Introduction to Machine Learning Workshop of the UP Center for Integrative and Development Studies (UP CIDS) Program on Data Science for Public Policy (DSPP)
Selected participants from UP colleges/units and different sectors attended the 3-day Introduction to Machine Learning Workshop conducted by UP CIDS Data Science for Public Policy Program (UP CIDS DSPPP) on August 2019.
The Data Science for Public Policy Program is among the initiatives of the UP Center for Integrative and Development Studies (UP CIDS) of the Office of the Vice President for Academic Affairs. The Program aims to build capacity of UP faculty in data science as applied to challenges in public policy and governance. In relation to this, the Program endeavors to build and engage a community of faculty and encourage the pursuit of interdisciplinary problem-oriented research using high-level quantitative analyses. Other objectives include organizing multidisciplinary teams with social scientists, humanists, and scientists to conduct research on issues in the public sector.
The workshop is a training course for beginners and non-programmers to introduces the fundamentals of Machine Learning, a family of algorithms that learn from experience and make decisions accordingly. The training includes lectures and practical hands-on sessions for participants who will be introduced to Machine Learning using the Python programming language while commencing work on our preferred datasets.
Participants are required to submit an output to the UP CIDS Data Science Program, a draft Policy Note (2,500 words) reporting on the findings of their analysis in the workshop.
UP Data Science Program
Day 1 – Intro to Python Programming
1.1 Introduction / Python and Python Notebook Installation
1.2 Basic Data Types / Expressions / Basic Input and Output
1.5 Functions and Modules
1.6 Data Structures: Strings, Lists, Tuples, Dictionaries
1.7 Plotting using Python
1.8 Create a basic web scraper
Day 2 – What is Machine Learning? / The Naive Bayes Classifier
2.1 Overview of Machine Learning
2.2 Supervised Learning and Applications
2.3 Unsupervised Learning and Applications
3.1 Review of Basic Statistics / Bayes Theorem
3.2 Naive Bayes Classifier
3.3 Hands-on Exercises
Day 3 – Neural Networks
4.1 The Perceptron
4.2 Multi-layer Perceptron
4.3 Backpropagation Algorithm
4.4 Applications of the MLP
4.5 Hands-on Exercises
As of 20 August 2019, we already submitted the policy draft for review. The dataset we worked on was about “The State of Non-traditional Work in the Philippines: 2019 Filipino Online Freelancers,” currently we are still gathering data of Filipino online workers nationwide. The target completion of the survey is set on the fourth quarter of 2019. The link to the online survey, please click here.
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