COMS W4721 Machine Learning for Data Science @ 422 Mudd BuildingSynopsis: This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models. Methods covered in class include linear and logistic regression, support vector machines, boosting, K-means clustering, mixture models, expectation-maximization algorithm, hidden Markov models, among others. Prerequisites: Basic linear algebra and calculus, introductory-level courses in probability and statistics. Text: There is no required text for the course. T.
Machine Learning is Fun! What is machine learning? Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. “Machine learning” is an umbrella term covering lots of these kinds of generic algorithms. Two kinds of Machine Learning Algorithms You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. Supervised Learning Let’s say you are a real estate agent. To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it’s size, neighborhood, etc, and what similar houses have sold for. This is called supervised learning. return price
Understanding LSTM Networks -- colah's blog Posted on August 27, 2015 Recurrent Neural Networks Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks can’t do this, and it seems like a major shortcoming. Recurrent neural networks address this issue. Recurrent Neural Networks have loops. In the above diagram, a chunk of neural network, , looks at some input and outputs a value . These loops make recurrent neural networks seem kind of mysterious. An unrolled recurrent neural network. This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. And they certainly are used! Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. LSTM Networks
How to Think Like a Computer Scientist Learning with Python by Allen Downey, Jeff Elkner and Chris Meyers. This book is now available for sale at Lulu.com. How to Think... is an introduction to programming using Python, one of the best languages for beginners. How to Think... is a Free Book available under the GNU Free Documentation License. Please send suggestions, corrections and comments about the book to feedback{at}thinkpython{dot}com. Download The book is available in a variety of electronic formats: Precompiled copies of the book are available in PDF and Postscript . Translations Here are some translations of the book into other (natural) languages: Spanish translation by Gregorio Inda. Other Free Books by Allen Downey are available from Green Tea Press. If you are using this book and would like to make a contribution to support my work, please consider making a donation toward my web hosting bill by clicking on the icon below.
Masinõpe - Kursused - Arvutiteaduse instituut I. Association rules and decision trees Given by Sven Laur Brief summary: Advantages and drawbacks of machine learning. When is it appropriate to use machine and when knowledge based modelling is more appropriate. overview of standard experiment design. Slides: PDF Video: UTTV(2013) Literature Lecture slides by Tom Mitchell Thomas Mitchell: Machine learning (1997) pages 52 - 80 Complementary exercises Free implementations
Machine Learning Exercises In Python, Part 1 This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here. Part 1 - Simple Linear RegressionPart 2 - Multivariate Linear RegressionPart 3 - Logistic RegressionPart 4 - Multivariate Logistic RegressionPart 5 - Neural NetworksPart 6 - Support Vector MachinesPart 7 - K-Means Clustering & PCAPart 8 - Anomaly Detection & Recommendation One of the pivotal moments in my professional development this year came when I discovered Coursera. This blog post will be the first in a series covering the programming exercises from Andrew's class. While I can explain some of the concepts involved in this exercise along the way, it's impossible for me to convey all the information you might need to fully comprehend it. Examining The Data In the first part of exercise 1, we're tasked with implementing simple linear regression to predict profits for a food truck. data.describe()
A 'Brief' History of Neural Nets and Deep Learning, Part 1 – Andrey Kurenkov's Web World This is the first part of ‘A Brief History of Neural Nets and Deep Learning’. Part 2 is here, and parts 3 and 4 are here and here. In this part, we shall cover the birth of neural nets with the Perceptron in 1958, the AI Winter of the 70s, and neural nets’ return to popularity with backpropagation in 1986. “Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences.” This may sound hyperbolic - to say the established methods of an entire field of research are quickly being superseded by a new discovery, as if hit by a research ‘tsunami’. I am in no capacity an expert on this topic. Let’s start with a brief primer on what Machine Learning is. This generalization principle is so important that there is almost always a test set of data (more examples of inputs and outputs) that is not part of the training set. of Donald Hebb. .
A Programmer's Guide to Data Mining | The Ancient Art of the Numerati Deep Learning Course ⇢ François Fleuret You can find here the materials for the EPFL course EE-559 “Deep Learning”. These documents are under heavy development, in particular due to pytorch updates. Please avoid to distribute the pdf files, and share the URL of this page instead. Info sheet: dlc-info-sheet.pdf We will use the pytorch framework for implementations. Thanks to Adam Paszke, Alexandre Nanchen, Xavier Glorot, Matus Telgarsky, and Diederik Kingma, for their help, comments, or remarks. Course material You will find here the slides I use to teach, which are full of “animations” and not convenient to print or use as notes, and the handouts, with two slides per pages. Practical session prologue Helper python prologue for the practical sessions: dlc_practical_prologue.py Lecture 1 (Feb 21, 2018) – Introduction and tensors Lecture 2 (Feb 28, 2018) – Machine learning fundamentals Empirical risk minimization, capacity, bias-variance dilemma, polynomial regression, k-means and PCA. Cross-entropy, L1 and L2 penalty.
How to get started with machine learning? Contrary to the other advice around here, I would strongly advise NOT taking a course. I think it is a good idea at some point, but it is not the first thing you should be doing. The very first thing you should do is play! Identify a dataset you are interested in and get the entire machine learning pipeline up and running for it. Here's how I would go about it. 1) Get Jupyter up and running. 2) Choose a dataset. I wouldn't collect my own data first thing. And don't go with a neural net first thing, even though it is currently in vogue. 3) Write a classifier for it. For this step, let scikit-learn be your guide. 4) Now you've built out the supervised machine learning pipeline all the way through! 4a) Experiment with different models: Bayes' nets, random forests, ensembling, hidden Markov models, and even unsupervised learning models such as Guassian mixture models and clustering. I hope this helps.
Yudkowsky - Bayes' Theorem An Intuitive Explanation of Bayes' Theorem Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. This page has now been obsoleted by a vastly improved guide to Bayes's Theorem, the Arbital Guide to Bayes's Rule. Please read that instead. Seriously. Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. It's this equation. So you came here. Why does a mathematical concept generate this strange enthusiasm in its students? Soon you will know. While there are a few existing online explanations of Bayes' Theorem, my experience with trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Or so they claim. And let's begin. Here's a story problem about a situation that doctors often encounter: What do you think the answer is? Do you want to think about your answer again? Group 1: 100 women with breast cancer. Which is common sense.
Deep Learning course: lecture slides and lab notebooks | lectures-labs This course is being taught at as part of Master Datascience Paris Saclay Table of contents The course covers the basics of Deep Learning, with a focus on applications. Lecture slides Note: press “P” to display the presenter’s notes that include some comments and additional references. Lab and Home Assignment Notebooks The Jupyter notebooks for the labs can be found in the labs folder of the github repository: git clone These notebooks only work with keras and tensorflow Please follow the installation_instructions.md to get started. Direct links to the rendered notebooks including solutions (to be updated in rendered mode): Lab 1: Intro to Deep Learning Lab 2: Neural Networks and Backpropagation Lab 3: Embeddings and Recommender Systems Lab 4: Convolutional Neural Networks for Image Classification Lab 5: Deep Learning for Object Dection and Image Segmentation Lab 6: Text Classification, Word Embeddings and Language Models Lab 8: Intro to PyTorch License
Machine learning algorithm cheat sheet | Microsoft Docs The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for a predictive analytics model. Azure Machine Learning Studio has a large library of algorithms from the regression, classification, clustering, and anomaly detection families. Each is designed to address a different type of machine learning problem. Download: Machine learning algorithm cheat sheet Download the cheat sheet here: Machine Learning Algorithm Cheat Sheet (11x17 in.) Download and print the Machine Learning Algorithm Cheat Sheet in tabloid size to keep it handy and get help choosing an algorithm. More help with algorithms Note Try Azure Machine Learning for free No credit card or Azure subscription needed. Notes and terminology definitions for the machine learning algorithm cheat sheet The suggestions offered in this algorithm cheat sheet are approximate rules-of-thumb.
The Unreasonable Effectiveness of Recurrent Neural Networks There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I’ve in fact reached the opposite conclusion). Fast forward about a year: I’m training RNNs all the time and I’ve witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. We’ll train RNNs to generate text character by character and ponder the question “how is that even possible?”