SB2b Statistical Machine Learning Hilary Term 2017


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1 SB2b Statistical Machine Learning Hilary Term 2017 Mihaela van der Schaar and Seth Flaxman Guest lecturer: Yee Whye Teh Department of Statistics Oxford Slides and other materials available at: page/course_ml.html
2 Administrative details Course Structure MMath Part B & MSc in Applied Statistics Lectures: MSc: Part C: Wednesdays 12:0013:00, LG.01. Thursdays 16:0017:00, LG problem sheets, discussed at the classes: weeks 2,4,6,7 (check website) 4 problem sheets Class Tutors: Lloyd Elliott, Kevin Sharp, and Hyunjik Kim Please sign up for the classes on the sign up sheet!
3 Administrative details Course Aims 1 Understand statistical fundamentals of machine learning, with a focus on supervised learning (classification and regression) and empirical risk minimisation. 2 Understand difference between generative and discriminative learning frameworks. 3 Learn to identify and use appropriate methods and models for given data and task. 4 Learn to use the relevant R or python packages to analyse data, interpret results, and evaluate methods.
4 Administrative details Syllabus I Part I: Introduction to supervised learning (4 lectures) Empirical risk minimization Bias/variance, Generalization, Overfitting, Cross validation Regularization Logistic regression Neural networks Part II: Classification and regression (3 lectures) Generative vs. Discriminative models Knearest neighbours, Maximum Likelihood Estimation, Mixture models Naive Bayes, Decision trees, CART Support Vector Machines Random forest, Boostrap Aggregation (Bagging), Ensemble learning Expectation Maximization
5 Administrative details Syllabus II Part III: Theoretical frameworks Statistical learning theory Decision theory Part IV: Further topics Optimisation Hidden Markov Models Backwardforward algorithms Reinforcement learning
6 Overview What is Machine Learning? Statistical Machine Learning
7 Overview Statistical Machine Learning What is Machine Learning? Arthur Samuel, 1959 Field of study that gives computers the ability to learn without being explicitly programmed.
8 Overview Statistical Machine Learning What is Machine Learning? Arthur Samuel, 1959 Field of study that gives computers the ability to learn without being explicitly programmed. Tom Mitchell, 1997 Any computer program that improves its performance at some task through experience.
9 Overview Statistical Machine Learning What is Machine Learning? Arthur Samuel, 1959 Field of study that gives computers the ability to learn without being explicitly programmed. Tom Mitchell, 1997 Any computer program that improves its performance at some task through experience. Kevin Murphy, 2012 To develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest.
10 Overview What is Machine Learning? Statistical Machine Learning Information Structure Prediction Decisions Actions data Larry Page about DeepMind s ML systems that can learn to play video games like humans
11 Overview What is Machine Learning? Statistical Machine Learning statistics business finance computer science biology genetics Machine Learning cognitive science psychology physics engineering operations research mathematics
12 Overview Statistical Machine Learning What is Data Science? Early years John Tukey, The Future of Data Analysis, 1962 For a long time I have thought I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt.... All in all I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data Four driving forces, according to Tukey The formal theories of statistics Accelerating developments in computers... The challenge, in many fields, of more and ever larger bodies of data The emphasis on quantification in an ever wider variety of disciplines
13 Overview Statistical Machine Learning What is Data Science? Bin Yu, Let us own Data Science, IMS Presidential Address, 2014 Statistics Domain/science knowledge Computing Collaboration/teamwork Communication to outsiders David Donoho, 50 years of Data Science, 2015 Greater Data Science : Data Exploration and Preparation Data Representation and Transformation Computing with Data Data Modeling Data Visualization and Presentation Science about Data Science
14 Overview Statistical Machine Learning Statistics vs Machine Learning Traditional Problems in Applied Statistics Well formulated question that we would like to answer. Expensive data gathering and/or expensive computation. Create specially designed experiments to collect high quality data. Information Revolution Improvements in data processing and data storage. Powerful, cheap, easy data capturing. Lots of (low quality) data with potentially valuable information inside. CS and Stats forced back together: unified framework of data, inferences, procedures, algorithms statistics taking computation seriously computing taking statistical risk seriously Michael I. Jordan: On the Computational and Statistical Interface and "Big Data" Max Welling: Are Machine Learning and Statistics Complementary?
15 Overview Types of Machine Learning Types of Machine Learning Unsupervised learning Extract key features of the unlabelled data clustering, signal separation, density estimation Goal: representation, hypothesis generation, visualization Supervised learning Data contains labels : every example is an inputoutput pair classification, regression Goal: prediction on new examples
16 Overview Types of Machine Learning Types of Machine Learning Semisupervised Learning A database of examples, only a small subset of which are labelled. Multitask Learning A database of examples, each of which has multiple labels corresponding to different prediction tasks. Reinforcement Learning An agent acting in an environment, given rewards for performing appropriate actions, learns to maximize their reward.
17 Overview Supervised Learning Supervised Learning Unsupervised learning: To extract structure and postulate hypotheses about data generating process from unlabelled observations x 1,..., x n. Visualize, summarize and compress data. Supervised learning: In addition to the observations of X, we have access to their response variables / labels Y Y: we observe {(x i, y i )} n i=1. Types of supervised learning: Classification: discrete responses, e.g. Y = {+1, 1} or {1,..., K}. Regression: a numerical value is observed and Y = R. The goal is to accurately predict the response Y on new observations of X, i.e., to learn a function f : R p Y, such that f (X) will be close to the true response Y.
18 Overview Supervised Learning Applications of Machine Learning spam filtering recommendation systems fraud detection selfdriving cars image recognition stock market analysis ImageNet: Computer Eyesight Gets a Lot More Accurate, Krizhevsky et al, 2012 New applications of ML: Machine Learning is Eating the World
19 Machine learning in practice Spam detection Observations X are text documents Labels Y are spam = +1 and not spam = 1. How do we encode documents of different lengths as a vector X R p? Given a set of labelled documents {(x i, y i )} n i=1 how do we learn a function f : R p Y Many answers to both questions will be covered in this course: logistic regression, naive Bayes, neural networks, Support Vector Machines, etc.
20 Image classification Machine learning in practice Observations X are images Labels Y {0, 1,..., 9} Learn a function f : R p Y
21 Face recognition Machine learning in practice Observations X are images Labels Y are a very large set of people: {Queen Elizabeth, Bill Gates, Justin Trudeau, Leonardo DiCaprio, etc.} How do we encode images as vectors X R p? Given a set of labelled images {(x i, y i )} n i=1 how do we learn a function f : R p Y Fundamentally harder or different than image classification?
22 Machine learning in practice Face detection Farfade, Saberian, and Li
23 Machine learning in practice Face detection Farfade, Saberian, and Li Observations X are images What are the labels Y? How should our function f work?
24 Machine translation Machine learning in practice Kyunghyun Cho introductionneuralmachinetranslationgpuspart3/ Observations X are sentences in language A Labels Y are sentences in language B How should we encode X and Y numerically? Is this regression or classification?
25 Speech recognition Machine learning in practice Dahl et al. 2012
26 Machine learning in practice Selfdriving cars 27 million connections and 250 thousand parameters devblogs.nvidia.com/parallelforall/ deeplearningselfdrivingcars/
27 Machine learning in practice Product recommendation Fully observe all user interactions on a website (what pages they view, what items they buy, what reviews they leave, etc.) What products should be recommended to them? On which websites? How can you phrase this as supervised learning?
28 Machine learning in practice Software Software R Python: scikitlearn, mlpy, Theano Weka, mlpack, Torch, Shogun, TensorFlow... Matlab/Octave
29 Machine learning in practice Software Machine learning advances in 2016 and challenges ahead 2016: Free/open source software for deep learning: TensorFlow (Google), CNTK (Microsoft), PaddlePaddle (Baidu), MXNet (Amazon) Audio generation Go Advances in machine translation (Google translate) 2017 and beyond: Increasing concern about, regulation of algorithms Transparency / explainability in machine learning Effect of increasing automation of work on society Medical advances?
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