addressed using machine learning.
- Gather the necessary data and preprocess it for use in your model.
- Select and train a suitable machine learning algorithm for your problem.
- Evaluate the performance of your model and make adjustments as needed.
- Present your findings in a report or presentation.
Contribute to open-source projects related to AI
Show steps
Gain practical experience and learn from others by contributing to open-source projects that are relevant to the field of artificial intelligence.
Browse courses on
AI
Show steps
Show steps
Show steps
- Research and identify open-source projects that align with your interests and skill level.
- Contact the project maintainers to express your interest in contributing.
- Review the project's documentation and codebase to understand its structure and goals.
- Suggest improvements or offer to implement new features, bug fixes, or documentation.
Mentor other students in AI-related topics
Show steps
Expand your understanding of the course material and enhance your communication skills by mentoring other students in areas where you excel.
Browse courses on
AI
Show steps
Show steps
Show steps
- Assess your own understanding of the course material and identify areas where you can provide guidance to others.
- Offer to help fellow students during study sessions or online forums.
- Provide clear explanations, examples, and support to help others grasp the concepts.
Organize and review course notes, readings, and assignments
Show steps
Enhance your retention of course material by regularly reviewing and organizing your notes, readings, and assignments.
Browse courses on
Note-Taking
Show steps
Show steps
Show steps
- Set aside time to review your notes and readings.
- Summarize key concepts and ideas in your own words.
- Create flashcards or other study aids to help you memorize important information.
Career center
Learners who complete Artificial Intelligence will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models that enable computers to learn without being explicitly programmed. Machine Learning is a subfield of Artificial Intelligence that gives computers the ability to learn without being explicitly programmed. A Machine Learning Engineer may wish to take a course in AI in order to build a foundation for understanding tasks such as deep learning, natural language processing, and predictive analytics.
Management Consultant
Management Consultant
A Management Consultant helps businesses improve their performance by analyzing their operations and identifying areas for improvement. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Management Consultant may wish to take a course in AI in order to build a foundation for understanding topics such as business strategy, process improvement, and organizational change.
Business Analyst
Business Analyst
A Business Analyst identifies and defines business needs, and develops solutions to meet those needs. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Business Analyst may wish to take a course in AI in order to build a foundation for understanding topics such as business process modeling, requirements gathering, and solution design.
Data Architect
Data Architect
A Data Architect designs and manages data systems and infrastructure. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Data Architect may wish to take a course in AI in order to build a foundation for understanding topics such as data modeling, data integration, and data security.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to improve the efficiency and effectiveness of business operations. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. An Operations Research Analyst may wish to take a course in AI in order to build a foundation for understanding topics such as linear programming, network optimization, and simulation.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze financial data and make investment decisions. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Quantitative Analyst may wish to take a course in AI in order to build a foundation for understanding topics such as financial modeling, risk management, and portfolio optimization.
Sales Manager
Sales Manager
A Sales Manager is responsible for the development and execution of sales strategies. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Sales Manager may wish to take a course in AI in order to build a foundation for understanding topics such as sales forecasting, territory management, and customer relationship management.
User Experience Designer
User Experience Designer
A User Experience Designer designs and evaluates user interfaces for websites, apps, and other products. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A User Experience Designer may wish to take a course in AI in order to build a foundation for understanding topics such as human-computer interaction, information architecture, and user research.
Technical Writer
Technical Writer
A Technical Writer creates technical documentation, such as user manuals, white papers, and training materials. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Technical Writer may wish to take a course in AI in order to build a foundation for understanding topics such as natural language processing, information architecture, and user experience.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights to solve business problems. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Data Scientist may wish to take a course on AI in order to build a foundation for understanding tasks such as text and speech processing, machine learning, and computer vision.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data to help businesses make informed decisions. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Data Analyst may wish to take a course in AI in order to build a foundation for understanding topics such as data mining, data visualization, and statistical analysis.
Product Manager
Product Manager
A Product Manager is responsible for the development and marketing of a product or service. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Product Manager may wish to take a course in AI in order to build a foundation for understanding topics such as product design, market research, and customer feedback.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for the development and execution of marketing campaigns. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Marketing Manager may wish to take a course in AI in order to build a foundation for understanding topics such as market segmentation, target audience identification, and campaign measurement.
Software Engineer
Software Engineer
A Software Engineer designs, develops, tests, deploys, and maintains software systems. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Software Engineer may wish to take a course in AI in order to build a foundation for understanding topics such as data structures and algorithms, software design, and software development.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are satisfied with a company's products or services. Artificial Intelligence is a subfield of Computer Science that gives computers the ability to perform tasks that normally require human intelligence. A Customer Success Manager may wish to take a course in AI in order to build a foundation for understanding topics such as customer relationship management, customer support, and product feedback.
For more career information including salaries, visit:
OpenCourser.com/course/yunzbn/udacity
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Artificial Intelligence.
Save
Provides a comprehensive overview of the field of machine learning. It covers a wide range of topics, including supervised and unsupervised learning, reinforcement learning, and deep learning.
Save
Provides a comprehensive overview of the field of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive overview of the field of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradient methods.
Provides a comprehensive overview of the field of natural language processing. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation.
Provides a comprehensive overview of the field of computer vision. It covers a wide range of topics, including image processing, feature detection, and object recognition.
Provides a comprehensive overview of the field of probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov random fields, and conditional random fields.
Provides a comprehensive overview of the field of information theory. It covers a wide range of topics, including entropy, mutual information, and channel capacity.
Provides a comprehensive overview of the field of logic and computer science modeling. It covers a wide range of topics, including propositional logic, predicate logic, and first-order logic.
Save
Provides a comprehensive overview of the field of knowledge representation and reasoning. It covers a wide range of topics, including ontologies, description logics, and rule-based systems.
Provides a comprehensive overview of the field of machine learning. It covers a wide range of topics, including supervised and unsupervised learning, reinforcement learning, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yunzbn/udacity
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Effort
2 hours
Via
Udacity
Instructor
Thad Starner
Language
English
Good to know
Teaches classical search, which is standard in AI
Introduces probability, which is fundamental in AI
Introduces Bayes Nets, a key element in probabilistic reasoning
Covers machine learning, a trending topic in current AI research
Teaches pattern recognition through time, a key topic in time series analysis
Provides an overview of logic and planning, essential concepts in AI
Requires no prior knowledge in AI
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