Training Calendar

Machine Learning using Stata: Introduction & Advanced - in collaboration with Lancaster University (online)

Online 4 days (26th October 2020 - 10th November 2020) Stata Intermediate, Introductory

Course Overview: Part one

Recent years have witnessed an unprecedented availability of information on social, economic, and health-related phenomena. Researchers, practitioners, and policymakers have nowadays access to huge datasets (the so-called “Big Data”). This data is collected on people, companies and institutions, web and mobile devices and satellites, at an increasing speed and detail.

Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. It's primary objective is that of turning information into knowledge and value by “letting the data speak”. Machine learning limits prior assumptions about data structure, and relies on a model-free philosophy that supports algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development, and analytical solutions. Machine learning was computationally unfeasible up until a few years ago. It is only possible on the machines of today, with their increased computing power and ability to learn, their hardware development, and with continuous software upgrading.

This course is a primer to machine learning techniques using Stata. Stata owns various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with Stata's potential to draw knowledge and value form row, large, and possibly noisy data. The teaching approach will be mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including, among others:

  • (i) factor-importance detection
  • (ii) signal-from-noise extraction
  • (iii) correct model specification
  • (iv) model-free classification, both from a data-mining and a causal perspective.

Course Overview: part 2

No prior knowledge of machine learning techniques are required to attend this course, as the first session will start from scratch with a fresh introduction to the subject. This course will focus on three specific techniques not covered in the first-part of the course, that is: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).

The teaching approach will be mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including:

  • (i) factor-importance detection,
  • (ii) signal-from-noise extraction,
  • (iii) model-free regression and classification, both from a data-mining and a causal perspective.

The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.

Course Agenda: Introductory course

    DAY 1
    Session 1 (10:00-12:00 GMT): The basics of Machine Learning
    Machine Learning: definition, rational, usefulness
  • Supervised vs. unsupervised learning
  • Regression vs. classification problems
  • Inference vs. prediction
  • Sampling vs. specification error
  • Coping with the fundamental non-identifiability of E(y|x)
  • Parametric vs. non-parametric models
  • The trade-off between prediction accuracy and model interpretability
  • Goodness-of-fit measures
  • Measuring the quality of fit: in-sample vs. out-of-sample prediction power
  • The bias-variance trade-off and the Mean Square Error (MSE) minimization
  • Training vs. test mean square error
  • The information criteria approach
  • Estimating training and test error
  • Validation set, K-fold cross-validation, and the Bootstrap

  • Session 2 (14:00-16:00 GMT): Model Selection and regularization
    Model selection as a correct specification procedure
  • The information criteria approach
  • Subset Selection
  • Best subset selection
  • Backward stepwise selection
  • Forward stepwise Selection
  • Shrinkage Methods
  • Lasso and Ridge, and Elastic regression
  • Adaptive Lasso
  • Information criteria and cross validation for Lasso
  • Stata implementation

    DAY 2
    Session 1 (10:00-12:00 GMT):Discriminant analysis and nearest-neighbor classification
  • The classification setting
  • Bayes optimal classifier and decision boundary
  • Misclassification error rate
  • Discriminant analysis
  • Linear and quadratic discriminant analysis
  • Naive Bayes classifier
  • The K-nearest neighbours classifier Stata implementation

    Session 2 (14:00-16:00 GMT): Neural networks
    The neural network model
  • Neurons, hidden layers, and multi-outcomes
  • Training a neural networks
  • Back-propagation via gradient descent
  • Fitting with high dimensional data
  • Fitting remarks
  • Cross-validating neural network hyperparameters Stata implementation

    Session 3 (16:00-17:00 GMT): Q&A session with the instructor
    The neural network model
  • Neurons, hidden layers, and multi-outcomes
  • Training a neural networks
  • Back-propagation via gradient descent
  • Fitting with high dimensional data
  • Fitting remarks
  • Cross-validating neural network hyperparameters Stata implementation

Course Agenda: Advanced course

DAY1 Session 1 (10:00-12:00 GMT): The basics of Machine Learning
Machine Learning: definition, rational, usefulness
  • Supervised vs. unsupervised learning
  • Regression vs. classification problems
  • Inference vs. prediction
  • Sampling vs. specification error
  • Coping with the fundamental non-identifiability of E(y|x)
  • Parametric vs. non-parametric models
  • The trade-off between prediction accuracy and model interpretability
  • Goodness-of-fit measures
  • Measuring the quality of fit: in-sample vs. out-of-sample prediction power
  • The bias-variance trade-off and the Mean Square Error (MSE) minimization
  • Training vs. test mean square error
  • The information criteria approach
  • Estimating training and test error
  • Validation set, K-fold cross-validation, and the Bootstrap

  • Session 2 (2 hours) Kernel-based and Nearest-neighbour methods
    • Beyond parametric models: an overview
    • Local, semi-global, and global approaches
    • Local methods
    • Kernel-based regression
    • Nearest-neighbour regression
    • Nearest-neighbour classification
    DAY2

    Session 3: Semi-global and global approaches (2 hours)

    Semi-global methods
    • Constant step-function
    • Piece wise polynomials
    • Spline regression
    • Global methods
    • Polynomial and series estimators
    • Partially linear models
    • Generalised additive models
    • Stata implementation

    Session 4: Tree-based methods (2 hours)

      Regression and classification trees: an introduction
    • Growing a tree via recursive binary splitting
    • Optimal tree pruning via cross-validation
    • Tree-based ensemble methods
    • Bagging
    • Random forests
    • Boosting
    • Stata implementation

    Session 3 - 1 hour: Q&A with the instructor

    Prerequisites

    Knowledge of basic statistics, Stata and econometrics is required, including:

    • The notion of conditional expectation and related properties;
    • point and interval estimation;
    • regression model and related properties;
    • probit and logit regression.
    Reading List:
    • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, T., Tibshirani, R., Friedman, J., Springer (2009)
    • An Introduction to Statistical Learning, Gareth, J., Witten, D., Hastie, T., Tibshirani, R., Springer (2013)
    • Microeconometrics Using Stata, Cameron e Trivedi, Revised Edition, StataPress (2010)
    • A Super-Learning Machine for Predicting Economic Outcomes, Giovanni Cerulli

    Terms & Conditions

    • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
    • Additional discounts are available for multiple registrations. Contact us for more information.
    • Temporary, time limited licences for the software(s) used in the course will be provided. You are required to install the software provided prior to the start of the course.
    • Full payment of course fees is required prior to the course start date to guarantee your place.
    • Registration closes 1 calendar day prior to the start of the course.

    Cancellations or changes to your registration

    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
    •  CommercialAcademicStudent
      26th - 27th October & 9th - 10th November 4 day course (10-12 & 2-4pm GMT, 2020) (26/10/2020 - 10/11/2020)
      26th - 27th October (10-12 & 2-4pm GMT, 2020) (26/10/2020 - 27/10/2020)
      9th - 10th November (10-12 & 2-4pm GMT, 2020) (09/11/2020 - 10/11/2020)

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