ST449      Half Unit
Artificial Intelligence and Deep Learning

This information is for the 2020/21 session.

Teacher responsible

Prof Milan Vojnovic COL 5.05


This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Quantitative Methods for Risk Management, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

The MSc in Data Science students are given priority for enrollment in this course. 

Course content

The course will introduce the basic principles and algorithms used in artificial intelligence systems, with a focus on statistical and machine learning foundations, including the design and training of deep neural networks and reinforcement learning algorithms. These algorithms will be applied to classification tasks such as image recognition, speech recognition, natural language processing, as well as to agents learning to play various games. Use of Python and open source software libraries for machine intelligence such as Tensorflow and OpenAI Gym constitutes an integral part of the course, allowing students to gain hands-on experience in data analysis and use of modern computational tools.


This course will be delivered through a combination of classes and lectures totalling a minimum of 35 hours across Lent Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Lent Term.


Formative coursework

Students will be expected to produce 10 problem sets in the LT.

Indicative reading

  • I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2017, 
  • R. Sutton and A. C. Barto, Reinforcement Learning: An Introduction, Second Edition, MIT Press, 2018
  • TensorFlow, An Open Source Software Library for Machine Intelligence,
  • OpenAI Gym,


Project (80%) in the LT.
Continuous assessment (10%) in the LT Week 4.
Continuous assessment (10%) in the LT Week 7.

Two problems sets submitted by students will be assessed (20% in total). In addition, there will be a graded take-home research project (80%) which will completed on an individual basis in which they will demonstrate the ability to apply and train an appropriate model to a specific problem and dataset using principles they have learnt in the course. This may be done by publishing the code to a GitHub repository and GitHub pages website.  

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2019/20: 31

Average class size 2019/20: 31

Controlled access 2019/20: Yes

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills