Machine Learning and Data Mining I Course Descriptor

Course Overview

This course introduces supervised and unsupervised predictive modelling, data mining, and related machine learning concepts. It uses modern tools and libraries to analyse data sets, build predictive models, and evaluate their fit to the data. Common learning algorithms covered in this course include dimensionality reduction, classification, principal component analysis, nearest neighbours, clustering, gradient descent, regression, logistic regression, regularisation, multiclass data and algorithms, boosting, and decision trees. Students also study computational aspects of probability, statistics, and linear algebra that support the aforementioned algorithms (e.g., sampling theory). Students learn to apply machine learning concepts to common problem domains, such as recommendation systems, fraud detection, or advertising.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage you in your studies.

The scheduled teaching and learning activities for this course are:

Lectures/labs. 40 scheduled hours – typically including induction, consolidation or revision, and assessment activity hours:

• Version 1:All sessions in the same sized group, or
• Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

Further information about the assessments can be found in the Course Syllabus.

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

• Joel Grus. 2019. Data Science from Scratch, 2nd Edition, O’Reilly.
• Wes McKinney. 2017. Python for Data Analysis, 2nd Edition, O’Reilly.
• Hadrien Jean. 2020. Essential Math for Data Science, O’Reilly.

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.

• Dimensionality reduction
• Regression
• Classification
• Principal component analysis
• Nearest neighbours
• Clustering
• Gradient descent
• Regularisation
• Decision trees