Course syllabus

Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Students should understand the foundations of machine learning, and gain practical skills to solve different problems.


Course Outcomes

A student will be able to:

  • Demonstrate technical knowledge of the underlying principals and concepts of machine learning science.
  • Apply efficient machine learning algorithms on a problem.
  • Design evaluation procedures to evaluate a model.
  • Interpret the results of machine learning run on real data.
  • Assess the benefits/drawbacks of competing models and algorithms, relevant to real problems.
  • Demonstrate your knowledge about cutting edge research streams and developments in machine learning.
  • Recognise real-world problems suitable to machine learning.



The course information page can be found HERE .


  • Yun Sing Koh, Room 303-485, (Course Coordinator)
  • Pat Riddle, Room 303- 490,
  • Joerg Wicker, Room 303-526,



  • Jordan Douglas, room 303-461,




Mo 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)
Tu 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)
We 10:00AM - 11:00AM, 301-G050 (Science Chem, Room G050)



We 1:00PM - 2:00PM 301-G050 (Science Chem, Room G050)


Class representatives


Assignment deadlines

Fri, 15 Mar 2019 A1 due by 23:59
Fri, 29 Mar 2019 A2 due by 23:59
Fri, 12 Apr 2019 A3 due by 23:59
Fri, 3 May 2019 A4 due by 23:59
Tues, 21 May 2019 A5 due by 23:59
Fri, 31 May 2019 A6 due by 23:59

Course summary:

Date Details