Tag: university

Understanding Machine Learning (free e-book)

Understanding Machine Learning (free e-book)

Shai Shalev-Shwartz and Shai Ben-David of the Hebrew University of Jerusalem made their machine learning book free to download.

The book covers the basic foundations up to advanced theory and algorithms. I copied the table of contents below. It’s kind of math heavy, but well explained with visual examples and pseudo-code.

Moreover, the book contains multiple exercises for you to internalize the knowledge and skills.

As an added bonus, the professors teach a number of machine learning courses, the lecture slides and materials of which you can also access for free via the book’s website.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

About the book

If you want to reward the professors for their efforts, please do buy a hardcopy version of book.

Table of contents

Part I: Foundations

  • A gentle start
  • A formal learning model
  • Learning via uniform convergence
  • The bias-complexity trade-off
  • The VC-dimension
  • Non-uniform learnability
  • The runtime of learning

Part II: From Theory to Algorithms

  • Linear predictors
  • Boosting
  • Model selection and validation
  • Convex learning problems
  • Regularization and stability
  • Stochastic gradient descent
  • Support vector machines
  • Kernel methods
  • Multiclass, ranking, and complex prediction problems
  • Decision trees
  • Nearest neighbor
  • Neural networks

Part III: Additional Learning Models

  • Online learning
  • Clustering
  • Dimensionality reduction
  • Generative models
  • Feature selection and generation

Part IV: Advanced Theory

  • Rademacher complexities
  • Covering numbers
  • Proof of the fundamental theorem of learning theory
  • Multiclass learnability
  • Compression bounds
  • PAC-Bayes

Appendices

  • Technical lemmas
  • Measure concentration
  • Linear algebra
Bayesian Statistics using R, Python, and Stan

Bayesian Statistics using R, Python, and Stan

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already.

Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics.

At the Max Planck Institute for Evolutionary Anthropology, Richard teaches Bayesian statistics, and he was kind enough to put his whole course on Statistical Rethinking: Bayesian statistics using R & Stan open access online.

You can find the video lectures here on Youtube, and the slides are linked to here:

Richard also wrote a book that accompanies this course:

For more information abou the book, click here.

For the Python version of the code examples, click here.

A free, self-taught education in Computer Science!

A free, self-taught education in Computer Science!

The Open Source Society University offers a complete education in computer science using online materials.

They offer a proper introduction to the fundamental concepts for all computing disciplines. Evyerthing form algorithms, logic, and machine learning, up to databases, full stack web development, and graphics is covered. Moreover, you will acquire skills in a variety of languages, including Python, Java, C, C++, Scala, JavaScript, and many more.

According to their GitHub page, the curriculum is suited for people with the discipline, will, and good habits to obtain this education largely on their own, but who’d still like support from a worldwide community of fellow learners.

Curriculum

  • Intro CS: for students to try out CS and see if it’s right for them
  • Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
  • Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student’s interests
  • Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
  • Pro CS: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job

It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies. Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible, each class’s prerequisites are specified so that you can design a logical but non-linear progression based on the class schedules and your own life plans.

Links to the contents

Links to the curriculum (v8.0.0)

How to Speak – MIT lecture by Patrick Winston

How to Speak – MIT lecture by Patrick Winston

Patrick Winston was a professor of Artificial Intelligence at MIT. Having taught with great enthusiasm for over 50 years, he passed away past June.

As a speaker [Patrick] always had his audience in the palm of his hand. He put a tremendous amount of work into his lectures, and yet managed to make them feel loose and spontaneous. He wasn’t flashy, but he was compelling and direct.

Peter Szolovits via http://news.mit.edu/2019/patrick-winston-professor-obituary-0719

I’ve written about Patrick’s MIT course on Artificial Intelligence before, as all 20+ lectures have been shared open access online on Youtube. I’ve worked through the whole course in 2017/2018, and it provided me many new insights into the inner workings of common machine learning algorithms.

Now, I stumbled upon another legacy of Patrick that has been opened up as of December 20th 2019. A lecture on “How to Speak” – where Patrick explains what he think makes a talk enticing, inspirational, and interesting.

Patrick Winston’s How to Speak talk has been an MIT tradition for over 40 years. Offered every January, the talk is intended to improve your speaking ability in critical situations by teaching you a few heuristic rules.

https://ocw.mit.edu/resources/res-tll-005-how-to-speak-january-iap-2018/

That’s all I’m going to say about it, you should have a look yourself! If you don’t apply these techniques yet, do try them out, they will really upgrade your public speaking effectiveness:

StatQuest: Statistical concepts, clearly explained

StatQuest: Statistical concepts, clearly explained

Josh Starmer is assistant professor at the genetics department of the University of North Carolina at Chapel Hill.

But more importantly:
Josh is the mastermind behind StatQuest!

StatQuest is a Youtube channel (and website) dedicated to explaining complex statistical concepts — like data distributions, probability, or novel machine learning algorithms — in simple terms.

Once you watch one of Josh’s “Stat-Quests”, you immediately recognize the effort he put into this project. Using great visuals, a just-about-right pace, and relateable examples, Josh makes statistics accessible to everyone. For instance, take this series on logistic regression:

And do you really know what happens under the hood when you run a principal component analysis? After this video you will:

Or are you more interested in learning the fundamental concepts behind machine learning, then Josh has some videos for you, for instance on bias and variance or gradient descent:

With nearly 200 videos and counting, StatQuest is truly an amazing resource for students ‘and teachers on topics related to statistics and data analytics. For some of the concepts, Josh even posted videos running you through the analysis steps and results interpretation in the R language.


StatQuest started out as an attempt to explain statistics to my co-workers – who are all genetics researchers at UNC-Chapel Hill. They did these amazing experiments, but they didn’t always know what to do with the data they generated. That was my job. But I wanted them to understand that what I do isn’t magic – it’s actually quite simple. It only seems hard because it’s all wrapped up in confusing terminology and typically communicated using equations. I found that if I stripped away the terminology and communicated the concepts using pictures, it became easy to understand.

Over time I made more and more StatQuests and now it’s my passion on YouTube.

Josh Starmer via https://statquest.org/about/