Update your skillset in Machine Learning (ML) / Artificial Intelligence (AI) to state-of-the-art in only 3 working days and one weekend, being taught by a team of industry-leading practitioners and top academics.
Work with real datasets from finance, economics, trading, and life sciences at one of the oldest and most beautiful colleges in Oxford: Christ Church.
Network with other leading data scientists, fintech industry leaders, and Oxford academics at the celebrated medieval Dining Hall.
Solve scientific, technical, and business challenges that matter to you under the guidance of our team of highly experienced tutors while staying and working in the rooms where Einstein and Lewis Carrol used to work and acquiring unforgettable memories of Britain’s most beautiful city.
Following an introduction to data science and the underlying mathematics, you will go up to Oxford to study the theory and practice machine learning on real examples. You will review, learn, and master:
- deep reinforcement learning
- dynamic programming
- high-frequency & big data
- Bayesian methods
- mathematics of neural nets
- convnets, RNNs
- advanced neural network architectures
- generative deep learning
- graph neural networks (GNNs)
- decision trees, random forests
- time series analysis
- Kalman and particle filtering
- Markov chain Monte Carlo
- workflow of ML
- feature engineering
- hyperparameter tuning
- NLP and alternative data
- GPUs for deep learning
- quantum computing
- Saeed Amen
- Paul Bilokon, PhD
- Prof. Matthew Dixon, PhD
- Blanka Horvath, PhD
- Ivan Zhdankin
The Christ Church (Ædes Christi) college of the University of Oxford.
Preparatory day: Wednesday, 1 April, 2020
Main School: Thursday, 2 April, 2020 – Sunday, 5 April, 2020
Accommodation at the historic Christ Church college is included in the price. You will join a distinguished company of scholars who lived in these very rooms: Lewis Carroll, Albert Einstein, William Ewart Gladstone, Robert Hooke, John Locke, Sir Robert Peel, and many others.
|Time||Day 0: Preparatory||Day 1||Day 2||Day 3||Day 4A: Finance, Trading, Economics||Day 4B: Life Sciences|
|08:30 – 09:00||Registration and welcome||Registration and welcome||Registration and welcome||Registration and welcome||Registration and welcome||Registration and welcome|
|09:00 – 10:00||Fundamentals of Python||Overview of ML, scikit-learn||NN libraries||Bayesian methods||Overview of alternative data||Featurising molecules, RDKit|
|10:00 – 10:30||Advanced Python features||Decision trees, random forests, ensemble regressors, elastic nets||Advanced architecture patterns||Kalman and particle filtering||Web crawling / scraping (Scrapy, BeautifulSoup)||Graph NNs, DeepChem|
|10:30 – 11:00||Coffee break||Coffee break||Coffee break||Coffee break||Coffee break||Coffee break|
|11:00 – 12:00||Computational complexity and distributed computing||Overview of NNs, feedforward NNs||Reinforcement learning, OpenAI gym||MCMC||Natural language processing (NLTK, Stanford Core NLP, Genism, spaCy, pattern, TextBlob, BERT)||Generative models|
|12:00 – 12:30||Python libraries for working with data (NumPy, Pandas), visualisation (Matplotlib, Plotly, Seaborn)||Calibration of NNs, backpropagation||Finite Markov decision processes||Hyperparameter optimisation, crossvalidation||The practice of extracting value from alternative datasets||Deep learning for genomics|
|12:30 – 13:30||Lunch||Lunch||Lunch||Lunch||Lunch||Lunch|
|13:30 – 14:30||Exploratory data analysis||Maths of NNs||Deep reinforcement learning||PCA, randomised PCA, kernel PCA and related methods||An overview of volatility modelling||Deep learning for medicine|
|14:30 – 15:00||Preparing and downloading data, web scraping||Convnets||Generative deep learning||Unsupervised deep learning, self-supervised deep learning||Applying NNs to derivative pricing||Deep learning for microscopy|
|15:00 – 15:30||Coffee break||Coffee break||Coffee break||Coffee break||Coffee break||Coffee break|
|15:30 – 16:30||Introduction to kdb+/q||RNNs||GANs||Quantum computing||Lessons from applying NNs to derivative pricing||Overview of life sciences datasets|
|16:30 – 17:00||kdb+/q tables, selects, and joins||GPU computing for deep learning||Time series analysis, working with high-frequency data||Quantum ML||Frontiers of ML/AI in finance and economics||Frontiers of ML/AI in life sciences|
|17:00 – 18:00||Lab||Lab||Lab||Lab||Lab||Lab|
|18:00 – 19:30||Tour of Christ Church||Tour of Oxford City Centre||Visit to Oxford’s historic pub The Eagle & Child, home of The Inklings||Graduation and leaving drinks at the Buttery||Graduation and leaving drinks at the Buttery|
|19:30 – 21:00||Dinner at the Dining Hall||Dinner at the Dining Hall||Dinner at the Dining Hall||Dinner at the Dining Hall|
Please refer to the Datasets page to view the list of some of the datasets that we are going to be working with during the School.
Your course is designed to be self-contained. However, should you wish to read up on Artificial Intelligence / Machine Learning before starting the course, we recommend that you look up the relevant sections of our Publications page.