Machine Learning
Syllabus
Tentative Schedule
Lectures
Course content
4: Vectors
Course content
1: Introduction to Machine Learning
2: Supervised Learning
3: Data & Features
4: Vectors
5: K-Nearest Neighbors (KNN) Algorithm
6: Linear Regression - Least Squares Method
7: Linear Regression - Gradient Descent
8: Logistic Regression
9: Support Vector Machine
10: Introduction to Unsupervised Learning and Clustering
11: Hierarchical Clustering
12: K-Means Clustering
13: Probability Theory
14: Naive Bayes Learning Algorithm
15: Bayes Nets
16: Introduction to Decision Trees
17: Entropy and Information Gain
18: Overfitting and Pruning
19: Advantages and Limitations of Decision Trees
20: Introduction to Reinforcement Learning
21: Reinforcement Learning through Feedback Networks
22: Function Approximation in Reinforcement Learning
23: Introduction to Ensemble Methods
24: Random Forests
On this page
Slides
Readings
Edit this page
Report an issue
Vectors
Content for Monday, August 12, 2024
Slides
View slides in new window
Download slides
Readings
3: Data & Features
5: K-Nearest Neighbors (KNN) Algorithm