LEJ Knowledge Hub

Lecture

Right Arrow

SEARCH COURSES / LECTURES

Left Arrow

Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T)

(15 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • 1. Introduction, Optimization Problems (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
2
  • 10. Understanding Experimental Data (cont.) (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
3
  • 11. Introduction to Machine Learning (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
4
  • 12. Clustering (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
5
  • 13. Classification (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
6
  • 14. Classification and Statistical Sins (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
7
  • 15. Statistical Sins and Wrap Up (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
8
  • 2. Optimization Problems (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
9
  • 3. Graph-theoretic Models (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
10
  • 4. Stochastic Thinking (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
11
  • 5. Random Walks (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
12
  • 6. Monte Carlo Simulation (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
13
  • 7. Confidence Intervals (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
14
  • 8. Sampling and Standard Error (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences
15
  • 9. Understanding Experimental Data (M-I-T)
Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) MIT John Guttag Applied Sciences

Basic and Health Sciences

Applied Sciences

Social Sciences