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What belongs in a low-math undergraduate AI elective besides ML?
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The primarily undergraduate institution at which I teach does not have an Artificial Intelligence course. We do have classes on Data Analysis and Machine Learning:
DATA 150: Introduction to Data Analysis (4 Credits)
Data analysis is the extraction of knowledge and insights from complex
data. This course introduces the concepts, issues, and techniques of
data analysis. Topics include data cleaning and preparation, feature
selection, association rules, classification, clustering, evaluation
and validation. Tools implemented in R and Python will be used to
explore data sets using these techniques.
CS 141: Machine Learning (4 Credits)
This course provides a broad introduction to machine learning and
statistical pattern recognition including both supervised and
unsupervised learning from a computational perspective. Topics include
generative/discriminative learning, parametric/non-parametric
learning, neural networks, support vector machines, clustering,
dimensionality reduction, and kernel methods. Additional topics as
time allows.
It's been a long time since I was in school, and even then my knowledge of AI was pretty eclectic. What belongs in an undergraduate AI course that is not covered by the above two courses?
A constraint is that we do not require Calculus in our program, and most students have only had discrete mathematics. (While the above classes nominally have Linear Algebra and/or Calculus-based Probability and Statistics as prerequisites, we sometimes waive those requirements.)
curriculum-design undergraduate artificial-intelligence
asked 3 hours ago
Ellen Spertus
4,28321949
add a comment |Â
up vote
2
down vote
favorite
The primarily undergraduate institution at which I teach does not have an Artificial Intelligence course. We do have classes on Data Analysis and Machine Learning:
DATA 150: Introduction to Data Analysis (4 Credits)
Data analysis is the extraction of knowledge and insights from complex
data. This course introduces the concepts, issues, and techniques of
data analysis. Topics include data cleaning and preparation, feature
selection, association rules, classification, clustering, evaluation
and validation. Tools implemented in R and Python will be used to
explore data sets using these techniques.
CS 141: Machine Learning (4 Credits)
This course provides a broad introduction to machine learning and
statistical pattern recognition including both supervised and
unsupervised learning from a computational perspective. Topics include
generative/discriminative learning, parametric/non-parametric
learning, neural networks, support vector machines, clustering,
dimensionality reduction, and kernel methods. Additional topics as
time allows.
It's been a long time since I was in school, and even then my knowledge of AI was pretty eclectic. What belongs in an undergraduate AI course that is not covered by the above two courses?
A constraint is that we do not require Calculus in our program, and most students have only had discrete mathematics. (While the above classes nominally have Linear Algebra and/or Calculus-based Probability and Statistics as prerequisites, we sometimes waive those requirements.)
curriculum-design undergraduate artificial-intelligence
asked 3 hours ago
Ellen Spertus
4,28321949
Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago
add a comment |Â
up vote
2
down vote
favorite
up vote
2
down vote
favorite
The primarily undergraduate institution at which I teach does not have an Artificial Intelligence course. We do have classes on Data Analysis and Machine Learning:
DATA 150: Introduction to Data Analysis (4 Credits)
Data analysis is the extraction of knowledge and insights from complex
data. This course introduces the concepts, issues, and techniques of
data analysis. Topics include data cleaning and preparation, feature
selection, association rules, classification, clustering, evaluation
and validation. Tools implemented in R and Python will be used to
explore data sets using these techniques.
CS 141: Machine Learning (4 Credits)
This course provides a broad introduction to machine learning and
statistical pattern recognition including both supervised and
unsupervised learning from a computational perspective. Topics include
generative/discriminative learning, parametric/non-parametric
learning, neural networks, support vector machines, clustering,
dimensionality reduction, and kernel methods. Additional topics as
time allows.
It's been a long time since I was in school, and even then my knowledge of AI was pretty eclectic. What belongs in an undergraduate AI course that is not covered by the above two courses?
A constraint is that we do not require Calculus in our program, and most students have only had discrete mathematics. (While the above classes nominally have Linear Algebra and/or Calculus-based Probability and Statistics as prerequisites, we sometimes waive those requirements.)
curriculum-design undergraduate artificial-intelligence
asked 3 hours ago
Ellen Spertus
4,28321949
The primarily undergraduate institution at which I teach does not have an Artificial Intelligence course. We do have classes on Data Analysis and Machine Learning:
DATA 150: Introduction to Data Analysis (4 Credits)
Data analysis is the extraction of knowledge and insights from complex
data. This course introduces the concepts, issues, and techniques of
data analysis. Topics include data cleaning and preparation, feature
selection, association rules, classification, clustering, evaluation
and validation. Tools implemented in R and Python will be used to
explore data sets using these techniques.
CS 141: Machine Learning (4 Credits)
This course provides a broad introduction to machine learning and
statistical pattern recognition including both supervised and
unsupervised learning from a computational perspective. Topics include
generative/discriminative learning, parametric/non-parametric
learning, neural networks, support vector machines, clustering,
dimensionality reduction, and kernel methods. Additional topics as
time allows.
It's been a long time since I was in school, and even then my knowledge of AI was pretty eclectic. What belongs in an undergraduate AI course that is not covered by the above two courses?
A constraint is that we do not require Calculus in our program, and most students have only had discrete mathematics. (While the above classes nominally have Linear Algebra and/or Calculus-based Probability and Statistics as prerequisites, we sometimes waive those requirements.)
curriculum-design undergraduate artificial-intelligence
curriculum-design undergraduate artificial-intelligence
asked 3 hours ago
Ellen Spertus
4,28321949
asked 3 hours ago
Ellen Spertus
4,28321949
asked 3 hours ago
Ellen Spertus
4,28321949
asked 3 hours ago
Ellen Spertus
4,28321949
asked 3 hours ago
Ellen Spertus
4,28321949
4,28321949
Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago
add a comment |Â
Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago
Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago
add a comment |Â
1 Answer
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2
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I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell & Norvig's textbook is basically the bible for these sorts of courses.
From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving.
From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:
- Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
- Adversarial Game-playing AI (minimax and alpha-beta pruning)
- Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT).
These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")
Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability.
Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
add a comment |Â
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell & Norvig's textbook is basically the bible for these sorts of courses.
From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving.
From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:
- Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
- Adversarial Game-playing AI (minimax and alpha-beta pruning)
- Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT).
These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")
Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability.
Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
add a comment |Â
up vote
2
down vote
I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell & Norvig's textbook is basically the bible for these sorts of courses.
From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving.
From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:
- Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
- Adversarial Game-playing AI (minimax and alpha-beta pruning)
- Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT).
These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")
Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability.
Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
add a comment |Â
up vote
2
down vote
up vote
2
down vote
I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell & Norvig's textbook is basically the bible for these sorts of courses.
From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving.
From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:
- Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
- Adversarial Game-playing AI (minimax and alpha-beta pruning)
- Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT).
These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")
Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability.
Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I took both the AI and (basic) ML undergrad courses at Princeton and currently teach an AI elective at the HS level for some very bright students. I've seen some good online material from Berkeley (CS188) and MIT (6.034), and of course Stanford's ML lecture series is amazing. Russell & Norvig's textbook is basically the bible for these sorts of courses.
From what I've seen, AI undergraduate courses have changed a lot in the past decade to focus on ML, since much of the recent breakthroughs and publicity are in those areas. AI is a very broad and historically rich field beyond just Data Analysis or ML. I would say a comprehensive AI course would start from Alan Turing's classic (and often misused) Turing Test and ground itself in an ongoing definition of rationality as effective problem-solving.
From there, there are lot of "classical" AI fields that are very approachable without any calculus; most of their discrete algorithms are intuitive, and you can wave your hands and say the continuous (calculus-involving) counterparts are basically the same idea. Here's my shortlist of essential first topics:
- Goal-based state-space searching algorithms (e.g. DFS, BFS, Greedy, A*). These are a great start since the notion of searching is very universal and lays the groundwork for the next two:
- Adversarial Game-playing AI (minimax and alpha-beta pruning)
- Constraint-Satisfaction Problems (ARC-3, backtracking search) and/or Boolean SAT (DPLL, WalkSAT).
These three areas form my HS curriculum, and provide really rich applications and philosophical fodder ("is this how rational humans think? Is it intrinsically better, or worse, or completely unrelated?")
Some courses might go from these topics into Bayes Nets and/or Hidden Markov Models, though I think this leans math heavy since its all about probability.
Usually by this point AI courses have become more or less completely focused on ML, often starting with k-means clustering, basic classification/regression using perceptrons or decision trees, ensemble learning/boosting, reinforcement learning (usually in the framework of Markov Decision Processes), and maybe deeper neural networks.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
Matthew W.
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 1 hour ago
Matthew W.
211
answered 1 hour ago
Matthew W.
211
211
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Matthew W. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
add a comment |Â
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
Thank you. We cover some of the topics you mention in our classes on data structures, algorithms, and theory of computation. FWIW, I took 6.034 ~30 years ago. (I assume there have been some changes.)
– Ellen Spertus
22 mins ago
add a comment |Â
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Just as a thought experiment, and a bit orthogonal, have you considered whether it might be better to focus more on what you already do well, going deeper, rather than trying to cover things that might turn out to be sketchy? Not every student, even today, needs, or will go on in, AI.
– Buffy
2 hours ago
Another thought. Would a non-required upper level course with a heavy math requirement be a bad thing? Perhaps as a goad. It might take some lead time so that students have a shot, of course.
– Buffy
2 hours ago
@Buffy I don't think Calculus is the best use of all of our students' time. Also, if too few students sign up for a class, it gets canceled.
– Ellen Spertus
24 mins ago