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About The Course

BioNcube, a DBT BioNEST Ag-biotech Incubator and the Statis-tics, Bioinformatics and Data Management (SBDM) Theme (http://data.icrisat.org/) at ICRISAT (https://www.icrisat.org/) in collaboration with Professor Osval A Montesinos-López from Uni-versity of Colima, Mexico (https://www.ucol.mx) is organizing a training workshop on Advanced R & Application of Machine 3rd International Workshop on Genomic Selection in Plant Breeding through Advanced R & Machine Learning The course will be mainly divided into four modules.

First module will given general introduction on Genomic Selec-tion in plant breeding, development of models and application. Various genomic selection models commonly used and basics of mixed model analysis and genomic selection using Bayesian ap-proaches will be discussed. This module will introduce analyzing genomic selection using R. Illustrative examples of various GS models will be discussed with participants.

Second module will focus on basics of R. This module will intro-duce R to participants and will include installation, introduction to RStudio, basic data management with R, package installation, basic programming and use of R Graphics. After covering basic graphics we will also cover advanced graphics by introducing ggplot2 package.

Third module will focus on general machine learning, concepts, algorithms, application in genomic selection, overfitting problem and metrics for the evaluation of prediction performance. Partici-pants will learn logistic regression and penalized logistic regres-sion—with theory and illustrative examples. Concept of multino-mial regression will also be discussed.

Fourth module will be focused on artificial neural networks and deep learning for continuous outcomes. Participants will learn defining different artificial neural networks topologies, activation functions, loss functions, early-stopping method of training, the backpropagation algorithm and examples in keras for binary, ordinal, and continues outcomes and some successful applica-tions of artificial neural networks and deep learnings.

module will be focused on artificial neural networks and deep learning for continuous outcomes. Participants will learn defining different artificial neural networks topologies, activation functions, loss functions, early-stopping method of training, the backpropagation algorithm and examples in keras for binary, ordinal, and continues outcomes and some successful applica-tions of artificial neural networks and deep learnings.

Course Content

Module 1: Genomic Selection & Linear Mixed Models

• Basics of Genomic Selection (GS)
• Elements of mixed models for GS
• Different GS Model
• Illustrative examples

Module 2: Introduction to R

• Introduction to R & RStudio
• R Packages
• R for Data Manipulations
• R Graphics & ggplot2

Module 3: Elements of Machine Learning – I

• Logistic regression
• Penalized logistic regression
• Multinomial regression
• Overfitting problem

Module 4: Elements of Machine Learning – II

• Artificial neural networks vs Deep learning
• Artificial neural network topologies
• Deep learning for continuous outcomes
• Loss functions
• Early-Stopping Method of Training
• The backpropagation algorithm
• Example in Keras for univariate and multivariate outcomes

How to Apply

To apply, please complete following google form with details: https://forms.gle/4j7yBjYxGLCWQWez6

Registration Fee: USD 450 / INR 30,000

Last Date of Application: 20th September 2019

• Selected candidate will be contacted with registration details
• Course fee includes lunch & coffee breaks for 5 days
• Accommodation available in ICRISAT on request
• BioNEST incubatee / startups will be given 50% discount on course fee

Logistics & Accommodation
Ms. Laxmi Sarika
ICRISAT Patancheru, Hyderabad
Telangana, India—502 324
email: L.Sarika@cgiar.org
Office Landline: +91 40 3071 3365
Mobile: +91-9703028538