Intensive School in ML/AI

Christ Church, Oxford

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
  • GANs
  • 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.

Instructors

  • Saeed Amen
  • Paul Bilokon, PhD
  • Prof. Matthew Dixon, PhD
  • Blanka Horvath, PhD
  • Ivan Zhdankin

Venue

Peckwater Quad, Christ Church

The Christ Church (Ædes Christi) college of the University of Oxford.

Timing

Preparatory day: Wednesday, 1 April, 2020

Main School: Thursday, 2 April, 2020 – Sunday, 5 April, 2020

Accommodation

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.

Schedule

TimeDay 0: PreparatoryDay 1Day 2Day 3Day 4A: Finance, Trading, EconomicsDay 4B: Life Sciences
08:30 – 09:00Registration and welcomeRegistration and welcomeRegistration and welcomeRegistration and welcomeRegistration and welcomeRegistration and welcome
09:00 – 10:00Fundamentals of PythonOverview of ML, scikit-learnNN librariesBayesian methodsOverview of alternative dataFeaturising molecules, RDKit
10:00 – 10:30Advanced Python featuresDecision trees, random forests, ensemble regressors, elastic netsAdvanced architecture patternsKalman and particle filteringWeb crawling / scraping (Scrapy, BeautifulSoup)Graph NNs, DeepChem
10:30 – 11:00Coffee breakCoffee breakCoffee breakCoffee breakCoffee breakCoffee break
11:00 – 12:00Computational complexity and distributed computingOverview of NNs, feedforward NNsReinforcement learning, OpenAI gymMCMCNatural language processing (NLTK, Stanford Core NLP, Genism, spaCy, pattern, TextBlob, BERT)Generative models
12:00 – 12:30Python libraries for working with data (NumPy, Pandas), visualisation (Matplotlib, Plotly, Seaborn)Calibration of NNs, backpropagationFinite Markov decision processesHyperparameter optimisation, crossvalidationThe practice of extracting value from alternative datasetsDeep learning for genomics
12:30 – 13:30LunchLunchLunchLunchLunchLunch
13:30 – 14:30Exploratory data analysisMaths of NNsDeep reinforcement learningPCA, randomised PCA, kernel PCA and related methodsAn overview of volatility modellingDeep learning for medicine
14:30 – 15:00Preparing and downloading data, web scrapingConvnetsGenerative deep learningUnsupervised deep learning, self-supervised deep learningApplying NNs to derivative pricingDeep learning for microscopy
15:00 – 15:30Coffee breakCoffee breakCoffee breakCoffee breakCoffee breakCoffee break
15:30 – 16:30Introduction to kdb+/qRNNsGANsQuantum computingLessons from applying NNs to derivative pricingOverview of life sciences datasets
16:30 – 17:00kdb+/q tables, selects, and joinsGPU computing for deep learningTime series analysis, working with high-frequency dataQuantum MLFrontiers of ML/AI in finance and economicsFrontiers of ML/AI in life sciences
17:00 – 18:00LabLabLabLabLabLab
18:00 – 19:30Tour of Christ ChurchTour of Oxford City CentreVisit to Oxford’s historic pub The Eagle & Child, home of The InklingsGraduation and leaving drinks at the ButteryGraduation and leaving drinks at the Buttery
19:30 – 21:00Dinner at the Dining HallDinner at the Dining HallDinner at the Dining HallDinner at the Dining Hall

Datasets

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.

Bibliography

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.