Machine Intelligence - Lecture 6 (Validation, Overfitting, Underfitting)
Channel: Kimia Lab
Date: 07/03/2019

SYDE 522 – Machine Intelligence (Winter 2019, University of Waterloo)

Target Audience: Senior Undergraduate Engineering Students
  
Instructor: Professor H.R.Tizhoosh (http://kimia.uwaterloo.ca/)

Course Outline - The objective of this course is to introduce the students to the main concepts of machine intelligence as parts of a broader framework of “artificial intelligence”. An overview of different learning, inference and optimization schemes will be provided, including Principal Component Analysis, Support Vector Machines, Self-Organizing Maps, Decision Trees, Backpropagation Networks, Autoencoders, Convolutional Networks, Fuzzy Inferencing, Bayesian Inferencing, Evolutionary algorithms, and Ant Colonies. 

Lecture 6 - Validation (overfitting, underfitting, k-fold cross validation, leave-one-out)