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.
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, firstname.lastname@example.org (Course Coordinator)
- Pat Riddle, Room 303- 490, email@example.com
- Joerg Wicker, Room 303-526, firstname.lastname@example.org
- Jordan Douglas, room 303-461, email@example.com
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)
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
The syllabus page shows a table-oriented view of course schedule and basics of course grading. You can add any other comments, notes or thoughts you have about the course structure, course policies or anything else.
To add some comments, click the 'Edit' link at the top.