Due to the ongoing COVID-19 pandemic, we are rescheduling the School, tentatively to March 2021. Unfortunately, the situation remains unpredictable, and we are unable to provide any further detail at this stage. We sincerely apologize for the inconvenience.
Intensive School in ML/AI
Dates: to be confirmed, 3 working days and a weekend in 2021 (tentatively)
Venue: Christ Church, Oxford
You are warmly invited to join our unique School in AI and ML. You will go up to Oxford to study the theory and practise machine learning (ML) / artificial intelligence (AI) on real financial examples.
Upgrade your skill set in ML/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 Einsteain and Lewis Carroll used to work and acquiring unforgettable memories of Britain’s most beautiful city.
You will review, learn, and master:
- deep reinforcement learning;
- dynamic programming;
- high-frequency and 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;
- …and more.
|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||Featurizing 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), visualization (Matplotlib, Plotly, Seaborn)||Calibration of NNs, backpropagation||Finite Markov decision processes||Hyperparameter optimization, 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, randomized 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|
Your course was designed for practitioners by practitioners with a mathematical and computational background. It was meant to build a solid theoretical foundation, which is vital for understanding data science and machine learning.
The emphasis, however, was not on theory but on getting results in practice. As George Pólya put it, mathematics is not a spectator sport!
For this very reason, we used active learning. Practical exercises were provided in unassessed tutorials, and Jupyter-based laboratory sessions.
There were no formal prerequisites for the course, although some familiarity with linear algebra, probability, and optimization theory was a plus. We refreshed the delegates’ memory whenever they needed a refresher.
Those who wished to read up on AI/ML before starting the course were recommented Intelligent Data Analysis by Berthold and Hand, The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Hastie, Tibshirani, and Friedman, and Deep Learning by Goodfellow, Bengio, and Courville. Some of these books are freely available online.
The course began with a review of Python programming, visualization, and libraries.
We reviewed probability and statistics needed for machine learning, examined linear regression methods as a basic example of a supervised machine learning technique, considered dimensionality reduction, unsupervised machine learning, bias-variance tradeoff, model and feature selection, classification, until focussing on neural nets and deep learning (the emphasis of this course).
Deep learning architectures such as deep neural networks, deep belief networks, and recurrent neural networks had been applied to many fields, including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, and board game programmes, where they had produced results comparable to and, in some cases, superior to human experts.
The course examines several real-life datasets, including:
- S&P 500 stock data
- High-frequency commodity future price data
- Cryptocurrency order book data
- FINRA TRACE corporate bond data
- Car insurance claim data
- The National Institute of Diabetes and Digestive and Kidney Diseases data about the Pima group diabetes tendency
The course is designed to be self-contained. However, those wishing to read up on Machine Learning / Artificial Intelligence before starting the course are recommended:
- Michael R. Berthold (ed.), David Hand (ed.). Intelligent Data Analysis: An Introduction, second edition. Springer, 2006.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. Springer, 2009.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2017.
Those wishing to read up on the mathematical foundations of your course (covered on the first day) are recommended:
- Murray R. Spiegel, John Schiller, R. Alu Srinivasan. Schaum’s Outlines: Probability and Statistics, second edition. McGraw-Hill, 2000.
- John B. Fraleigh, Raymond A. Beauregard. Linear Algebra, third edition. Addison Wesley, 1995.
- Gerard Cornuejols, Reha Tütüncü. Optimization Methods in Finance. Cambridge University Press, 2007.
- Philip E. Gill, Walter Murray, Margaret H. Wright. Practical Optimization. Emerald Group Publishing Limited, 1982.
We also recommend the following video lectures:
- Gilbert Strang. Linear Algebra, course 18.06 MIT, Fall of 1999: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
Saeed Amen is a Co-Founder of Thalesians and Founder of Cuemacro.
Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura.
He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor (with Alexander Denev) of The Book of Alternative Data (Wiley).
Through Cuemacro he now consults and publishes research for clients in the area of systematic trading.
He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis.
His clients have included major quant funda and data companies such as Bloomberg.
He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England, and Federal Reserve Board.
He is also a visiting lecturer at Queen Mary University of London.
Paul Bilokon, PhD
CEO and Founder of Thalesians Ltd. Previously served as Director and Head of global credit and core e-trading quants at Deutsche Bank, the teams that he helped set up with Jason Batt and Martin Zinkin. Having also worked at Morgan Stanley, Lehman Brothers, and Nomura, Paul pioneered electronic trading in credit with Rob Smith and William Osborn at Citigroup.
Paul has graduated from Christ Church, University of Oxford, with a distinction and Best Overall Performance prize. He has also graduated twice from Imperial College London.
Paul’s lectures at Imperial College London in machine learning for MSc students in mathematics and finance and his courses consistently achieve top rankings among the students.
Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. Paul’s books are being published by Wiley and Springer.
Dr Bilokon is a Member of the British Computer Society, Institution of Engineering and Technology, and European Complex Systems Society.
Paul is a frequent speaker at premier conferences such as Global Derivatives/QuantMinds, WBS QuanTech, AI, and Quantitative Finance conferences, alphascope, LICS, and Domains.
Prof. Matthew Dixon
Assistant Progessor in the Applied Math Department at the Illinois Institute of Technology. His research in computational methods for finance is funded by Intel.
Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modelling.
He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively.
He has published over 20 peer reviewed publications on machine learning and financial modelling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert.
Blanka Horvath, PhD
Blanka is an Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.
Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.
Blanka has co-authored a paper introducing one of the first applications of neural networks to mathematical finance, facilitating the calibration of rough volatility models.
Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
Quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, xVA, and electronic trading across asset classes. Ivan has consulted at many different banks in London, including JP Morgan, Citigroup, Jefferies, Nomura, HSBC, and BNP Paribas.
Ivan has generated convincing results in electronic trading alpha with neural nets. Ivan has developed a trading platform for the cryptocurrency for electronic market making.
Ivan is an author of several machine learning articles and appears regularly in QuantNews. Ivan regularly delivers guest lectures on artificial intelligence and machine learning at Imperial College and at Thalesians’ seminars.
Ivan has graduated from new Economic School with a Masters degree in economics. He has a solid mathematical background from Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.
Ivan is an accomplished sportsman.
The University of Oxford
Our trainings take place at one of the constituent colleges of the University of Oxford, the oldest university in the English-speaking world and the world’s second-oldest university in continuous operation. Teaching at Oxford goes back as far as 1096.
There are 38 constituent colleges at Oxford and a full range of academic departments organized into four divisions. Christ Church, or Ædes Christi in Latin, is a constituent college of the University of Oxford. It is colloquially known as The House. The college, especially its dining hall, have been featured in the Harry Potter movies.
Sixty-nine Novel Prize winners, four Fields Medalists, and six Turing Award winners have studied, worked, or held visiting fellowships at the University of Oxford.
For all participants, accommodation on Oxford’s campus will be provided. 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 others.