Training Calendar

Advanced Machine Learning using Stata - Co-Developed with Lancaster University

Online 2 days (25th October 2021 - 26th October 2021) Stata Advanced, Intermediate
Automation, Data Management, Programming, Statistics

Course Overview

If you would like to join the more introductory course of machine learning with Stata, click here.

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 Timetable

Morning Session Afternoon Session Q&A with Instructor
10am-12pm 2pm-4pm 4pm-4:30pm
Post your comment

Timberlake Consultants

Course Agenda: Advanced course

DAY 1 Session 1 (10:00-12:00 London time): 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 London time) 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 (10:00 - 12:00 London time)

    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 (14:00 - 16:00 London time)

      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
      2-day pass (25/10/2021 - 26/10/2021)

    All prices exclude VAT or local taxes where applicable.

    * Required Fields

    £0