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.