ST311      Half Unit
Artificial Intelligence

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Pre-requisites:

Before taking this course, students must have completed: ST102 or (EC1C1 and ST109)

Additional requisites:

Equivalent combinations may be accepted at the lecturer's discretion. A computer programming course using Python, e.g. ST101 Programming for Data Science. Students who have no previous experience in Python are required to complete an online pre-sessional Python course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7696). Students should be comfortable with basic matrix algebra and calculus. 

The objective of this course is to introduce students to basic principles of artificial intelligence (AI) systems. By AI, we refer to machines (or computers) that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving. The course will take a practical approach, explaining the main principles and methods used in the design of AI systems.

The course will provide an introduction to main principles of deep learning, covering topics of neural nets as universal approximators, design of neural network architectures, backpropagation and optimisation methods for training neural networks, and some special deep neural network architectures commonly used for solving AI tasks such as image classification, sequence modelling, natural language processing and generative models. Students will gain practical knowledge to learn and evaluate deep learning algorithms using PyTorch.

15 hours of seminars and 20 hours of lectures in the Winter Term.

The lectures cover fundamental methodological and theoretical principles while computer workshops provide students with an opportunity to gain hands-on-experience by solving exercises using PyTorch. Students are required to use a Python programming environment, e.g. by installing Anaconda / Jupyter notebooks on their own laptops or using Google Colab, and to use their own laptops in the workshops.

1. A. Zhang, Z. Lipton, M. Li and A. Smola, Dive into Deep Learning, 2022, http://d2l.ai

2. I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2017, http://www.deeplearningbook.org

3. R. Sutton and A. C. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018

4. M. Nielsen, Neural Networks and Deep Learning, 2016, online book.

5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson, 2016

Quiz (40%)

Project (50%)

Data analysis (10%)

Randomised follow-up interviews take place on this course in the form of a 15mins discussion on the homework submitted.