Prof.  Zoltán Szabó (http://www.cmap.polytechnique.fr/~zoltan.szabo/)

Practical sessions: Dr. Marcos Barreto (https://www.lse.ac.uk/Statistics/People/Dr-Marcos-Barreto), plus a Graduate Teaching Assistant.

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Management of Information Systems and Digital Innovation, 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.

This course has a limited number of places (it is controlled access) and demand is typically high. This may mean that you are not able to get a place on this course. The MSc in Data Science students are given priority for enrollment in this course. 

The course will provide a broad overview on fundamental concepts and algorithms of artificial intelligence systems, with focus on search methods, knowledge representation, safety and fairness, game playing, (probabilistic) reasoning, planning, neural networks and reinforcement learning. We will use Python and open source libraries on the Google Colab platform to translate the studied principles and methods into practice, and to gain hands-on experience in data analysis.

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

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

• Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 4th edition, Pearson, 2020. [http://aima.cs.berkeley.edu/]

• Kevin Murphy. Probabilistic Machine Learning. 2021-2022. [https://probml.github.io/pml-book/]

• Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2007. [https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/]

• Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into Deep Learning, 2021. [http://d2l.ai/]

• Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning, MIT Press, 2016. [https://www.deeplearningbook.org/]

• Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd edition, MIT Press, 2018. [http://incompleteideas.net/book/the-book.html]

• TensorFlow, An Open Source Software Library for Machine Intelligence. [http://www.tensorflow.org]

• Jake VanderPlas. Python Data Science Handbook. O'Reilly Media, Inc., 2017. [https://www.oreilly.com/library/view/python-data-science/9781491912126/ , https://jakevdp.github.io/PythonDataScienceHandbook/]

• Mark Lutz. Learning Python, 5th Edition. O'Reilly Media, Inc., 2013. [https://www.oreilly.com/library/view/learning-python-5th/9781449355722/]

Project (80%) in the MT.
Continuous assessment (10%) in the MT Week 4.
Continuous assessment (10%) in the MT 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 be completed by students in groups, 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.  

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.