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
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()
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.
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.
MIT 6.S094: Deep Learning for Self-Driving Cars A Visual Introduction to Machine Learning Finding better boundaries Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is 73m, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data. For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified. The best split Recursion
CS446: Fall 2017 - RELATE Course Description The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches. Topics to be covered include: Required text Text: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press Exams Exam will be in class exam with 75 minutes. Homework Please see the class calendar for homework deadlines. Scribe Scribe Submission Project Project Description
How to get started with Machine Learning on Bluemix There is a lot of talk about artificial intelligence (AI) these days, especially since Google’s AlphaGo beat a Go world champion. Companies like IBM are using this technology already in a number of products. For example on Bluemix developers can easily consume cognitive Watson services like speech or image recognition that use machine and deep learning under the cover. While these Watson services are very easy to use for developers, sometimes you want to use machine learning for other scenarios. Since this technology looks so promising and powerful I’m trying to learn machine learning. However I found the open source framework Scikit Learn which seems powerful and at the same time it provides relative simple samples to get started. You can run this sample easily via Bluemix. One nice thing about Scikit Learn is that it provides some sample data for your first steps in machine learning. The core difference to classic programming is that you don’t code any longer rules.
Syllabus | CS 231N The Spring 2020 iteration of the course will be taught virtually for the entire duration of the quarter. (more information available here ) Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm. Discussion sections will (generally) be Fridays 12:30pm to 1:20pm. This is the syllabus for the Spring 2020 iteration of the course. 10 Machine Learning Examples in JavaScript Machine learning libraries are becoming faster and more accessible with each passing year, showing no signs of slowing down. While traditionally Python has been the go-to language for machine learning, nowadays neural networks can run in any language, including JavaScript! The web ecosystem has made a lot of progress in recent times and although JavaScript and Node.js are still less performant than Python and Java, they are now powerful enough to handle many machine learning problems. Web languages also have the advantage of being super accessible - all you need to run a JavaScript ML project is your web browser. Most JavaScript machine learning libraries are fairly new and still in development, but they do exist and are ready for you to try them. 1. Brain is a library that lets you easily create neural networks and then train them based on input/output data. Deep playground Educational web app that lets you play around with neural networks and explore their different components. Synaptic