Become a fan of Slashdot on Facebook

 



Forgot your password?
typodupeerror

Comment Sets a great precedent for AT&T, Comcast, etc. (Score 1) 337

Surprise, surprise. Being rude to a company results in bad service from that company. Hardly news except that it was [AT&T / Comcast / insert any company] that was the victim. Maybe the entitled customer has learned his lesson, but probably not.

Wrong message to send to corporations.

Comment How I came to work in ML (Score 2) 123

I was in the same position as OP about 5 years ago. I have a PhD in CS from many years back but in operating systems and programming languages. Around 2010 I wanted to get into machine learning and decided to enroll part-time in a university to take some classes. Currently, I am leading a small team of engineers that work on ML-related topics.

Here are some points that the OP needs to understand.

1. There are two different levels of expertise with working on machine learning: either as a library/tool user, or as a ML algorithm developer. It is EXACTLY analogous to how one approaches SQL: You can make a great living being a SQL user and knowing how to write efficient queries and build indexes, or you can go deeper and build the SQL engine itself along with its query optimizer, storage layer, etc. If you want to use ML as a library/tool user, you can have a great career as long as you know what tools and algorithms to use. If you want to be a ML algorithm developer, that means you want to work on the innards, such as using new SVM kernels or building new deep learning networks; for this role, you'll usually need a PhD-calibre background heavy in math. I personally started out as a library/tool user with Weka and Mallet, but as I used them more, I was able to understand the math behind them.

2. ML is an abstract field, and it's best to approach it from an applications point of view. Pick a problem that needs ML, such as natural language processing or image recognition. It's important to pick a problem that has an abundant amount of labelled data. There are some fields such as voice recognition where it is terribly difficult to get real labelled data. For NLP (aka computational linguistics), you can start with some basic problems such as document classification (e.g. for this document, is it about sports, business, entertainment, etc.?) or sentiment analytics (e.g. for this Twitter tweet, is it positive or negative?). There are lots of good datasets in the NLP field.

3. You can explore datasets from the Kaggle competitions and the University of California, Irvine, repository: http://archive.ics.uci.edu/ml/

4. Pick a tool and stick with it. I have used Weka, Mallet, and R. You can also use Python and Matlab.

5. When you read the literature, you will find two nearly-synonymous terms: "machine learning" and "data mining". Both are closely related. Machine learning historically comes from the AI community and generally focuses on building better ML algorithms and solving supervised ML problems. Data mining historically comes from the database community and generally focuses on using tools and solving unsupervised ML problems (e.g. finding clusters of similar customers).

6. At the end of the day, creating a better solution does not come down to the ML algorithms themselves. Rather, the better solution comes from the amount of data and what features you are able to extract. As for the many ML algorithms for supervised learning: at the end of the day, your main responsibility will come down to picking the one that best suits your application. It is just like picking which sorting algorithm to use: when do you use Quicksort, and when do you use Mergesort?

7. Here are some really good books that I have personally read:

Beginner level:
- Programming Collective Intelligence by T. Segaran.
- Introduction to Data Mining by P.-N. Tan and M. Steinbach.

Intermediate level:
- Data Mining: Practical Machine Learning Tools by I. Witten and E. Frank. (goes with the Weka tool)

Advanced level:
- Artificial intelligence: A Modern Approach by S. Russell and P. Norvig. (touches on all aspects of AI, such as tic-tac-toe algorithms with minimax and First Order Logic)
- Introduction to Machine Learning by E. Alpaydin

PROTIP: How to tell if you're reading an advanced machine learning book -- if the index contains reference to Vapnik–Chervonenkis dimension or shattering, then the book is hardcore.

Submission + - Socialism apparently doesn't pay off with employee salaries

jmcbain writes: In April 2015, Dan Price, the CEO of online payments company Gravity Payments based in Seattle, announced that all employees would have their salary bumped up to a minimum $70,000. Slashdot covered this news. Since that time, however, things have not gone well. Some employees quit because they felt it was unfair to double the pay of some new hires while the longest-serving staff members got small or no raises. Furthermore, after reducing his own salary from $1M to $70K, Mr. Price is now renting a house ‘to make ends meet’. On an unrelated note, Mr. Price's brother, who is a co-founder of the company, is suing him.

Comment Re:I hope they realize... (Score 1) 264

Since this report is about US students, then these students already have far more opportunity than those in foreign countries. In practical terms, here are the "opportunities" and decisions that US students have:

1. Do I spend another hour watching TV after school, or do I study?
2. Do I go out on Friday to party, or do I work on my homework?
3. Do I choose to focus on getting into college, or not?
4. Do I choose to major in a STEM field, or do I major in a humanities field?

Those are the opportunities and the decisions. Those who can obtain a high-paying software job apparently made the most with what opportunities they had and made the right choices.

Comment Re:I hope they realize... (Score 1) 264

I kinda hate the way "privilege" gets thrown around a lot of the time, but this is pretty much the clearest sense of privilege here.

This is not an intelligent comment. The folks who succeed in getting high-paying software jobs are not privileged. They are the ones who are (1) able to identify where the good jobs are, and (2) take the steps needed to obtain that goal. I don't consider taking the time to learn software skills as some sort of "privilege". If you get a 100K job, it means you are good at it, not because someone handed you that job on a silver platter.

Slashdot Top Deals

An engineer is someone who does list processing in FORTRAN.

Working...