Industrial Data Analysis and Visualization 2024

Course Syllabus 

Lecture: Friday. 9:00-12:00
Room: 25409
Class code: urpxxqz
Instructor: Vacharapat Mettanant
Book: Kevin Murphy: Probabilistic Machine Learning: An Introduction [Link ]
Prerequisites: 

• 03609351 Database Design and Data Mining. 

• Basic knowledge in probability theory and programming using Python.
  
  

Course Learning Outcomes: Students who pass this course will have ability to

• Explain the key principles of probability theory and statistics as they relate to machine learning, including concepts like probability distributions, Bayes' theorem, and expectation.

• Choose, implement, and evaluate machine learning models.

• Recognize and address ethical considerations related to the use of machine learning, including issues of privacy and adversarial security.

• Create and interpret various plots and charts (e.g., histograms, scatter plots, box plots) to visually explore data using a data visualization tool (matplotlib).

• Conduct a machine learning project on given data using computational tools and a programming language (Python).

Learning activities: This course is lecture based with some in-class activities. 

Scores: 

• Online quizzes 10%

• In-class assignments 20%

• Group assignments and presentation 20%

• Evaluation from other students 10%

• Midterm 20%

• Final 20%

Topics (draft):

References:

Cornell CS4780 Machine Learning for Intelligent Systems [YouTube] [Homepage]