Calendar
Session 1 of 2: Introduction to C
Site event
DESCRIPTION: This course introduces the fundamental concepts of programming such as conditional statement, Loops(while and for), Arrays, Pointers, Functions and Dynamic memory allocation. No programming experience will be assumed or required.
TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)
LEVEL: Introductory
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES: None
TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)
LEVEL: Introductory
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES: None
Session 1 of 2: Machine Learning
Site event
DESCRIPTION: This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:
* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.
Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
TEACHER: Weiguang Guan (SHARCNET, McMaster University)
LEVEL: Introductory to Intermediate
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES:
* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.
* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.
Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
TEACHER: Weiguang Guan (SHARCNET, McMaster University)
LEVEL: Introductory to Intermediate
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES:
* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.
Session 2 of 2: Introduction to C
Site event
DESCRIPTION: This course introduces the fundamental concepts of programming such as conditional statement, Loops(while and for), Arrays, Pointers, Functions and Dynamic memory allocation. No programming experience will be assumed or required.
TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)
LEVEL: Introductory
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES: None
TEACHER: Rakesh Srirajaraghavaraju (CAC, Queen's University)
LEVEL: Introductory
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES: None
Session 2 of 2: Machine Learning
Site event
DESCRIPTION: This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:
* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.
Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
TEACHER: Weiguang Guan (SHARCNET, McMaster University)
LEVEL: Introductory to Intermediate
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES:
* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.
* Part I covers the fundamentals of machine learning, and
* Part II demonstrates the applications of various machine methods in solving a real world problem.
Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
TEACHER: Weiguang Guan (SHARCNET, McMaster University)
LEVEL: Introductory to Intermediate
FORMAT: Lecture
CERTIFICATE: Attendance
PREREQUISITES:
* Data preparation or equivalent knowledge.
* Basic Python knowledge and experience.
* Knowledge and experience with Tensorflow and Scikit-learn would also be helpful.