Ημερολόγιο Εκδηλώσεων

Importing and Organizing Data

Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.

• Data types
• Tables
• Categorical data
• Data preparation

Finding Natural Patterns in Data (Clustering)

Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

• Unsupervised learning
• Clustering methods
• Cluster evaluation and interpretation

Building Classification Models

Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.

• Supervised learning
• Training and validation
• Classification methods

Building Regression Models

Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.

• Parametric regression methods
• Nonparametric regression methods
• Evaluation of regression models

Creating Neural Networks

Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.

• Clustering with Self-Organizing Maps
• Classification with feed-forward networks
• Regression with feed-forward networks

Transfer Learning for Image Classification

Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.

• Pretrained networks
• Image datastores
• Transfer learning
• Network evaluation

Building Convolutional Networks

Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work. Train networks to locate and label specific objects within images.

• Training from scratch
• Neural networks, convolution layers and filters
• Object detection