วีดีโอ

Subset selection, Forward stepwise selection, Backward stepwise selection

Within class variance, Between class variance, Fisher's criterion

Evaluation measures, gini index, data sampling decision trees

Supervised learning, classification and regression, examples, linear mapping, quadratic mapping, performance, over-fitting, inductive biases - language bias, search bias, training set, test set, validation set, applications of supervised learning, sum-of-square error minimization.

Week 6 Lecture 35 Stopping Criteria & Pruning

Week 6 Lecture 33 Decision Trees - Introduction

MAXIMUM LIKELIHOOD ESTIMATE

Week 5 Lecture 31 Parameter Estimation II - Priors & MAP

Week 2 Lecture 5 - Statistical Decision Theory - Regression

Week 5 Lecture 26 ANN I - Early Models

Week 2 Lecture 9 - Multivariate Regression

Week 4 Lecture 21 SVM - Formulation

Week 6 Lecture 38 Decision Trees - Multiway Splits

Week 3 Lecture 16 Logistic Regression

Week 3 Lecture 11 Subset Selection 2

Week 3 Lecture 15 Linear Classification

Week 5 Lecture 29 ANN IV - Initialization, Training & Validation

Week 4 Lecture 20 Perceptron Learning

Week 2 Lecture 8 - Linear Regression

Week 2 Lecture 7 - Bias - Variance

Week 3 Lecture 17 Linear Discriminant Analysis 1

Week 2 Lecture 6 - Statistical Decision Theory - Classification

Week 3 Lecture 12 Shrinkage Methods

Week 6 Lecture 36 Decision Trees for Classification - Loss Functions

Week 1 Lecture 3 - Unsupervised Learning

Week 3 Lecture 13 Principal Components Regression

Week 6 Lecture 34 Regression Trees

Week 4 Lecture 22 SVM - Interpretation & Analysis

Reinforcement learning, trial-and-error, environmental feedback, stochastic environment, policy to map states to actions, applications of reinforcement learning