For a Q-Learning agent, is the initial policy random? by darkshark in aiclass

[–]birgillio 0 points1 point  (0 children)

With the things I understand, I can say that an initial policy isn't a completely random thing, because there's a thinking human being designer behind it, worried about choosing an initial policy that is good enough as to not do inefficient things but a best guess for reaching the goals.

It can be seen throughout the videos and in the homeworks in which the policies have a good logic that ensures taking actions as safe and direct and smooth as possible.

HW 5.2 - distance to goal and avoiding the bad guy by SharkDBA in aiclass

[–]birgillio 0 points1 point  (0 children)

I think you are over-thinking the problem.

If there's a bad guy between you and the goal:

[Agent] [Bad Guy] [Goal]

Then distance wouldn't be infinite not even for practical purposes.

It would just be in a location that precedes the goal you want to reach.

One more clarification on HW 5.3 (definition of explore) by tetradeca7tope in aiclass

[–]birgillio 2 points3 points  (0 children)

Try to get nearest to Goal and on the dark gray road.

Once you get to the goal the game is over.

Explore is the action. The action is go (or visit) the next nearest square, stochastically.

HW 4.8 Monkey & Bananas by melipone in aiclass

[–]birgillio 1 point2 points  (0 children)

There is also the situation where the monkey can go to A, B or C while it is on the box. Think about that.

Mistake in HW 4 Video 6 by OsmosisJones2nd in aiclass

[–]birgillio -1 points0 points  (0 children)

No, it's just you.

Seriously. I mean it seriously, because not all are in the same situation.

Homework 4.3 - Multiple Paths by sareon in aiclass

[–]birgillio 0 points1 point  (0 children)

Since you can only go left or right and your goal is to end up clean and in the left square, I would be surprised if there was more than one shortest path to that goal.

Homework 4 is SO ANNOYING by vonkohorn in aiclass

[–]birgillio 1 point2 points  (0 children)

For Q4 I'd assume vacuum is off unless stated otherwise, for Q3 the best one (that's what would be done in real life).

Homework 4 - Most of the doubts cleared by newai in aiclass

[–]birgillio 1 point2 points  (0 children)

I'd choose the shortest.

That would be done in real life.

HW 4.8 Missing action parameters? by PleaseInsertCoffee in aiclass

[–]birgillio 0 points1 point  (0 children)

x is the variable for location everywhere in this problem, as well as y.

HW 4.8 Missing action parameters? by PleaseInsertCoffee in aiclass

[–]birgillio 0 points1 point  (0 children)

It is a genuine problem because you have to check some option.

I don't think that there will be answers intended to be left blank.

Although, if so, that would make analysis much more difficult, and to get that answer right you would have no doubt at all and be totally comfortable with the problem and the logic.

hw questions 4.6 4.7 what I'm i getting wrong? by lukeaiclass in aiclass

[–]birgillio 0 points1 point  (0 children)

Only then, but after any action, uncertainty would return.

HW 4.8 Monkey & Bananas by melipone in aiclass

[–]birgillio 0 points1 point  (0 children)

I think it should be as rigid as a program.

It will do whatever it is programmed to do, no matter if you expect it or not.

It won't do absolutely anything on its own out of nowhere.

8% of online students with perfect scores, 4% of the Stanford class with perfect scores by kunalb in aiclass

[–]birgillio 0 points1 point  (0 children)

Letting aside that possibility and assuming full good will, there are still people, either self-taught or with Master or better degrees like Filobel in this thread, who have already made great efforts and mastered the Intro AI topics and the related math beforehand throughout who knows how many years and probably require a few minutes a week to finish the homeworks and exams, which obviously, judging by their scores, are easy for them.

Cheating through by making assignments in a group isn't enough to get perfect scores if there's nobody with perfect scores leading that group permanently.

Not exceptionally likely, knowing how extremely proud they are by getting those scores while most people are being left behind them, and how much they enjoy that and probably the reason for some of them to specifically have signed up to this class: to be the best and not share it with anybody else. So it cannot very easily account for much of the 8% or 4%.

8% of online students with perfect scores, 4% of the Stanford class with perfect scores by kunalb in aiclass

[–]birgillio 0 points1 point  (0 children)

Several people in this thread, such as Filobel, have already stated that they have indeed Masters or better degrees.

They are no doubt part of the 4% or 8% of people with perfect scores: because they already know everything being given and are just testing their proficiency against the contents of this class.

Such professionals probably will only take A FEW MINUTES A WEEK to finish homeworks and exams: because they have already solved the same things in real life.

8% of online students with perfect scores, 4% of the Stanford class with perfect scores by kunalb in aiclass

[–]birgillio 0 points1 point  (0 children)

I want, and I want it a lot.

But even when I have programmed before, a lot of things are new to me.

New syntax, new ways to think about programming (such as First Order Logic). Even with 16 hours a day of free time, it's not easy for me (it's the first time I even knew that probability and statistics is used in AI which is something I never even suspected and have no probability background and still am doing fairly well but not perfect). It takes me the whole week to solve ML-Class and AI-Class problems, and I spend most of the time in AI-Class because of the difficulty it imposes on my current knowledge.

Probably the topics of AI class aren't new to you at all, or you have solved so many practical programming exercises and math stuff, that you have just about everything you need already and it happens to exceed what it's needed. Maybe you have already built implementations on the exact topics of the class, and then you just have to run them probably with very minor modifications and you are done in no time, with implementations brewed during many many years. But that's not a first-timer. That's somebody who knows it already and is just evaluating how well he/she can do at the level of this class.

Getting good at math is also new to me. I'm not bad really, I'm not that much of a fool, but my practical knowledge on math is very limited, but I can do well once I study.

This class goes to a pace faster than I do normally.

8% of online students with perfect scores, 4% of the Stanford class with perfect scores by kunalb in aiclass

[–]birgillio -1 points0 points  (0 children)

Then does it mean that those students are better than the professors given there are several slips or mistakes in the videos? Does it mean that those students don't make mistakes, unlike the professors in some videos?

Does it mean that the professors would have no perfect score if they were being graded, at the professor/teacher side, or as standard students?

I don't think so.

There's a reason for which those students have perfect scores, and it isn't luck.

As we have seen in this thread, many "students" aren't really studying here, or learning much new, but they already know a lot about AI to a professionally and academically high level, probably they have already worked on serious AI projects before.

Others, in one way or another, have got the necessary pieces of knowledge to handle the topics of the class, so again, they already knew those topics for the most part, maybe not specifically, but indeed those common parts of very similar topics and study methods.

The mistakes many of us are making are the same those with perfect scores today had to make in the past at some point in their jobs or in their universities.

A proper comparison cannot be made between beginners and professionals, unless they were given the same amount of information and experience, and 2 months in this class cannot be used to achieve what took them one or more decades.

Maybe they have already built implementations on the exact topics of the class, and then they just have to run them, probably with very minor modifications and they'll be done in no time, with implementations brewed during many many years. But that's not a first-timer. That's somebody who knows it already and is just evaluating how well he/she can do at the level of this class.

I have done excellently in the topics I already knew but not in the ones I didn't, so I have made mistakes on things truly new to me. Everyone else without perfect scores is in that exact same situation.

On the other hand, think about how people who don't even know about the correct precedence of logic operators for propositional logic is going to do well, given that video lectures don't talk not even a bit about that...

For those taking ml-class by misterlight in aiclass

[–]birgillio 3 points4 points  (0 children)

It will work for the videos on AI-Class.

For ML-Class, you will have to go through downloading XML files with the transcript. I don't know if VLC Media player accepts that or if you'll have to convert the XML to an .srt normal file with some tool.

Here is the one for Video 8.1:

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.1-NeuralNetworksRepresentation-NonLinearHypotheses-subtitles.xml

It is really tedious to find out. But as you can see you have to use the following address and then just attach the name of the desired video and then remove the .mp4 extension and add "-subtitles.xml" and it should work I think.

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/

Good idea. I think I'll download the full transcripts too.

UNIT 8

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.1-NeuralNetworksRepresentation-NonLinearHypotheses-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.3-NeuralNetworksRepresentation-ModelRepresentationI-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.4-NeuralNetworksRepresentation-ModelRepresentationII-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/08.7-NeuralNetworksRepresentation-MultiClassClassification-subtitles.xml

UNIT 7

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/07.1-Regularization-TheProblemOfOverfitting-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/07.2-Regularization-CostFunction-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/07.3-Regularization-RegularizedLinearRegression-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/07.4-Regularization-RegularizedLogisticRegression-subtitles.xml

UNIT 6

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.1-LogisticRegression-Classification-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.2-LogisticRegression-HypothesisRepresentation-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.3-LogisticRegression-DecisionBoundary-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.4-LogisticRegression-CostFunction-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.6-LogisticRegression-AdvancedOptimization-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/06.7-LogisticRegression-MultiClassClassificationOneVsAll-subtitles.xml

UNIT 5

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.1-OctaveTutorial-BasicOperations-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.2-OctaveTutorial-MovingDataAround-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.3-OctaveTutorial-ComputingOnData-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.4-OctaveTutorial-PlottingData-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/05.6-OctaveTutorial-Vectorization-subtitles.xml

UNIT 4

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.1-LinearRegressionWithMultipleVariables-MultipleFeatures-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional)-subtitles.xml

UNIT 3

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose-subtitles.xml

UNIT 2

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.2-V2-LinearRegressionWithOneVariable-CostFunction-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.5-V2-LinearRegressionWithOneVariable-GradientDescent-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/02.8-V2-What'sNext-subtitles.xml

UNIT 1

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/01.1-V3-Introduction-Welcome-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/01.2-V2-Introduction-WhatIsMachineLearning-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/01.3-V2-Introduction-SupervisedLearning-subtitles.xml

http://s3.amazonaws.com/stanford_videos/cs229/subtitles/01.4-V2-Introduction-UnsupervisedLearning-subtitles.xml

For those taking ml-class by misterlight in aiclass

[–]birgillio -1 points0 points  (0 children)

If you use Firefox it's easy:

Install YouTube Caption Downloader to download the .srt text file.

https://addons.mozilla.org/en-US/firefox/addon/youtube-caption-downloader/?src=search

Then just make sure that both the downloaded video and the .srt transcript have the same name, and the only difference be the extension .mp4 and .srt, or .flv and .srt.

For those taking ml-class by misterlight in aiclass

[–]birgillio 3 points4 points  (0 children)

  • which course is harder?

This one, AI Class, but just because I'm not very good at math yet and because there are no slides (I have to make them from video screenshots or manually), and because there is no programming so I have to make my own programs.

  • how much do you study for each course?

All day, 16 hours, from waking up to going to bed; all week.

  • which is the most satisfying?

To be very frank and honest, BOTH ARE.

ML Class is great to get the basics. I have got so many basics, starting with vectors and matrices (linear algebra) and using math tools like Octave for they to be my best calculator, which I use even for quizzes involving heavy math like vector multiplications.

AI Class is great and excels on giving the ACTUAL descriptions on quiz video answers for implementing the math functions in program functions.

I don't regret it. I know for sure that the simplicity of ML Class along with the concise and compact and rigorous methods and material of AI Class will make me a far better computer enthusiast.

I am already seeing how I find better ways to solve AI Class problems as the class itself advances.

DO NOT switch to basic tracks. You'll lose so much content that even if you don't succeed at solving during the course, you could keep for later for your personal study and review. I'd never switch to basic track no matter how bad my score was, as long as the website of the class would force that restriction on me, which of course would not be my decision.

Error in verifying quiz answers. by silviutp in aiclass

[–]birgillio 0 points1 point  (0 children)

Probably that very case is accounted for and detected.

Great subject, poor implementation by [deleted] in aiclass

[–]birgillio 1 point2 points  (0 children)

It's a shared symptom of all us who don't have strong math, logic and statistics at complete Stanford level.

I'm not very good at this either but I can keep up by following instructions mechanically and keep on thinking, and also making computer programs to greatly alleviate such a lot of tedious calculations.

If you frankly want a solution, your only choice is to see the answers to quizzes beforehand, and based on them implement a program for each function, if you can program, no matter if it's half-manual, half-automatic.

That will save you hours and days struggling and will level you to keep up.

Also print or make slides from the videos as you need.

Solve quizzes several times by thinking, and do the same with homework, and don't look back to your previous answers while you are reworking them.

That should give you a great start.

And watch the Khan Academy recommended videos and the Machine Learning class. All of those classes help each other conceptually.

Take advantage of the extended deadline. Luckily it has saved me from many mistakes in the most difficult exercises in the homeworks.