Course syllabus

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Machine learning techniques are widely used in many computing applications; for example, in web search engines, spam filtering, speech and image recognition, computer games, machine vision, credit card fraud detection, stock market analysis and product marketing applications. Machine learning implies that there is some improvement that results from the learning program having seen some data. The improvement can be in terms of some performance program (e.g., learning an expert system or improving the performance of a planning or scheduling program), in terms of finding an unknown relation in the data (e.g., data mining, pattern analysis), or in terms of customizing adaptive systems (e.g., adaptive user-interfaces or adaptive agents).

In Pat Riddle’s part of the paper, we will study several techniques for learning such as Ensemble Learning and Neural Networks. In addition, we will provide an overview of the experimental methods necessary for understanding machine learning research.

In Joerg Wicker's part of the paper, we will cover further recent research topics in machine learning and data mining. Specifically, we will address Multi-Label and Multi-Target Learning, Adversarial Learning, and Privacy.

Kaiqi Zhao's part of the paper will cover several commonly used techniques in both data mining and machine learning. We will start from pattern mining and clustering. Then, we will cover the powerful probabilistic graphical models and recommendation algorithms.

Learning Outcomes

The students will be able to:

  • Discuss the idea that all machine learning algorithms have a basis and will be able to describe the basis of several algorithms
  • Discuss the theory that for a particular dataset one algorithm will perform well and for another dataset a different algorithm will perform well. There is no one algorithm that performs well on all datasets.
  • Describe a machine learning algorithm as a search algorithm through a space of hypotheses.
  • Design a good set of experiments for determining the answer do some basic research question, such that they can show that the experiments actually support the question they are asking.

Assessment

Your final grade will consist of a number of internal marks worth 40% combined and an exam worth 60%. This is set up as a research based course. So the internal marks will be based on a research project, done in 3 member teams. There is a practical and a theoretical pass on this paper. This means you need to have more marks than the passing threshold in both the exam marks and the marks for the report assignments. So make sure you spend enough time on the internal assessments and prepare for the exam.

Teaching Staff

Pat Riddle
Room: 490, Computer Science Building (Building 303S)
Phone: 373-7599, Ext 87093 Email: pat@cs.auckland.ac.nz 

Kaiqi Zhao
Room: 492, Computer Science Building (Building 303S)
Phone: 373-6958, Email: kaiqi.zhao@auckland.ac.nz 

Joerg Wicker (coordinator)
Room: 526, Computer Science Building (Building 303)
Phone: 373-7599, Ext 82184 Email: j.wicker@auckland.ac.nz 

Katerina Taskova

Room: 493, Computer Science Building (Building 303S) 

Email: katerina.taskova@auckland.ac.nz

Lecture Times

  • Mon 1pm, Room ClocktT029/ 105-029
  • Tue 1pm, Room 206-209
  • Fri 1pm, Room 206-220

Seeking Assistence

The primary source of assistance is the teaching staff. Please contact us  with any questions or concerns about the course. We all are available via email. For help with more generic study skills or literacy, the Student Learning Centre and Library both offer many courses designed to help students become more efficient at study.

Exam

The final exam is worth 60% of your final mark. Please check Student Services Online for the exam time and date. The exam is closed book, calculators are not permitted. Provisional exam results can be obtained from Student Services Online.

You will be get your final grade via SSO.  Please also do understand that we are not allowed to be in communication with you in regards to your exam after the exam is written. If you email any of us during this period regarding the exam we won't be able to respond to your email. 
If you would like to know more about exams process please see
If you feel you need to talk to a person about the exam results, we suggest the science student center or the student adviser relevant to your degree.

Missed Exam

If you miss the exam for any valid reason, or you sit the exam but believe that your performance was impaired for some reason, then you may be able to apply for an aegrotat, compassionate or special pass consideration. For more detailed information, contact the science student center.

Suggested text

T. Mitchell, Machine Learning, McGraw Hill, 1997.

Policy on Cheating and Plagiarism

Cheating is viewed as a serious offense by the University of Auckland. Penalties are administered by the Discipline Committee of the Senate, and may include suspension or expulsion from the university. Do not copy anyone else’s work, or allow anyone else to copy from you. For more information on the University’s policy on cheating, please refer to the web page: http: //www.auckland.ac.nz/uoa/home/about/teaching-learning/honesty

Course summary:

Date Details Due