Lecture
Machine Learning Basics: Supervised and Unsupervised Learning
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Machine Learning Basics: Supervised Learning
Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.
Supervised Learning: k-NN and Decision Trees
Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.
Flexibility of Models & Bias-Variance Trade-Off
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Supervised Learning: Regression Methods
Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
Bias-Variance Trade-Off
Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.
Polynomial Regression: Overview
Covers polynomial regression, flexibility impact, and underfitting vs overfitting.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Decision Trees: Induction & Attributes
Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.
Error Decomposition and Regression Methods
Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.