/IntroductionToDataScience2019Fall

Introduction to Data Science

Announcements

Administrative information

Course Content

Description:

This course is a preliminary course for learning from data, artificial intelligence and other related application areas.

Prerequisite:

  • Calculus, linear algebra, basic statistics, numerical optimization, signal processing
  • Programming and algorithms, e.g. Python

Grading

Mid-term exam (40%): writing exam (Dec 9)

Final projects (60%):

  • Kaggle-in-Class competition, select 1 out of 3
  • including submission to the Kaggle platform and report writing
  • Deadline: Jan 19, 2020 (strict)
  • At most 2 students as a team
  • the links of competitions are as follows

References

  • Elements of statistical learning
  • Pattern recognition and machine learning
  • Vapnik. The nature of statistical learning theory
  • 《数据科学导引》

Schedule (subject to change)

Week 1

  • Mon 9/9: Introduction to Data Science
  • Thu 9/12: Review of Preliminary Knowledge

Week 2

Week 3

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

  • Mon 11/18: Graphical Models4
  • Thu 11/21: Graphical Models5

Week 12

Week 13

  • Mon 12/2: Introduction to Deep Learning
  • Thu 12/7

Week 14

  • Mon 12/9: Mid-term Exam

Week 15

  • Mon 12/16
  • Thu 12/19

Week 16

  • Mon 12/23