CS3AM: Artificial Intelligence and Machine Learning

Module code: CS3AM

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 3 (Honours)

When you'll be taught: Semester 1

Module convenor: Dr Muhammad Shahzad, email: m.shahzad2@reading.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE CS1MA20 AND TAKE CS2AO17 AND TAKE CS1PC20 AND TAKE CS2PJ20 (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 21 May 2024

Overview

Module aims and purpose

The main goal of this module is to familiarise students with both the foundational and advanced concepts in Artificial Intelligence (AI) and Machine Learning (ML). Specifically, the module shall cover adversarial search, game theory, and learning methodologies including both shallow/conventional (e.g., Naïve Bayes, Decision Trees, Multilayer Perceptrons etc.), ensemble (Bagging and Boosting) and deep learning (Convolutional Neural Networks and Recurrent Neural Networks for both National Language Processing and Vision) methods. The application of these methods shall be demonstrated over variety of real-world problems including classification, regression, predictive modelling, information extraction, and signal (vision/speech) processing. 

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Explain the foundational theory and advanced concepts underpinning Artificial Intelligence (AI)
2. Discuss and differentiate wide variety of AI algorithms and techniques
3. Apply a variety of learning algorithms to a given data
4. Evaluate various learning algorithms for optimal model selection
5. Employ modern tools and frameworks to address a real-world problem in a small-scale AI project and demonstrate the practical skills in the field
6. Demonstrate their abilities in critical thinking to solve a large problem integrating components of data engineering, algorithm development and implementation
7. Demonstrate their abilities in professional and effective writing for algorithm development and software implementation

Module content

The module covers the following topics:

• Introduction to AI and ML concepts (Bias Variance Trade Off, Overfitting, Regularization)
• Supervised Learning (Regression/Classification)
• Linear/Logistic Regression
• Shallow machine learning methods (Naïve Bayes, Decision Trees, SVM etc.)
• Ensemble methods (Bagging and Boosting)
• Unsupervised Learning (K-means and meanshift learning)
• Deep Neural Networks Architectures, Training and Hyper Parameter Tuning
• Artificial Agents, Adversarial Search, and Game Theory
• Reinforcement Learning

Structure

Teaching and learning methods

The module consists of 2-hour lectures and 2-hour practical sessions per week. The lectures will introduce students the theories, concepts and underpinning principles specified in the indicative content while the supervised practical sessions will guide them to develop thorough understanding in implementing AI algorithms for variety of different tasks. The formal lecture and practical sessions will enable students to apply the fundamental AI & ML techniques to solve a given problem, by demonstrating using programming, analysis and report writing. Moreover, these sessions will be supplemented with several forms of digital resources to support learning. The summative assessment consists of one piece of individually written coursework assignment which requires every student to demonstrate his/her achievement in developing a small-scale AI/ML solution. 

Study hours

At least 44 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Each topic in a week has defined learning tasks which will enable students to self-reflect on the learning.   

Outcomes of the formative assessment for each topic may be given in the guidance tutorial notes, online tests feedback. 

Basic algorithms will be presented in pseudo codes and/or in executable codes (e.g., Python) towards weekly studies.  

Reassessment

Additional costs