Machine Learning Crash Course: Part 2
ml.berkeley.edu
Machine Learning Crash Course: Part 2
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Re: Machine Learning Crash Course: Part 2
#2Here's Part 1: https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/
Re: Machine Learning Crash Course: Part 2
#3does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?
Re: Machine Learning Crash Course: Part 2
#4does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?
The main reason we see all these relatively information-less blog posts / tutorials is because most people on HN are afraid of the math. If you're not afraid of the math (or have a relatively strong math background), then honestly you can jump into the CS 229 Stanford lecture notes and homework assignments. If that's a little too heavy for you, you can also start with the MOOC version of that course (which is a very watered down version of CS 229).
Re: Machine Learning Crash Course: Part 2
#5does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?
You can order the standard machine learning texts from most to least math-y, and least to most modern:
- Pattern Recognition and Machine Learning (Bishop 2007)
- Machine Learning: A Probabilistic Perspective (Murphy 2012)
- Deep Learning (Goodfellow, Bengio, Courville 2016)
If you want cutting-edge material, read the Deep Learning book (which is still quite technical, though some of its content may be outdated in a few years). If you want timeless mathematical foundations very clearly presented, read Bishop. Murphy is a good middle ground.
If you're self-teaching and have trouble focusing on a textbook for long periods, the Stanford CS229 lectures combined with Andrew Ng's course notes and assignments are probably the best resource. They are still quite rigorous and working through them will give you a solid foundation, after which you'll be more prepared to understand the deeper content in any of the texts above.
Re: Machine Learning Crash Course: Part 2
#6does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?
You can order the standard machine learning texts from most to least math-y, and least to most modern: - Pattern Recognition and Machine Learning (Bishop 2007) - Machine Learning: A Probabilistic Perspective (Murphy 2012) - Deep Learning (Goodfellow, Bengio, Courville 2016) If you want cutting-edge material, read the Deep Learning book (which is still quite technical, though some of its content may be outdated in a f…
Where would you place "Elements of Statistical Learning" in relation to these, if you know?
Re: Machine Learning Crash Course: Part 2
#7does anyone have recommendation on a text that is mathematically heavy but also looks at modern approaches/appleications?
Also consider Foundations of Machine Learning (2012) by Meryar Mohri. It is a very good book with a statistical learning approach to ML. If you're interested by statistical guarantees to ML algorithms, eg. why the SVM algorithm works and its sample complexity (the expected out of sample error in function of the sample size), it's the book you need.
Re: Machine Learning Crash Course: Part 2
#8Now that there's a bunch of AI/ML-related links in the front page, probably now is the best time to ask:
As I learn deep learning, from the practical point-of-view, I found that the idea is simply to feed some "black box" with labeled data so next time it can give you correct label given unlabeled data. In essence, it's pattern recognition. What do you think?
And then, as I try to find use cases for ML (you know, finding problem for the solution), I found that actually, many problems that can be solved with ML can actually be solved with rules. For example, detecting transaction fraud. You just need to find the right rules/formula. Forget ML, if you can't hardcode if-else, just use rules engine. What do you think?
So, I'm starting to think that ML is good for solving problems where (1) we're too lazy to formulate the rules, or (2) the data is too complex/big to analyze by rules (as in, understanding image or voice). What do you think?
Re: Machine Learning Crash Course: Part 2
#9Now that there's a bunch of AI/ML-related links in the front page, probably now is the best time to ask: As I learn deep learning, from the practical point-of-view, I found that the idea is simply to feed some "black box" with labeled data so next time it can give you correct label given unlabeled data. In essence, it's pattern recognition. What do you think? And then, as I try to find use cases for ML (you know, fin…
As I understand it, ML is about the finding of those rules. The big example I can think of is stocks -- what signals are important in determining the fiscal health of a company or stock? Every trader or investor has their own ruleset they believe in, ML (and specifically Quant) is meant to dig through as much data as possible to find those signals that affect the as-best-understood quality of a pick.
For example, a human can think 'oh I will look at numbers like P/E and growth and...' whereas with ML you can feed it all those, and things like number of times the CEO has tweeted in the past week or the number of PR articles or even the language in PRs released to see if there is some strong correlative signal in the bunch, or if it is all just noise.
Re: Machine Learning Crash Course: Part 2
#10Earlier quoted context omitted.
You can order the standard machine learning texts from most to least math-y, and least to most modern: - Pattern Recognition and Machine Learning (Bishop 2007) - Machine Learning: A Probabilistic Perspective (Murphy 2012) - Deep Learning (Goodfellow, Bengio, Courville 2016) If you want cutting-edge material, read the Deep Learning book (which is still quite technical, though some of its content may be outdated in a f…
Where would you place "Elements of Statistical Learning" in relation to these, if you know?
You want ISLr (http://www-bcf.usc.edu/~gareth/ISL/), not ESL. ESL is the prototype for the former.