Course syllabus

This page gives a basic overview of CS361 for Semester 1 2021. All course material is in Modules.

Course Overview	Digital Course Outline	Staff
Class Representatives	Meeting Times	Course Resources
Assessments	Course Expectations	Getting Started

 

Course Overview

This course will provide the foundations of machine learning, and provide practical skills to solve different problems. Students will explore research frontiers in machine learning while learning about the theoretical underpinnings of machine learning.

This course provides a broad introduction to machine learning. Topics include: (i) Supervised learning (decision trees, support vector machines, neural networks). (ii) Unsupervised learning (clustering, association rule mining, anomaly detection). (iii) fundamental practices in machine learning (bias/variance theory).

Digital Course Outline

A full overview of the course is provided in the Digital Course Outline

Staff

Lecturers

• Yun Sing Koh, Room 303-485, ykoh@cs.auckland.ac.nz (Course Coordinator)
• Pat Riddle, Room 303- 490, pat@cs.auckland.ac.nz
• Joerg Wicker, Room 303-526, j.wicker@auckland.ac.nz

 

Tutors

• Aaron Keesing
• Ben Halstead

 

Class Representatives

• Struan Caughey scau903@aucklanduni.ac.nz
• Yongqi (Hana) Liang ylia107@aucklanduni.ac.nz

Meeting times

Lectures

(During Level 1)

Mon 1:00PM - 2:00PM, Engineering  Block 1, Room 439
We 1:00PM - 2:00PM, Engineering  Block 1, Room 439
Fri 1:00PM - 2:00PM, Engineering  Block 1, Room 439

Check SSO for room times

 

Lecturer's Office Hours (at Level 1)

• Yun Sing Koh, Monday 2 - 3pm, 
  • https://auckland.zoom.us/j/92974635891?pwd=czF1ZGxGcktObE9QYTN6MG5KVU1JZz09
• Pat Riddle, (please email for an appointment)
• Joerg Wicker, Monday 3 - 4pm

 

Course Resources and Getting Help

Piazza: Piazza is the main forum we will be using for asking and answering questions. In a large class like this it works well so you are encouraged to participate asking and answering questions there.

Reading List.

Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.

Allen B. Downey (2014) Think Stats: Exploratory Data Analysis in Python.

Witten, I. H., Frank, E., Hall, M., & Pal, C. (2016). Data Mining (4th edition). Retrieved from https://ebookcentral.proquest.com/lib/auckland/detail.action?docID=4708912#

Bifet, A., Gavaldà, R., Holmes, G., & Pfahringer, B. (2018). Machine Learning for Data Streams with Practical Examples in MOA. Retrieved from https://mitpress.mit.edu/books/machine-learning-data-streams

Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining (1st ed). Boston: Pearson Addison Wesley.

Han, J., Kamber, M., & Pei, J. (2014). Data Mining (3rd Revised ed.). Morgan Kaufmann Publishers.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (1st ed. 2006. Corr. 2nd printing 2011). New York, NY: Springer-Verlag New York Inc.

 

Assessments and Pass Requirements

To pass the course, students must pass obtain 50% in their overall final mark. 

Course Expectations

Student Learning Expectations: https://docs.google.com/document/d/1vT5_czzIj4jZ96Uds7Ytq1yAav0zS83VPz2fSYLU7Sc/edit?usp=sharing

Getting Started

The course material is arranged in Modules.