LDSCI7227 Machine Learning Course Descriptor

Course Summary

This course is an opportunity for students to learn and experiment with the fundamental concepts and techniques used in modern machine learning applications through code and visualisations. This course offers students actionable knowledge of machine learning and deep learning, useful in their future careers (e.g. as data scientists or machine learning engineers).
In this course, students will learn how to set-up a machine learning project: (i) pre-processing a data set; (ii) choosing and implementing appropriate models for that data; (iii) improving its performance; and (iv) interpret and present the results. There is a particular focus on sustainable development.

Course Aims

The aims of this course are to:

• Systematically understand and reason about fundamentals concepts of machine learning.
• Consistently develop programs that solve machine learning problems using the modern techniques and software learned in this course.

Learning Outcomes

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

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional 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: Typically one lecture and one lab session per week:

• 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

Employability Skills

• Communication skills
• Mathematical skills
• Programming skills

Assessment

Formative

Students will be formatively assessed during the course by means of set assignments. These do not count towards the end of year results but will provide students with developmental feedback. Set assignments will also amplify problem-solving skills and develop software components that form part of the coding assignments.

Summative

The assessment will consist of two written coding assignments, which the student will have to do according to the set guidelines for coding.

The coding assignments will be assessed in accordance with the assessment aims set out in the Programme Specification.

Feedback

Students will receive formal feedback in a variety of ways: written (including via email correspondence); oral (on an ad hoc basis) and indirectly through discussion during group tutorials. Students will also attend the formal meeting, Collections, in which they will receive constructive and developmental feedback on their  performance. 

Feedback is provided on summative written assignments which will be handed back to the students.

Indicative Reading

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

Books 

Andreas C. Muller and Sarah Guido. 2016. Introduction to Machine Learning with Python. O’Reilly Media, Inc.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press

Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola. 2020. Dive into Deep Learning. Online at https://d2l.ai

Stuart J. Russell and Peter Norvig. 2020. Artificial Intelligence: A Modern Approach (4th. ed.). Pearson Education

Indicative Topics

Students will study the following topics: 

• Machine learning frameworks
• Cleaning and preparing data
• Supervised learning: Support vector machines and Decision Tree classifiers
• Neural networks
• Stochastic Gradient Descent and backpropagation
• Convolutional Neural Network
• Object detection and image processing
• Sequence-to-sequence models and attention mechanism
• Efficient learning and deep reinforcement learning

Version History