Data science and machine learning

Cours d'analyse de données

Cours signalées avec « Data science and machine learning »

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.

Prerequisites: Some Linux command line experience.  Python experience is strongly advised. 

Format: Virtual

Enseignant: Ramses van Zon
Date: : lun., 13 mai 2024 - 1:00 pm
Nombre de crédits - science des données: 3
Principles and uses of relational databases
Enseignant: Ramses van Zon
Date: : mer., 6 mai 2015 - 9:30 am
Principles and uses of Relational Databases
Enseignant: Ramses van Zon
Date: : mer., 30 oct. 2013 - 2:00 pm
Two days of mixed lectures and hands-on sessions by SciNet analysts on how to scale up and automate your data-centric analysis using Parallel R and Parallel Python.
Date de début: : 22 oct. 2014
Date de fin: : 23 oct. 2014
The goal of this course is to prepare students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. This course can be taken as Seminars in Translational Research (MSC1010Y-1011Y) for students in the Institute of Medical Science. This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance. The course can also be taken as a mini/modular graduate course by Physics students.
Date de début: : 11 oct. 2016
Date de fin: : 17 nov. 2016

This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts.

Classes will be held, April 22 - May 29, 2025, Tuesdays and Thursdays, 11:00am - 12:00pm.

Format: Virtual

Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 22 avril 2025
Date de fin: : 29 mai 2025
Nombre de crédits - science des données: 16

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.

Prerequisites: Some Linux command line experience.  Python experience is strongly advised. 

Format: Virtual

Enseignant: Marco Saldarriaga
Catégorie: Data Science
Date: : lun., 5 mai 2025 - 1:00 pm
Nombre de crédits - science des données: 3

This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.10; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.  Lectures will be 11:00am - 12:00pm, Tuesday and Thursdays.  There will be no lectures on May 30 and June 1.
Format: In-person, in the SciNet teaching room (661 University Ave., suite 1140A).  All lectures will be recorded.

Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 25 avril 2023
Date de fin: : 8 juin 2023
Nombre de crédits - science des données: 16

Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.
Format: In-person, but will also be broadcast and recorded.

Enseignant: Ramses van Zon
Catégorie: Data Science
Date: : lun., 29 mai 2023 - 1:00 pm
Nombre de crédits - science des données: 3

This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts.

Classes will be held, April 23 - May 30, Tuesdays and Thursdays, 11:00am - 12:00pm.

Format: Virtual

Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 23 avril 2024
Date de fin: : 30 mai 2024
Nombre de crédits - science des données: 16

This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.9; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.  Lectures will be 11:00am - 12:00pm, Tuesday and Thursdays.

Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 26 avril 2022
Date de fin: : 2 juin 2022
Nombre de crédits - science des données: 16
This six- or seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.
Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 27 avril 2021
Date de fin: : 3 juin 2021
Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.
Enseignant: Ramses van Zon
Catégorie: Data Science
Date de début: : 30 nov. 2020
Date de fin: : 4 déc. 2020
This seven-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.
Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 9 juin 2020
Date de fin: : 30 juil. 2020
This half-day session offers an overview of machine learning tools available in Python. -- Prerequisites: python programming
Enseignant: Fei Mao
Catégorie: Data Science
Date: : mer., 26 juin 2019 - 9:30 am
Nombre de crédits - science des données: 3
This workshop is part of the 2019 Chemical BioPhysics Symposium.

This workshop will provide an introduction to some of the key methods and concepts in machine learning. We will present and discuss the following topics: - Introduction to machine learning. - Regression. - Bias-variance tradeoff. - Resampling Methods. - Classification algorithms, in general. - Decision trees, kNN, k-means. - Agglomerative clustering.

We will use Python 3, and attendees are expected to be familiar with the Python programming language but extensive programming experience is not required - we will mainly be calling functions in existing packages, not writing large amounts of code.
Enseignant: Marcelo Ponce
Catégorie: Data Science
Date: : ven., 3 mai 2019 - 10:00 am
Nombre de crédits - science des données: 3
Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.
Enseignant: Ramses van Zon
Catégorie: Data Science
Date: : mer., 1 mai 2019 - 1:00 pm
This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.
Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 16 avril 2019
Date de fin: : 6 juin 2019
This half days session offers an overview of machine learning tools available in Python.
Enseignant: Erik Spence
Catégorie: Data Science
Date: : mer., 13 juin 2018 - 1:30 pm
Principles and uses of relational databases with practical examples using python and sqlite.
Enseignant: Ramses van Zon
Catégorie: Data Science
Date: : mer., 6 juin 2018 - 12:00 pm
This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 2.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.
Enseignant: Erik Spence
Catégorie: Data Science
Date de début: : 17 avril 2018
Date de fin: : 29 mai 2018
Principles and uses of relational databases with practical examples using python and sqlite.
Enseignant: Ramses van Zon
Catégorie: Data Science
Date: : mer., 21 juin 2017 - 10:00 am
Principles and uses of relational databases with practical examples using python and sqlite.
Enseignant: Ramses van Zon
Catégorie: Data Science
Date: : mer., 4 mai 2016 - 9:30 am
This class will introduce students to using Apache Spark on the GPC. The Python interface PySpark will used to access the Spark infrastructure. Students are encouraged to bring a laptop to the class, so as to follow along with the exercises and quizzes. Students will be introduced to the PySpark syntax and commands, techniques for loading and managing data, and various data analysis strategies.
Enseignant: Erik Spence
Catégorie: Data Science
Date: : mar., 1 déc. 2015 - 9:00 am
Learn the basic of data research with R, in 4 weeks with 2 lectures per week (mini/modular course).
Enseignant: SciNet Team
Catégorie: Data Science
Date de début: : 6 oct. 2015
Date de fin: : 29 oct. 2015