Introduction to Machine Learning and Deep Learning using R (IMDL02) 17th - 18th November
https://www.prstatistics.com/course/introduction-to-machine-learning-and-deep-learning-using-r-imdl02/ Course Overview: In this two day course, we provide an introduction to machine learning and deep learning using R. We begin by providing an overview of the machine learning and deep learning landscape, and then turn to some major machine learning applications. We begin with binary and multiclass classification problems, then look at decision trees and random forests, then look at unsupervised learning methods, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this, we will use the powerful TensorFlow and Keras deep learning toolboxes. As examples of deep learning nets, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks. THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES Wednesday 17th – Classes from 12:00 to 20:00 Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning, deep learning, big data, data science have become widely used and celebrated in the last 10 years. However, their definitions are relatively nebulous, and how they related to one another and to major fields like artificial intelligence and general statistics are not simple matters. In this introduction, we briefly overview the field of machine learning and deep learning, discussing its major characteristics and sub-topics. Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine learning, and is usually the first topic covered in introductions to machine learning. Here, we will cover image classification (itself a “hello world” type problem in machine learning), and particularly focus on logistic regression and support vector machines (SVMs). Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated, which often leads to much improved performance. Thursday 18th – Classes from 12:00 to 20:00 Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in unclassified data. Here, we will look at two of the most widely used unsupervised techniques: k-means clustering and probabilistic mixture models. Topic 5: Introducing artificial neural networks and deep learning. R provides many packages for artificial neural networks and deep learning. These include Keras and TensorFlow, which are in fact interfaces to Python packages. These are the most widely used major packages for deep learning in R. More recently, native support for deep learning using R via Torch has been introduced. We will discuss this, but our focus will be on Keras and TensorFlow given that widespread use. Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful, yet relatively easy to understand, artificial neural networks. They are also the simplest type of deep learning model. Here, we will build and train a multilayer perceptron for a classification problem. Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at image classification, primarily due to their translation invariance, which is highly suitable for computational vision. Here, we use PyTorch to build and train a CNN for image classification. Other upcoming courses FREE 1 DAY INTRO TO R AND R STUDIO (FIRR01) https://www.prstatistics.com/course/free-1-day-intro-to-r-and-r-studio-firr01/ 20 October 2021 Introduction to generalised linear models using R and Rstudio (IGLM04) https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm04/ 3 November 2021 - 4 November 2021 Introduction to mixed models using R and Rstudio (IMMR05) https://www.prstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr05/ 10 November 2021 - 11 November 2021 Introduction to Machine Learning and Deep Learning using R (IMDL02) https://www.prstatistics.com/course/introduction-to-machine-learning-and-deep-learning-using-r-imdl02/ 17 November 2021 - 18 November 2021 Model selection and model simplification (MSMS02) https://www.prstatistics.com/course/model-selection-and-model-simplification-msms02/ 24 November 2021 - 25 November 2021 Species distribution modelling with Bayesian statistics in R (SDMB03) www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb03/ 6 December 2021 - 10 December 2021 Introduction to Hidden Markov and State Space models (HMSS01) https://www.prstatistics.com/course/introduction-to-hidden-markov-and-state-space-models-hmss01/ 8 December 2021 - 9 December 2021 Time Series Data Analysis (TSDA01) https://www.prstatistics.com/course/time-series-data-analysis-tsda01/ 14 December 2021 - 17 December 2021 Bayesian Data Analysis (BADA01) https://www.prstatistics.com/course/bayesian-data-analysis-bada01/ 10th January 2022 - 14th January 2022 Introduction to Stan for Bayesian Data Analysis (ISBD01) https://www.prstatistics.com/course/introduction-to-stan-for-bayesian-data-analysis-isbd01/ 18th January 2022 - 20th January 2022 Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM08) https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm08/ 1st February 2022 - 4th February 2022 Species Distribution Modeling using R (SDMR04) www.prstatistics.com/course/species-distribution-modeling-using-r-sdmr04/ 21 September 2021 - 30 September 2021 Introduction to eco-phylogenetics and comparative analyses using R (ECPH01) This course will be delivered live https://www.prstatistics.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph01/ 22 September 2021 - 28 September 2021 Functional ecology from organism to ecosystem: theory and computation (FEER02) https://www.prstatistics.com/course/functional-ecology-from-organism-to-ecosystem-theory-and-computation-feer02/ 5th September 2022 - 9th September 2022 -- Oliver Hooker PhD. PR statistics Species Distribution Modeling using R 21 September 2021 - 30 September Introduction to eco-phylogenetics and comparative analyses using R 22 September 2021 - 28 September 2021 Multivariate analysis of ecological communities in R with the VEGAN package 4 October 2021 - 8 October Introduction to Data Wrangling and Data Visualization using R 4 October 2021 - 8 October 2021 Introduction to Bayesian modelling with INLA 4 October 2021 - 8 October 2021 Landscape genetic data analysis using R 18 October 2021 - 27 October 2021 FREE 1 DAY INTRO TO R AND R STUDIO 20 October 2021 Introduction to generalised linear models using R and Rstudio 3 November 2021 - 4 November 2021 Introduction to mixed models using R and Rstudio 10 November 2021 - 11 November 2021 Introduction to Machine Learning and Deep Learning using R 17 November 2021 - 18 November 2021 Model selection and model simplification 24 November 2021 - 25 November 2021 Species distribution modelling with Bayesian statistics in R 6 December 2021 - 10 December 2021 [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
