IAML-PG2: Introductory Applied Machine Learning (Semester 2)

Welcome to Introductory Applied Machine Learning (Semester 2) 

We'll be introducing you a number of machine learning methods and concepts, helping to understand how they work, and how to apply them. The course content will be the same as when the course is run for Informatics students, but we will be focusing on example applications in finance.

We're looking forward to meeting you! 

Tiejun Ma, Fengxiang He and Waylon Li (TA)

Course Timetable

Click here to see the timetable.

Learning Outcomes

On successful completion of this course, you should be able to: 

1. Explain the scope, goals and limits of machine learning, and the main sub-areas of the field.
2. Describe the various techniques covered in the syllabus and where they fit within the structure of the discipline.
3. Critically compare, contrast and evaluate the different ML techniques in terms of their applicability to different Machine Learning problems.
4. Given a data set and problem, use appropriate software to apply these techniques to the data set to solve the problem.
5. Given appropriate data, use a systematic approach to conducting experimental investigations and assessing scientific hypotheses.

This course is delivered using "flipped-classroom" methods. Intellectual content will be delivered via a combination of online short video segments (overall, per topic, approximately the same length as a traditional lecture). Some of the topics have online quizzes associated with them, intended for you to review your understanding. During most of the lecture slots we will have other activities to review the topic material, also available by online recording.

We expect to cover the following general areas:

1. Review of maths and probability
2. Feature engineering (e.g., basis transforms, selection , Principal Components Analysis)
3. Classification vs. Regression
4. Supervised methods (e.g., Naive Bayes, Decision Trees and Random Forests, Linear & Logistic Regression,
5. Support Vector Machines, Nearest Neighbours, Neural Networks)
6. Unsupervised clustering methods (e.g., k-Means, Gaussian Mixture Models, Hierarchical Clustering)

We will use a modern machine learning programming environment and industry-standard libraries.