GeezerVison

• Lecturer: Zhanxing Zhu

• Teaching Assistants:

  • Hantao Guo, [email protected]
  • Yuanjin Zhu, [email protected]
  • Junzhao Zhang, [email protected]

• Time: Mon: 1:00-2:50 pm; Thu(odd week): 3:10-5:00 pm.

• Location: Room 107, Teaching Building 2

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

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
  • Traffic Prediction
  • Flower Classification
  • PES Challenge

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

• Mon 9/9: Introduction to Data Science
  • Lecture 1
• Thu 9/12: Review of Preliminary Knowledge
  • Lecture 2

• Mon 9/16: Data Preprocess
  • Lecture 3

• Mon 9/23: Regression
  • Lecture 4

• Mon 10/7: Classification1
  • Lecture 5
• Thu 10/10: Classification2
  • Lecture 6

• Mon 10/14: Classification3
  • Lecture 7
• Thu 10/17: Ensemble
  • Lecture 8

• Mon 10/21: Clustering
  • Lecture 9
• Thu 10/24: Dimension Reduction1
  • Lecture 10

• Mon 10/28: Dimension Reduction2
  • Lecture 11

• Mon 11/4: Graphical Models1
  • Lecture 12
• Thu 11/7: Graphical Models2
  • Lecture 13

• Mon 11/11: Graphical Models3
  • Lecture14

• Mon 11/18: Graphical Models4
• Thu 11/21: Graphical Models5
  • Lecture15

• Mon 11/25: Topic Model
  • Lecture16

• Mon 12/2: Introduction to Deep Learning
  • Lecture17
• Thu 12/7

• Mon 12/9: Mid-term Exam

• Mon 12/16
• Thu 12/19

• Mon 12/23