Machine Learning
Syllabus
Tentative Schedule
Lectures
Course content
13: Probability Theory
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
Probability Theory
Content for Tuesday, September 3, 2024
Slides
View slides in new window
Download slides
Readings
12: K-Means Clustering
14: Naive Bayes Learning Algorithm