UTK Notes

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• Lectures
  • Introduction
  • How do machines learn?
  • Decision Trees
  • Feature Selection and Model Assessment
  • Unsupervised Learning
  • Advanced Machine Learning Techniques

Lectures

Introduction

• Lecture 01: Course Overview
• Lecture 02: Machine Learning Motivation
• Lecture 03: Scientific Computing with Python
• Lecture 04: SkiKit Learn and Nearest Neighbor Classifier

How do machines learn?

• Lecture 05: Learning Theory and Gradient Descent
• Lecture 06: Gradient Descent and Linear Regression
• Lecture 07: Linear Regression
• Lecture 08: Logistic Regression
• Lecture 09: Data Splits and Overfitting
• Lecture 10: Bias, Variance, and Regularization

Decision Trees

• Lecture 11: Regularization and Decision Trees
• Lecture 12: Decision Trees
• Lecture E1: Exam 1 Review
• Lecture 13: Ensemble Methods

Feature Selection and Model Assessment

• Lecture 14: Data Wrangling and Hyperparameter Tuning
• Lecture 15: Hyperparameter Tuning and Cross Validation
• Lecture 16: Feature Selection and Extraction
• Lecture 17: Feature Extraction
• Lecture 18: Explainability

Unsupervised Learning

• Lecture 19: Unsupervised Learning

Advanced Machine Learning Techniques

• Lecture 20: Introduction to Artificial Neural Networks
• Lecture 21: Artificial Neural Networks and Deep Learning
• Lecture E2: Exam 2 Review
• Lecture 22 - BackProp Activation Functions and CNNs.pdf