Главная
Study mode:
on
1
1. Artificial Intelligence and Machine Learning
2
2. Cyber Network Data Processing; AI Data Architecture
3
Lecture: Mathematics of Big Data and Machine Learning
4
0. Introduction
5
0. Examples Demonstration
6
1. Using Associative Arrays
7
1. Examples Demonstration
8
2. Group Theory
9
2. Examples Demonstration
10
3. Entity Analysis in Unstructured Data
11
3. Examples Demonstration
12
4. Analysis of Structured Data
13
4. Examples Demonstration
14
5. Perfect Power Law Graphs -- Generation, Sampling, Construction, and Fitting
15
5. Examples Demonstration
16
6. Bio Sequence Cross Correlation
17
6. Examples Demonstration
18
Demonstration 7
19
7. Kronecker Graphs, Data Generation, and Performance
20
7. Examples Demonstration
Description:
Explore the mathematics behind big data and machine learning in this 14-hour course from MIT. Delve into artificial intelligence with a focus on data handling challenges, guided by instructors Jeremy Kepner and Vijay Gadepally. Learn about cyber network data processing, AI data architecture, and the use of associative arrays. Discover group theory applications, entity analysis in unstructured data, and structured data analysis techniques. Investigate perfect power law graphs, bio sequence cross-correlation, and Kronecker graphs. Gain hands-on experience through numerous demonstrations accompanying each lecture topic. Enhance your understanding of the mathematical foundations crucial for tackling complex data and AI challenges in this comprehensive IAP class.

Mathematics of Big Data and Machine Learning, IAP 2020

Massachusetts Institute of Technology
Add to list
0:00 / 0:00