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MLPI Lecture 0: Overview

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Overview of the course MLPI -- Advanced topics on machine learning and probabilistic inference.

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MLPI Lecture 0: Overview

  1. 1. Lecture 0 Course Overview Dahua Lin The Chinese University of Hong Kong 1
  2. 2. About this Course This is a graduate level introduction to advanced statistical learning. 2
  3. 3. Course Format • No Exams! • Topic driven • For each topic: • Introductory lecture • Paper reading and Homework • In-class discussion • You will present a paper/subject at the end of 3
  4. 4. What is Machine Learning? Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions and decisions, rather than following only explicitly programmed instructions. -- Wikipedia 4
  5. 5. Elements of Machine Learning • Elements: • Data • Model • Learning Algorithms • Prediction • Learn from old data, make predictions on new 5
  6. 6. Please write down five machine learning algorithms that you know. Don't write Deep Learning. 6
  7. 7. Basic Forms of Machine Learning • Supervised learning • Unsupervised learning • Semi-supervised learning • Reinforcement learning 7
  8. 8. Machine Learning Tasks • Classification • Regression • Clustering • Dimension Reduction • Density Estimation 8
  9. 9. What this Course is About • The course is not to teach you: • Support Vector Machine • Linear Regression • ... • Deep Learning • Instead, you are going to learn foundational theories and tools for developing your own models and algorithms. 9
  10. 10. Topics • Markov Chain Monte Carlo • Exponential family distributions and conjugate prior • Generalized linear model • Empirical risk minimization and Stochastic gradient descent • Proximal methods for optimization 10
  11. 11. Topics (cont'd) • Graphical models: Bayesian Networks and Markov random fields • Sum-product and max-product algorithms, Belief propagation • Variational inference methods • Gaussian Processes and Copula Processes • Handling Big Data 11

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