Dr Philip Chan

This course is compulsory on the BSc in Data Science. This course is available on the BSc in Actuarial Science, BSc in Finance, BSc in Mathematics with Data Science, BSc in Mathematics, Statistics and Business and BSc in Politics and Data Science. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.

Students must have completed either ST102, or ST109 and EC1C1. 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.

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, natrual language processing and generative models. Students will gain practical knowledge to learn and evaluate deep learning algorithms using PyTorch.

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.

This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours across Winter Term. 

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

Coursework (30%) in the WT.
Project (70%) in the ST.

Students are required to hand in the solutions to 2 sets of exercises (each accounting for 10% of the final grade), and complete an in-class quiz in Week 11.

There is one group project, which is due in the Spring Term.