All 6 results
Sort by
-
Evaluation Metrics; Probability Functions; Tensors
- Class notes • 30 pages • 2020
- Available in package deal
-
- $10.48
- + learn more
This document contains class notes and lucid description of the following topics:

1. Evaluation Metrics - Accuracy, Precision, Recall, F1 Score, PRC curve
2. Probability Density Function
3. Probability Mas Function
4. Cumulative Distribution Function
5. Dealing with tensors
-
Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification
- Class notes • 30 pages • 2020
- Available in package deal
-
- $10.48
- + learn more
This document contains class notes and lucid description of the following topics:

1. Introductory concepts of Artificial Intelligence
2. Why Machine Learning?
3. Timeline of Artificial Intelligence
4. Soft v/s Hard Classification
5. Various Machine Learning domains
6. Human brain v/s Computer
-
Feature Extraction; Dealing with data; Regression
- Class notes • 30 pages • 2020
- Available in package deal
-
- $10.48
- + learn more
This document contains class notes and lucid description of the following topics:

1. Feature extraction
2. Dealing with data
3. Least square solution
4. Minimum norm solution
5. Exploring the IRIS dataset using Python
6. Regression
-
Multi-class Classification; Gradient Descent; Data Normalization
- Class notes • 30 pages • 2020
- Available in package deal
-
- $10.48
- + learn more
This document contains class notes and lucid description of the following topics:

1. Classification problems
2. Gradient Descent Algorithm
3. Data Normalization
4. Multi-class classification (including non-linearity and loss function)
-
Pseudo Random Numbers
- Study guide • 4 pages • 2020
-
- $50.48
- + learn more
This series of handwritten notes contains everything in a gist that a Computer Science or Statistics graduate student needs to study for his/her Machine Learning course.

Topics covered:
1. History of Artificial Intelligence
2. The Turing Test
3. Weak AI v/s Strong AI
4. Human brain v/s Computer
5. Various Machine Learning domains
6. Feature Extraction
7. Soft Classification and Hard Classification
8. Linear Classifier
9. Evaluation Metrics
10. Probability Density Function
11. Probability Mass F...
-
Support Vector Machines
- Summary • 8 pages • 2019
-
- $7.49
- + learn more
Basic Summary of how Support Vector Machines Work, with historical background and the algorithms idea from the basic to Kernel functions.
How did he do that? By selling his study resources on Stuvia. Try it yourself! Discover all about earning on Stuvia