| Module Code | CSP7000 |
| Module Name | Introduction to Machine Learning |
| ECTS Weighting | 5 ECTS |
| Semester taught | Semester 1 |
| Module Coordinator/s | Giovanni Di Liberto |
| Module Learning Outcomes with reference to the Graduate Attributes and how they are developed in discipline | On successful completion of this module, students should be able to: Programme learning outcomes: PLO2: Analyse big data sets using technical tools to enable the better planning of cities. PLO3: Develop integrated plans to deliver smart and sustainable city interventions. PLO6: Effectively design, develop and deliver independent research focused on key elements of smart and sustainable urbanization. Module learning outcomes: MLO1 Configure a programming environment suitable for exploring ML techniques MLO2 Prepare datasets for ML processing, visualise the data, and understand the consequences of decisions made in cleaning data MLO3 Assess the performance of a ML pipeline MLO4 Critically evaluate the outputs of a ML pipeline MLO5 Communicate with ML experts and non-experts: Explain goals and requirements of a project, interpret the outcomes of typical ML analyses, present results to non-experts. MLO6 Assess the cost/benefit of distinct ML methodologies and explain what makes one approach more suitable than another one for a given task MLO7. Understand challenges involving data sharing, storage, and privacy Graduate Attributes: levels of attainment To act responsibly – Introduced To think independently – Enhanced To develop continuously – Enhanced To communicate effectively – Attained |
| Module Content | Introduction to Machine Learning is designed to offer an introduction to the basics of ML, specifically with a hands-on curriculum aimed at developing knowledge and skills in establishing ML pipelines with state of the art languages and toolkits. This module is designed for students with limited prior experience of programming. It will introduce the fundamentals of programming, with a focus on setting up an effective pipeline for processing datasets to execute common ML techniques such as Support Vector Machines and Linear Regression. Students will be assessed both on the acquired technical skills and on their ability to understand the ML pipeline and results and communicate effectively with experts and non-experts. |
| Teaching and Learning Methods | Lectures, tutorials, group project, guest lecture/seminar, classroom discussion |
| Assessment Component | Assessment Description | LO Addressed | % of total | Week due |
| Engagement and Communication | Class engagement/discussion | All LOs | 10 | |
| Technical (coding & ML) skills | Individual laboratory assignments | LO1-4 | 30 | Weeks 3, 6, 9 |
| Communication, presentation, group work | Group assignment (written report + oral presentation) | LO2-7 | 20 | Final week |
| Written Examination | 2h written test (both open and multiple choice questions) | LO1-4,6,7 | 40 | Final exam |
| Reassessment Requirements | 100% written examination |
| Contact Hours and Indicative Student Workload3 | Contact hours: 28h in total: 15h lectures + 8h tutorials + 3h lab assignments + 2 project presentation Independent Study (preparation for course and review of materials): 40h Independent Study (preparation for assessment, incl. completion of assessment): 49h |
| Recommended Reading List | Python Crash Course: A Hands-On, Project-Based Introduction to Programming, Eric Matthes (eBook available in the TCD library) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2nd Edition, O’Reilly Media (part 1) |
| Module Pre-requisite | None |
| Module Co-requisite | |
| Module Website | Blackboard. Website (to be defined). |
| Are other Schools/Departments involved in the delivery of this module? If yes, please provide details. | |
| Module Approval Date | |
| Approved by | |
| Academic Start Year | September 2021 |
| Academic Year of Date | 2021-22 |