MATH0001 Once Upon a Data – From Data to Artificial Intelligence and Human Decision

   

 

 

 

 

 

 

 

 

 
Course Code MATH0001  
Course Title Once Upon a Data – From Data to Artificial Intelligence and Human Decision  
Class Date 13, 15, 16, 17, 18 and 19 August 2022  
Class Time 9:00am – 11:30am
Teaching Mode Online with live lesson
Teacher Dr. Jeff WONG Chak-fu
Senior Lecturer
Department of Mathematics
The Chinese University of Hong Kong		  

 
  More about Department of Mathematics, CUHK:

  1. B.Sc. in Mathematics
  2. ZOOM Into Science – 追求幸福 – 幸福的秘訣可以用數學模型來表達嗎？
Medium of Instruction English
Pre-requisite Information Basic counting as well as basic experience in Excel will be required. Some basic knowledge in probability and statistics would be helpful but not is required. The course itself will cover many basics mathematical topics, including probability, statistics and matrix computations. This course targets form five students (with regard to DSE) or equivalent.
Course Description Experiments, observations, and numerical simulations in many areas of science nowadays produce enormous amounts of data. This rapid growth opens an era of data-centric science, which needs new paradigms addressing how data are acquired, processed, distributed, and analyzed. From a layman’s perspective, this course will cover simple but deep mathematical concepts and easy-to-learn algorithms that can solve some of the challenges posed by Artificial Intelligence and Big Data, and turn data into useful information and real-life connections.
Course Content
  1. Mathematics Beyond the Numbers
  2. Probability – Moving from Chaos to Stability
  3. Number Patterns – Using Vectors to Explore Pattern Relationships
  4. Interpolation and Extrapolation – Difference between Observation and Reality
  5. Networks and Matrices – Connecting to the Real World
  6. Text Analytics – New Era of Networked World
  7. Visualization Tools – Application of Computing Software
Learning Outcomes By the end of the course, the students are expected to understand the so-called “mathematics and data tradeoffs” in terms of data analytics from data to human decision related-stores/ real-life connections. In particular, the students must be able to

  1. Collect the data and build a data analytics problem by hand
  2. Formulate a mathematical model in the form of the data analytics problem
  3. Obtain approximate solutions for the data analytics problem by means of any mathematical tools, computer algorithms and visualization computing software
  4. Decipher whether or not its results can be explained in the real world
  5. Study the dual pair of data and reality
Recommended Reading(s) / Reference(s)
  1. Simon Haykin, Neural Networks and Learning Machines, Pearson, Third edition, 2009.
  2. Nassim Nicholas Taleb, The Black Swan – The Impact of the Highly Improbable, Random, House, Second edition, 2010.
  3. William Kent, Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World, Technics Publications, LLC, Third edition, 2016.
  4. Douglas A Luke, A User’s Guide to Network Analysis in R. Springer, 2015.
  5. Mohammed Zuhair Al-Taie, Seifedine Kadry, Python, for Graph and Network Analysis, Springer, 2017.
  6. John Scott, Social Network Analysis: A Handbook, SAGA, 2017.
  7. Nick Polson, AIQ: How People and Machines Are Smarter Together, St. Martin’s Press, 2018.
  8. Matthew O. Jackson, The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors, Pantheon, 2019.
  9. Matthew Cobb, The Idea of the Brain: The Past and Future of Neurosciences, Basic Books, 2020.
Course Assessment Class Participation and Classroom Activities (40%)

Coursework (40%)

Final (20%)
 
  Last updated on 2 August 2022
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