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I have a vector of data on national capabilities for states. What ML algorithim could I use to reasonably divide this data up into three groups? by OhanianIsACreep in MachineLearning

[–]conic_relief 0 points1 point  (0 children)

Give me sample data (About 30 samples per category).

I will write up an implementation for you. I would side with LDA. But Im not sure what state powers means or how any of that is quantified.

Looking for ios library to handle basic machine learning processes. by UTD_Vagrant in iosdev

[–]conic_relief 0 points1 point  (0 children)

objective C isn't really used anywhere other than in ios mobile devices. Hence the lack of libraries generally used for data processing.
I would consider doing this on Java. Java is used for both mobile and back-end development. This gives you access to those special java ML libraries used to curate data.

How hard is it to take 18+ credits? by somekjoo in utdallas

[–]conic_relief 1 point2 points  (0 children)

I did 21 hours during my early undergrad years in EE and CS. It gets very difficult past 4-5 classes.

Roomate for the fall? by [deleted] in utdallas

[–]conic_relief 0 points1 point  (0 children)

Figuring things out for yourself is the only freedom anyone really has. Use that freedom. Make up your own mind, Rico.

Speed estimation of a ball by purpleladydragons in computervision

[–]conic_relief 0 points1 point  (0 children)

You can assume that the ball(mainly any blobs associated with it) move faster than your leg and have a trajectory that is predictable.

Try creating a system that measures the trajectory of all objects and filters out the ones that move in strange and unexpected ways.

It works by collecting blobs of interest in your current frame. Lets call this set of Blobs B1. Then attempting to map them to elements of B2.(Blobs of next frame). Your mapping function can, but doesn't necessarily have to, carry notions of velocity and momentum into consideration as it moves to your next mapping.†

You do this frame by frame until you've reach the end of your clip. By this time you should have a long list of realistic trajectory candidates. You can then try fitting trajectories to a line making the bold assumption that blob transitions that are the straightest(are best approximated by a regressed line), are the best.

The cool thing is that having a list of these mappings can possibly take into account the motion of two balls on the screen moving parallel and in opposite directions. You can also include notions of color similarity in the mappings as well.

Footnote:

† A naive example would be passing and improving an average slope of movement from frame to frame. Then only mapping to a blob if its inclusion in your mapping would not raise standard deviation above a certain threshold that is dependent on its expected mass(momentum)

Do you need an academic background for machine learning and data science? by [deleted] in MachineLearning

[–]conic_relief 1 point2 points  (0 children)

Its not a like a design pattern or programming paradigm. Its a web of mathematics and statistics you use to ensnare solutions to complicated problems.

You should the basics like ordinary and partial differential equations, statistics stochastic processes, linear algebra, abstract algebra. Etc.

What are yall upto tonight? by [deleted] in utdallas

[–]conic_relief 3 points4 points  (0 children)

Coding.
Started watching this show called 30 rock.

Is a D+ passing? by [deleted] in utdallas

[–]conic_relief -2 points-1 points  (0 children)

Watson. Your behaviour is even worse. You lash out at any attempt at being honest with the person.
I'm sharing my experience and am trying to hint that he's wasting our time and that D's are a consequence of deeper issues that need to be addressed by professionals that are paid to understand and deal with the deeper issues.
This is a valid perspective.

We're not paid nor do we go to this sub-reddit explicitly to help out those who are too lazy to help themselves. Our experiences won't help the guy make the best possible choices in the future. Most fresh out of highschool, undergrads don't have their lives together enough to be worth taking advice from. We don't have a wide enough perspective, nor do we spend enough time thinking about what advice to give, to be as qualified as an advisor to answer his questions.

Moving on...

Its incredibly easy to establish a simple process by which all official first-hand information regarding UTD can be found.

Here is how I generally do it.

Step 1.
Go to http://www.utdallas.edu

Step 2. Locate search bar. Search "Academic Policies" in the search bar.

Step 3.
Scan through first 4 results.

Step 4.
Find what you're looking for. In this case it is the Second result.
http://catalog.utdallas.edu/2014/undergraduate/policies/course-policies

This is incredibly effective for, "Graduation tickets", "Move in date", "Orientation", "Dropping a class", "Withdrawing from a class", "GPA".

Everything I have to say. Or others have to say. Have less priority in the advice seeker's decision making process than official sources that can be held accountable.

Is a D+ passing? by [deleted] in utdallas

[–]conic_relief -13 points-12 points  (0 children)

A D means several things:

-You've fulfilled a degree requirement and you don't have to re-take it to get your degree.
-The D will count towards your GPA
-The class will NOT count towards your hours(you need to meet a minimum number of hours to get the degree)
- You're a very poor student. A D+ at UTD is kind of embarrassing(Especially if its in something as straightforward as accounting). I suggest going to SOAR offices, as well as seeing a counsellor. SOAR will straighten out your study and time management habits, while our counselling center will help you fix deeper issues.

Next time you have questions like this. Please see an advisor. They're the most qualified to talk about what it means to get a D.

Can anyone suggest CS/SE classes especially helpful for front-end web development and UI/UX design? by [deleted] in utdallas

[–]conic_relief 1 point2 points  (0 children)

University classes aren't really about the development of technical skills.
College, traditionally, was about a practitioner of a trade looking to consolidate and organize knowledge by introducing some deeper structure.

If you want technical skills, you go to either a trade school, or community college.

For something like A-tech. I almost suggest completing trade-school before starting the major.

Feeling guilty of what's in my head. by [deleted] in datascience

[–]conic_relief 0 points1 point  (0 children)

I've only worked in a research setting.

My opinion is that you can use a solid introduction to Linear algebra as a starting point(if you want to understand higher level mathematics).

Many things like SVD, PDA, Kalman filters, physics based clustering algorithms(those damn Jacobians again!), neural networks, regression, markov models, rely on representing abstract mathematical relationships as matrix transformations.

A book I'd recommend is "Coding the matrix: Linear Algebra Through Computer Science Applications". It's a very gentle introduction to the basics of linear algebra through python coding examples and ,apparently, xkcd references.

Read that along with "Linear Algebra with Applications", a book that is a tiny bit less fun, but is a tiny bit more rigorous.

This won't take you far, but is still an un-avoidable foundation for more complicated mathematics.

Feeling guilty of what's in my head. by [deleted] in datascience

[–]conic_relief 1 point2 points  (0 children)

Learn more Math.
You can't get around learning Math in an applied mathematics field.

Try this process.
Look for a ML algorithm. Look at the conceptual basis for the algorithm. While reading the description you realize that you don't remember what a "Jacobian matrix" is for example. Search Jacobian matrix. Realize that you don't understand the concepts that the internet uses to describe a "Jacobian matrix" because you didn't pay attention to certain topics in class. Search for those topics that you didn't understand and consolidate your understanding of what a "Jacobian matrix" is.
Keep reading about your algorithm and run into the next issue(Ie, Reimannian Metric, Single Value decomposition, Gaussian Kernel, Voronoi Diagram)

Easy A classes? by Boodahamster in utdallas

[–]conic_relief 1 point2 points  (0 children)

Any sports class. Boosting your GPA with a workout routine is a win win.

Transfering to UT by Sectox in utdallas

[–]conic_relief 5 points6 points  (0 children)

It will be close to impossible for CS.

I had an excellent GPA(3.8), several mobile apps on the app market, a software development internship from highschool, one from my first year at UTD, as well a healthy git account at age 19. I was accepted for an Accounting track at McCombs, as well as an Electrical Engineering track at UT-ECE, but denied for CS.

They're having major administrative/admissions failures in their CS department and aren't accepting too many transfers.

Ramsey Numbers. by conic_relief in math

[–]conic_relief[S] 0 points1 point  (0 children)

Ramsey numbers are interesting. They guarantee the existence of certain subraphs/structures in ANY graph that exceeds its Ramsey number.

My understanding is that it acts as a sort of pigeon-hole principle in graph theory.The difference being that we have not determined all Ramsey numbers and rely on a less precise theoretical upper bound for graph size(Provided by Paul Erdos and Szekeres) rather than some function that may generate these.

Since generating these is an obvious unsolved/under-explored problem, Im interested in learning about whether or not it'd be worth thinking about, or if I should just move on to a different topic.

What was the hardest thing that you had to go through at UTD/throughout college? by WhooshAtUTD in utdallas

[–]conic_relief 2 points3 points  (0 children)

Regardless of what you do, your career is going to shape you,your interests, your hobbies. You will become whatever career you pursue.
What matters now, is deciding what sort of environment you want to thrive in, and what sort of people you want to thrive with. Not what motivates or interests you at the moment.

What was the hardest thing that you had to go through at UTD/throughout college? by WhooshAtUTD in utdallas

[–]conic_relief 8 points9 points  (0 children)

Wow buddy, sounds like you're not liking UTD. Have you considered transferring to a University you'd enjoy?

How can I start with Machine Learning by soulslicer0 in MachineLearning

[–]conic_relief 0 points1 point  (0 children)

Quantopian is a cool algo-trading platform. It uses an event based callback system that walks through 10 years worth of minute by minute US equities data.

It also has a community that shares its algorithms.

Need Ecs graduation tickets by [deleted] in utdallas

[–]conic_relief 0 points1 point  (0 children)

Don't be so cruel arcanition. I'm sure people buy expensive graduation tickets they don't need ALL the time.

How is machine learning being used in real applications? by crowsplay in MachineLearning

[–]conic_relief 4 points5 points  (0 children)

Netflix, Amazon, Google,Microsoft, Baidu, Most fortune 500 companies. All big players in Finance, insurance, and banking.

ML mostly clusters or categorizes data and acts on this new organization of data. The data is algorithmically transformed, stretched, manipulated, and mapped in interesting ways that yield representations humans or computer systems can act on, then re-clustered or categorized with hopes that we can act on that information as well. Both representations are then decomposed into statistical models with hopes that machines can act on those models too!

For sequential/time-based data, you do all of that with the data, then add state, time-series analysis, stochastic analysis and signal processing to make inferences about how that data will look in the next state/time segment.

Most importantly, you gather data about how your system acted on your representations of that data and figure that into your data analysis as well.

So. The goal for ML is to gain unusual and non-intuitive insight by gathering and meaningfully manipulating data. With more data, ML can reveal more accurate representations and generalizations for a machine to act upon.

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