What are some good books on Machine Learning and AI like Krugman, Wells and Graddy's “Essentials of Economics”2019 Community Moderator ElectionWhat are some easy to learn machine-learning applications?Data science / machine learning books for mathematiciansWhat would you recommend to know before considering applying for a master in Machine Learning?What are some of the resources to learn practical issues in machine learning and data science?Good books on unsupervised learningCan anyone recommend some good books or articles on working with time series?Learning Attention Based Models [books]What does Machine Learning Paradigms means, and what are they?What are some good fields to research in data science?Books on time series and sequence classification
Minkowski space
Mathematical cryptic clues
What do you call a Matrix-like slowdown and camera movement effect?
Why do falling prices hurt debtors?
How to say job offer in Mandarin/Cantonese?
Accidentally leaked the solution to an assignment, what to do now? (I'm the prof)
Theorems that impeded progress
Dragon forelimb placement
Writing rule stating superpower from different root cause is bad writing
Show that if two triangles built on parallel lines, with equal bases have the same perimeter only if they are congruent.
Test whether all array elements are factors of a number
Is it tax fraud for an individual to declare non-taxable revenue as taxable income? (US tax laws)
Why are electrically insulating heatsinks so rare? Is it just cost?
What are these boxed doors outside store fronts in New York?
Why, historically, did Gödel think CH was false?
How to write a macro that is braces sensitive?
Do VLANs within a subnet need to have their own subnet for router on a stick?
Why does Kotter return in Welcome Back Kotter?
What's the point of deactivating Num Lock on login screens?
Font hinting is lost in Chrome-like browsers (for some languages )
Why doesn't Newton's third law mean a person bounces back to where they started when they hit the ground?
TGV timetables / schedules?
How does one intimidate enemies without having the capacity for violence?
A newer friend of my brother's gave him a load of baseball cards that are supposedly extremely valuable. Is this a scam?
What are some good books on Machine Learning and AI like Krugman, Wells and Graddy's “Essentials of Economics”
2019 Community Moderator ElectionWhat are some easy to learn machine-learning applications?Data science / machine learning books for mathematiciansWhat would you recommend to know before considering applying for a master in Machine Learning?What are some of the resources to learn practical issues in machine learning and data science?Good books on unsupervised learningCan anyone recommend some good books or articles on working with time series?Learning Attention Based Models [books]What does Machine Learning Paradigms means, and what are they?What are some good fields to research in data science?Books on time series and sequence classification
$begingroup$
I am a Logistics student. I like the book "Essentials of Economics" by Krugman, Wells and Graddy in that it is concise, easygoing and not a beginners book (it gradually approaches advanced subjects thus paving the way for further rigorous Economics course) so any one interested in Economics can learn it even if he/she never studied the subject before. Also, I am very interested in AI and Machine Learning and acknowledge their importance in this our postmodern era and I am self learning Real Analysis and web site development. What are some good introductory books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"?
machine-learning self-study books ai
$endgroup$
add a comment |
$begingroup$
I am a Logistics student. I like the book "Essentials of Economics" by Krugman, Wells and Graddy in that it is concise, easygoing and not a beginners book (it gradually approaches advanced subjects thus paving the way for further rigorous Economics course) so any one interested in Economics can learn it even if he/she never studied the subject before. Also, I am very interested in AI and Machine Learning and acknowledge their importance in this our postmodern era and I am self learning Real Analysis and web site development. What are some good introductory books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"?
machine-learning self-study books ai
$endgroup$
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago
add a comment |
$begingroup$
I am a Logistics student. I like the book "Essentials of Economics" by Krugman, Wells and Graddy in that it is concise, easygoing and not a beginners book (it gradually approaches advanced subjects thus paving the way for further rigorous Economics course) so any one interested in Economics can learn it even if he/she never studied the subject before. Also, I am very interested in AI and Machine Learning and acknowledge their importance in this our postmodern era and I am self learning Real Analysis and web site development. What are some good introductory books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"?
machine-learning self-study books ai
$endgroup$
I am a Logistics student. I like the book "Essentials of Economics" by Krugman, Wells and Graddy in that it is concise, easygoing and not a beginners book (it gradually approaches advanced subjects thus paving the way for further rigorous Economics course) so any one interested in Economics can learn it even if he/she never studied the subject before. Also, I am very interested in AI and Machine Learning and acknowledge their importance in this our postmodern era and I am self learning Real Analysis and web site development. What are some good introductory books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"?
machine-learning self-study books ai
machine-learning self-study books ai
edited Apr 4 at 2:44
community wiki
2 revs
Anti-American Anti-Zionist
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago
add a comment |
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
The two books that come into my mind are:
- Artificial Intelligence: A Modern Approach
- The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
$endgroup$
add a comment |
$begingroup$
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.
So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.
Here goes the list (it's a popular one)
Books
The Master Algorithm
If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.
An Introduction to Statistical Learning with Applications in R
This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.
This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.
The Elements Of Statistical Learning
This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.
These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:
Pattern Recognition and Machine Learning
Deep Learning
Reinforcement Learning
The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a lot of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.
Enjoy !
$endgroup$
add a comment |
$begingroup$
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think you would enjoy learning about reinforcement learning. Possibly as a result of recent renewed interest in the field, a second edition of
Reinforcement Learning: An Introduction
has just come out. The original version was quite good and this new one has, amongst other things, a very interesting section on applications including AlphaGo and Watson (of Jeopardy fame).
If you aren't really sure what you want to study, datasciencetexts.com contains a number of brief descriptions of related and prerequisite subjects, along with recommended books that you might be interested in. (Disclosure: I helped build it.)
Happy Reading!
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
);
);
, "mathjax-editing");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48548%2fwhat-are-some-good-books-on-machine-learning-and-ai-like-krugman-wells-and-grad%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The two books that come into my mind are:
- Artificial Intelligence: A Modern Approach
- The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
$endgroup$
add a comment |
$begingroup$
The two books that come into my mind are:
- Artificial Intelligence: A Modern Approach
- The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
$endgroup$
add a comment |
$begingroup$
The two books that come into my mind are:
- Artificial Intelligence: A Modern Approach
- The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
$endgroup$
The two books that come into my mind are:
- Artificial Intelligence: A Modern Approach
- The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
answered Apr 3 at 19:31
community wiki
pcko1
add a comment |
add a comment |
$begingroup$
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.
So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.
Here goes the list (it's a popular one)
Books
The Master Algorithm
If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.
An Introduction to Statistical Learning with Applications in R
This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.
This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.
The Elements Of Statistical Learning
This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.
These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:
Pattern Recognition and Machine Learning
Deep Learning
Reinforcement Learning
The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a lot of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.
Enjoy !
$endgroup$
add a comment |
$begingroup$
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.
So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.
Here goes the list (it's a popular one)
Books
The Master Algorithm
If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.
An Introduction to Statistical Learning with Applications in R
This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.
This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.
The Elements Of Statistical Learning
This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.
These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:
Pattern Recognition and Machine Learning
Deep Learning
Reinforcement Learning
The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a lot of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.
Enjoy !
$endgroup$
add a comment |
$begingroup$
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.
So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.
Here goes the list (it's a popular one)
Books
The Master Algorithm
If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.
An Introduction to Statistical Learning with Applications in R
This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.
This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.
The Elements Of Statistical Learning
This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.
These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:
Pattern Recognition and Machine Learning
Deep Learning
Reinforcement Learning
The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a lot of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.
Enjoy !
$endgroup$
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the goal here is to understand the relationship between input variables and output variables. If I change the values of the inputs, how do the output values change? prediction: Here we are not as much interested in how the data changes, but just want to know the value of the output variable.
So, in general, you should be interested in Statistics, more specifically concerning prediction and inference. That's it, except that doesn't help you decide which books to purchase.
Here goes the list (it's a popular one)
Books
The Master Algorithm
If you want to learn about machine learning algorithms in a relaxed and fun manner, good if to take up if the next books give you headaches. Certainly worth reading.
An Introduction to Statistical Learning with Applications in R
This book is the most approachable one in the list. It requires some understanding of mathematics to understand certain formulas, but the text is still written in a way that will make concepts clear before you dive into the math. Make sure you do the exercises with R. It's a good skill to pick-up and it will make the theory much more tangible.
This book and next one in the list are freely available online, but if you want you can still purchase paper versions on amazon. I linked you the free versions.
The Elements Of Statistical Learning
This one picks up where ISLR left off. it is more math heavy and explores new concepts. You will find some overlap with the first book which will help solidify the concepts you learned in the first book.
These first three books will already ease you quite into the field. However if you decide to become more serious about learning, the following books should definitely be on your reading list:
Pattern Recognition and Machine Learning
Deep Learning
Reinforcement Learning
The best advice I can give you with these books is to read them from cover to cover. Don't read too much at once, take breaks and try to explain what you read to yourself. It can often make sense on paper and then not so much when you say it aloud.
Don't look at the formulae as something to skip. Instead, look at them like lego blocks. Each symbol has a meaning that is defined in the index at the beginning of each book. Try to explain each symbol in the formula; Then explain how the symbols interact. Once you understand the formula, try to think what happens when certain symbols change values. You'll get a very firm grasp of the formula that way. The field of AI and ML has a lot of jargon it can become overwhelming. By really understanding how certain algorithms work you will stop being fooled by the fancy names and start to realize that there is a lot of repetition.
Enjoy !
answered Apr 3 at 19:38
community wiki
Nicolas
add a comment |
add a comment |
$begingroup$
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think you would enjoy learning about reinforcement learning. Possibly as a result of recent renewed interest in the field, a second edition of
Reinforcement Learning: An Introduction
has just come out. The original version was quite good and this new one has, amongst other things, a very interesting section on applications including AlphaGo and Watson (of Jeopardy fame).
If you aren't really sure what you want to study, datasciencetexts.com contains a number of brief descriptions of related and prerequisite subjects, along with recommended books that you might be interested in. (Disclosure: I helped build it.)
Happy Reading!
$endgroup$
add a comment |
$begingroup$
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think you would enjoy learning about reinforcement learning. Possibly as a result of recent renewed interest in the field, a second edition of
Reinforcement Learning: An Introduction
has just come out. The original version was quite good and this new one has, amongst other things, a very interesting section on applications including AlphaGo and Watson (of Jeopardy fame).
If you aren't really sure what you want to study, datasciencetexts.com contains a number of brief descriptions of related and prerequisite subjects, along with recommended books that you might be interested in. (Disclosure: I helped build it.)
Happy Reading!
$endgroup$
add a comment |
$begingroup$
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think you would enjoy learning about reinforcement learning. Possibly as a result of recent renewed interest in the field, a second edition of
Reinforcement Learning: An Introduction
has just come out. The original version was quite good and this new one has, amongst other things, a very interesting section on applications including AlphaGo and Watson (of Jeopardy fame).
If you aren't really sure what you want to study, datasciencetexts.com contains a number of brief descriptions of related and prerequisite subjects, along with recommended books that you might be interested in. (Disclosure: I helped build it.)
Happy Reading!
$endgroup$
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think you would enjoy learning about reinforcement learning. Possibly as a result of recent renewed interest in the field, a second edition of
Reinforcement Learning: An Introduction
has just come out. The original version was quite good and this new one has, amongst other things, a very interesting section on applications including AlphaGo and Watson (of Jeopardy fame).
If you aren't really sure what you want to study, datasciencetexts.com contains a number of brief descriptions of related and prerequisite subjects, along with recommended books that you might be interested in. (Disclosure: I helped build it.)
Happy Reading!
edited Apr 3 at 21:57
community wiki
Unrefracted_Hooloovoo
add a comment |
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48548%2fwhat-are-some-good-books-on-machine-learning-and-ai-like-krugman-wells-and-grad%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
I thank everyone from the bottom of my heart! I accept all answers as "the Answer"! So I didn't tick any.
$endgroup$
– Anti-American Anti-Zionist
2 days ago
$begingroup$
@Anti-AmericanAnti-Zionist let's keep any politics off this SE; consider whether your username is relevant or helpful for a data science site. I've removed your comment above. This Q is borderline closeable as opinion-based but I left it as a wiki as it has gotten some useful responses.
$endgroup$
– Sean Owen♦
2 days ago