SB2b Statistical Machine Learning Hilary Term 2017


 Paula Fleming
 10 months ago
 Views:
Transcription
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?
CSC321 Lecture 1: Introduction
CSC321 Lecture 1: Introduction Roger Grosse Roger Grosse CSC321 Lecture 1: Introduction 1 / 26 What is machine learning? For many problems, it s difficult to program the correct behavior by hand recognizing
More informationCS545 Machine Learning
Machine learning and related fields CS545 Machine Learning Course Introduction Machine learning: the construction and study of systems that learn from data. Pattern recognition: the same field, different
More informationSession 1: Gesture Recognition & Machine Learning Fundamentals
IAP Gesture Recognition Workshop Session 1: Gesture Recognition & Machine Learning Fundamentals Nicholas Gillian Responsive Environments, MIT Media Lab Tuesday 8th January, 2013 My Research My Research
More informationLecture 1: Introduc4on
CSC2515 Spring 2014 Introduc4on to Machine Learning Lecture 1: Introduc4on All lecture slides will be available as.pdf on the course website: http://www.cs.toronto.edu/~urtasun/courses/csc2515/csc2515_winter15.html
More informationCSE 546 Machine Learning
CSE 546 Machine Learning Instructor: Luke Zettlemoyer TA: Lydia Chilton Slides adapted from Pedro Domingos and Carlos Guestrin Logistics Instructor: Luke Zettlemoyer Email: lsz@cs Office: CSE 658 Office
More informationIntroduction to Machine Learning
Introduction to Machine Learning Hamed Pirsiavash CMSC 678 http://www.csee.umbc.edu/~hpirsiav/courses/ml_fall17 The slides are closely adapted from Subhransu Maji s slides Course background What is the
More informationWhat is Machine Learning?
What is Machine Learning? INFO4604, Applied Machine Learning University of Colorado Boulder August 2931, 2017 Prof. Michael Paul Definition Murphy: a set of methods that can automatically detect patterns
More informationMachine Learning L, T, P, J, C 2,0,2,4,4
Subject Code: Objective Expected Outcomes Machine Learning L, T, P, J, C 2,0,2,4,4 It introduces theoretical foundations, algorithms, methodologies, and applications of Machine Learning and also provide
More informationMACHINE LEARNING WITH SAS
This webinar will be recorded. Please engage, use the Questions function during the presentation! MACHINE LEARNING WITH SAS SAS NORDIC FANS WEBINAR 21. MARCH 2017 Gert Nissen Technical Client Manager Georg
More informationMachine Learning for NLP
Natural Language Processing SoSe 2014 Machine Learning for NLP Dr. Mariana Neves April 30th, 2014 (based on the slides of Dr. Saeedeh Momtazi) Introduction Field of study that gives computers the ability
More informationGovernment of Russian Federation. Federal State Autonomous Educational Institution of High Professional Education
Government of Russian Federation Federal State Autonomous Educational Institution of High Professional Education National Research University Higher School of Economics Syllabus for the course Advanced
More informationM. R. Ahmadzadeh Isfahan University of Technology. M. R. Ahmadzadeh Isfahan University of Technology
1 2 M. R. Ahmadzadeh Isfahan University of Technology Ahmadzadeh@cc.iut.ac.ir M. R. Ahmadzadeh Isfahan University of Technology Textbooks 3 Introduction to Machine Learning  Ethem Alpaydin Pattern Recognition
More informationMachine Learning Lecture 1: Introduction
Welcome to CSCE 478/878! Please check off your name on the roster, or write your name if you're not listed Indicate if you wish to register or sit in Policy on sitins: You may sit in on the course without
More informationStatistical Learning Classification STAT 441/ 841, CM 764
Statistical Learning Classification STAT 441/ 841, CM 764 Ali Ghodsi Department of Statistics and Actuarial Science University of Waterloo aghodsib@uwaterloo.ca Two Paradigms Classical Statistics Infer
More informationCS534 Machine Learning
CS534 Machine Learning Spring 2013 Lecture 1: Introduction to ML Course logistics Reading: The discipline of Machine learning by Tom Mitchell Course Information Instructor: Dr. Xiaoli Fern Kec 3073, xfern@eecs.oregonstate.edu
More informationWelcome to CMPS 142 and 242: Machine Learning
Welcome to CMPS 142 and 242: Machine Learning Instructor: David Helmbold, dph@soe.ucsc.edu Office hours: Monday 1:302:30, Thursday 4:155:00 TA: Aaron Michelony, amichelo@soe.ucsc.edu Web page: www.soe.ucsc.edu/classes/cmps242/fall13/01
More informationStatistics and Machine Learning, Master s Programme
DNR LIU201702005 1(9) Statistics and Machine Learning, Master s Programme 120 credits Statistics and Machine Learning, Master s Programme F7MSL Valid from: 2018 Autumn semester Determined by Board of
More informationLarge Scale Data Analysis Using Deep Learning
Large Scale Data Analysis Using Deep Learning Introduction to Deep Learning U Kang Seoul National University U Kang 1 In This Lecture Overview of deep learning History of deep learning and its recent advances
More informationA Review on Machine Learning Algorithms, Tasks and Applications
A Review on Machine Learning Algorithms, Tasks and Applications Diksha Sharma 1, Neeraj Kumar 2 ABSTRACT: Machine learning is a field of computer science which gives computers an ability to learn without
More informationIndepth: Deep learning (one lecture) Applied to both SL and RL above Code examples
Introduction to machine learning (two lectures) Supervised learning Reinforcement learning (lab) Indepth: Deep learning (one lecture) Applied to both SL and RL above Code examples 20170930 2 1 To enable
More informationProgramming Social Robots for Human Interaction. Lecture 4: Machine Learning and Pattern Recognition
Programming Social Robots for Human Interaction Lecture 4: Machine Learning and Pattern Recognition ZhengHua Tan Dept. of Electronic Systems, Aalborg Univ., Denmark zt@es.aau.dk, http://kom.aau.dk/~zt
More informationEECS 349 Machine Learning
EECS 349 Machine Learning Instructor: Doug Downey (some slides from Pedro Domingos, University of Washington) 1 Logistics Instructor: Doug Downey Email: ddowney@eecs.northwestern.edu Office hours: Mondays
More informationEECS 349 Machine Learning
EECS 349 Machine Learning Instructor: Doug Downey (some slides from Pedro Domingos, University of Washington) 1 Logistics Instructor: Doug Downey Email: ddowney@eecs.northwestern.edu Office hours: Mondays
More informationApplied Machine Learning Lecture 1: Introduction
Applied Machine Learning Lecture 1: Introduction Richard Johansson January 16, 2018 welcome to the course! machine learning is getting increasingly popular among students our courses are full! many thesis
More informationCS540 Machine learning Lecture 1 Introduction
CS540 Machine learning Lecture 1 Introduction Administrivia Overview Supervised learning Unsupervised learning Other kinds of learning Outline Administrivia Class web page www.cs.ubc.ca/~murphyk/teaching/cs540fall08
More information10701: Intro to Machine Learning. Instructors: Pradeep Ravikumar, Manuela Veloso, Teaching Assistants:
10701: Intro to Machine Instructors: Pradeep Ravikumar, pradeepr@cs.cmu.edu Manuela Veloso, mmv@cs.cmu.edu Teaching Assistants: Shaojie Bai shaojieb@andrew.cmu.edu Adarsh Prasad adarshp@andrew.cmu.edu
More informationBird Species Identification from an Image
Bird Species Identification from an Image Aditya Bhandari, 1 Ameya Joshi, 2 Rohit Patki 3 1 Department of Computer Science, Stanford University 2 Department of Electrical Engineering, Stanford University
More informationMachine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 11, 2011
Machine Learning 10701 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 11, 2011 Today: What is machine learning? Decision tree learning Course logistics Readings: The Discipline
More informationCPSC 340: Machine Learning and Data Mining. Course Review/Preview Fall 2015
CPSC 340: Machine Learning and Data Mining Course Review/Preview Fall 2015 Admin Assignment 6 due now. We will have office hours as usual next week. Final exam details: December 15: 8:3011 (WESB 100).
More informationWelcome to CMPS 142: Machine Learning. Administrivia. Lecture Slides for. Instructor: David Helmbold,
Welcome to CMPS 142: Machine Learning Instructor: David Helmbold, dph@soe.ucsc.edu Web page: www.soe.ucsc.edu/classes/cmps142/winter07/ Text: Introduction to Machine Learning, Alpaydin Administrivia Sign
More informationINTRODUCTION TO DATA SCIENCE
DATA11001 INTRODUCTION TO DATA SCIENCE EPISODE 6: MACHINE LEARNING TODAY S MENU 1. WHAT IS ML? 2. CLASSIFICATION AND REGRESSSION 3. EVALUATING PERFORMANCE & OVERFITTING WHAT IS MACHINE LEARNING? Definition:
More informationMachine Learning. Introduction. Hamid Beigy. Sharif University of Technology. Fall 1395
Machine Learning Introduction Hamid Beigy Sharif University of Technology Fall 1395 Hamid Beigy (Sharif University of Technology) Machine Learning Fall 1395 1 / 15 Table of contents 1 What is machine learning?
More informationW4240 Data Mining. Frank Wood. September 6, 2010
W4240 Data Mining Frank Wood September 6, 2010 Introduction Data mining is the search for patterns in large collections of data Learning models Applying models to large quantities of data Pattern recognition
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationEra of AI (Deep Learning) and harnessing its true potential
Era of AI (Deep Learning) and harnessing its true potential Artificial Intelligence (AI) AI Augments our brain with infallible memories and infallible calculators Humans and Computers have become a tightly
More informationMachine Learning with MATLAB Antti Löytynoja Application Engineer
Machine Learning with MATLAB Antti Löytynoja Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB MATLAB as an interactive
More informationStay Alert!: Creating a Classifier to Predict Driver Alertness in Realtime
Stay Alert!: Creating a Classifier to Predict Driver Alertness in Realtime Aditya Sarkar, Julien KawawaBeaudan, Quentin Perrot Friday, December 11, 2014 1 Problem Definition Driving while drowsy inevitably
More informationMachine Learning y Deep Learning con MATLAB
Machine Learning y Deep Learning con MATLAB Lucas García 2015 The MathWorks, Inc. 1 Deep Learning is Everywhere & MATLAB framework makes Deep Learning Easy and Accessible 2 Deep Learning is Everywhere
More informationCS 445/545 Machine Learning Winter, 2017
CS 445/545 Machine Learning Winter, 2017 See syllabus at http://web.cecs.pdx.edu/~mm/machinelearningwinter2017/ Lecture slides will be posted on this website before each class. What is machine learning?
More informationINTRODUCTION TO MACHINE LEARNING
https://xkcd.com/894/ INTRODUCTION TO MACHINE LEARNING David Kauchak CS 158 Fall 2016 Why are you here? Machine Learning is What is Machine Learning? Machine learning is a subfield of computer science
More informationMachine Learning for SAS Programmers
Machine Learning for SAS Programmers The Agenda Introduction of Machine Learning Supervised and Unsupervised Machine Learning Deep Neural Network Machine Learning implementation Questions and Discussion
More informationIntroduction to Classification
Introduction to Classification Classification: Definition Given a collection of examples (training set ) Each example is represented by a set of features, sometimes called attributes Each example is to
More informationLoad Forecasting with Artificial Intelligence on Big Data
1 Load Forecasting with Artificial Intelligence on Big Data October 9, 2016 Patrick GLAUNER and Radu STATE SnT  Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg 2
More informationCOMP 527: Data Mining and Visualization. Danushka Bollegala
COMP 527: Data Mining and Visualization Danushka Bollegala Introductions Lecturer: Danushka Bollegala Office: 2.24 Ashton Building (Second Floor) Email: danushka@liverpool.ac.uk Personal web: http://danushka.net/
More informationThe Health Economics and Outcomes Research Applications and Valuation of Digital Health Technologies and Machine Learning
The Health Economics and Outcomes Research Applications and Valuation of Digital Health Technologies and Machine Learning Workshop W29  Session V 3:00 4:00pm May 25, 2016 ISPOR 21 st Annual International
More informationAbout This Specialization
About This Specialization The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skillsbased specialization is intended
More informationE9 205 Machine Learning for Signal Processing
E9 205 Machine Learning for Signal Processing Introduction to Machine Learning of Sensory Signals 14082017 Instructor  Sriram Ganapathy (sriram@ee.iisc.ernet.in) Teaching Assistant  Aravind Illa (aravindece77@gmail.com).
More informationAccelerating the Power of Deep Learning With Neural Networks and GPUs
Accelerating the Power of Deep Learning With Neural Networks and GPUs AI goes beyond image recognition. Abstract Deep learning using neural networks and graphics processing units (GPUs) is starting to
More informationLearning Agents: Introduction
Learning Agents: Introduction S Luz luzs@cs.tcd.ie October 28, 2014 Learning in agent architectures Agent Learning in agent architectures Agent Learning in agent architectures Agent perception Learning
More informationSyllabus Data Mining for Business Analytics  Managerial INFOGB.3336, Spring 2018
Syllabus Data Mining for Business Analytics  Managerial INFOGB.3336, Spring 2018 Course information When: Mondays and Wednesdays 34:20pm Where: KMEC 365 Professor Manuel Arriaga Email: marriaga@stern.nyu.edu
More informationCS519: Deep Learning. Winter Fuxin Li
CS519: Deep Learning Winter 2017 Fuxin Li Course Information Instructor: Dr. Fuxin Li KEC 2077, lif@eecs.oregonstate.edu TA: Mingbo Ma: mam@oregonstate.edu Xu Xu: xux@oregonstate.edu My office hour: TBD
More informationMachine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 12, 2015
Machine Learning 10601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: What is machine learning? Decision tree learning Course logistics Readings: The Discipline
More informationCS519: Deep Learning 1. Introduction
CS519: Deep Learning 1. Introduction Winter 2017 Fuxin Li With materials from Pierre Baldi, Geoffrey Hinton, Andrew Ng, Honglak Lee, Aditya Khosla, Joseph Lim 1 Cutting Edge of Machine Learning: Deep Learning
More informationLecture I Outline. Course information and details Why do machine learning? What is machine learning? Why now? Type of Learning
Lecture I Outline Course information and details Why do machine learning? What is machine learning? Why now? Type of Learning Association Classification Three types: Linear, Decision Tree, and Nearest
More informationDeep Learning Explained
Deep Learning Explained Module 1: Introduction and Overview Sayan D. Pathak, Ph.D., Principal ML Scientist, Microsoft Roland Fernandez, Senior Researcher, Microsoft Course outline What is deep learning?
More informationPG DIPLOMA IN MACHINE LEARNING & AI 11 MONTHS ONLINE
& PG DIPLOMA IN MACHINE LEARNING & AI 11 MONTHS ONLINE UpGrad is an online education platform to help individuals develop their professional potential in the most engaging learning environment. Online
More informationCSC 411 MACHINE LEARNING and DATA MINING
CSC 411 MACHINE LEARNING and DATA MINING Lectures: Monday, Wednesday 121 (section 1), 34 (section 2) Lecture Room: MP 134 (section 1); Bahen 1200 (section 2) Instructor (section 1): Richard Zemel Instructor
More informationIntroduction to Classification, aka Machine Learning
Introduction to Classification, aka Machine Learning Classification: Definition Given a collection of examples (training set ) Each example is represented by a set of features, sometimes called attributes
More informationLecture 1. Introduction Bastian Leibe Visual Computing Institute RWTH Aachen University
Advanced Machine Learning Lecture 1 Introduction 20.10.2015 Bastian Leibe Visual Computing Institute RWTH Aachen University http://www.vision.rwthaachen.de/ leibe@vision.rwthaachen.de Organization Lecturer
More informationIntroducing Deep Learning with MATLAB
Introducing Deep Learning with MATLAB What is Deep Learning? Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep
More informationCSC 411: Lecture 01: Introduction
CSC 411: Lecture 01: Introduction Richard Zemel, Raquel Urtasun and Sanja Fidler University of Toronto Zemel, Urtasun, Fidler (UofT) CSC 411: 01Introduction 1 / 44 Today Administration details Why is
More informationCOLLEGE OF SCIENCE. School of Mathematical Sciences. NEW (or REVISED) COURSE: COSSTAT747 Principles of Statistical Data Mining.
ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences NEW (or REVISED) COURSE: COSSTAT747 Principles of Statistical Data Mining 1.0 Course Designations
More informationDeep (Structured) Learning
Deep (Structured) Learning Yasmine Badr 06/23/2015 NanoCAD Lab UCLA What is Deep Learning? [1] A wide class of machine learning techniques and architectures Using many layers of nonlinear information
More informationP(A, B) = P(A B) = P(A) + P(B)  P(A B)
AND Probability P(A, B) = P(A B) = P(A) + P(B)  P(A B) P(A B) = P(A) + P(B)  P(A B) Area = Probability of Event AND Probability P(A, B) = P(A B) = P(A) + P(B)  P(A B) If, and only if, A and B are independent,
More informationIt s a Machine World. Predictive Analytics with Machine Learning
It s a Machine World Predictive Analytics with Machine Learning Greg Deckler gdeckler@fusionalliance.com @GregDeckler It s a Machine World Predictive Analytics with Machine Learning Greg Deckler gdeckler@fusionalliance.com
More informationMachine Learning and Pattern Recognition Introduction
Machine Learning and Pattern Recognition Introduction Giovanni Maria Farinella gfarinella@dmi.unict.it www.dmi.unict.it/farinella What is ML & PR? Interdisciplinary field focusing on both the mathematical
More informationCS4780/ Machine Learning
CS4780/5780  Machine Learning Fall 2012 Thorsten Joachims Cornell University Department of Computer Science Outline of Today Who we are? Prof: Thorsten Joachims TAs: Joshua Moore, Igor Labutov, Moontae
More informationCOMS 4771 Introduction to Machine Learning. Nakul Verma
COMS 4771 Introduction to Machine Learning Nakul Verma Machine learning: what? Study of making machines learn a concept without having to explicitly program it. Constructing algorithms that can: learn
More informationIntro to Deep Learning for Core ML
Intro to Deep Learning for Core ML It s Difficult to Make Predictions. Especially About the Future. @JulioBarros Consultant EString.com @JulioBarros http://estring.com 1 Core ML "With Core ML, you can
More informationComputer Vision for Card Games
Computer Vision for Card Games Matias Castillo matiasct@stanford.edu Benjamin Goeing bgoeing@stanford.edu Jesper Westell jesperw@stanford.edu Abstract For this project, we designed a computer vision program
More informationDeep Learning for Cognitive EW with COTS
Defense Solutions Division Deep Learning for Cognitive EW with COTS Chad Augustine, Product Manager, Integrated Systems 1 June 12, 2016 Approved for Public Release Overview Important Notes on information
More informationMachine Learning with Weka
Machine Learning with Weka SLIDES BY (TOTAL 5 Session of 1.5 Hours Each) ANJALI GOYAL & ASHISH SUREKA (www.ashishsureka.in) CS 309 INFORMATION RETRIEVAL COURSE ASHOKA UNIVERSITY NOTE: Slides created and
More informationUnsupervised Learning
17s1: COMP9417 Machine Learning and Data Mining Unsupervised Learning May 2, 2017 Acknowledgement: Material derived from slides for the book Machine Learning, Tom M. Mitchell, McGrawHill, 1997 http://www2.cs.cmu.edu/~tom/mlbook.html
More informationL1: Course introduction
Introduction Course organization Grading policy Outline What is pattern recognition? Definitions from the literature Related fields and applications L1: Course introduction Components of a pattern recognition
More information18 LEARNING FROM EXAMPLES
18 LEARNING FROM EXAMPLES An intelligent agent may have to learn, for instance, the following components: A direct mapping from conditions on the current state to actions A means to infer relevant properties
More informationRecommender Systems. Sargur N. Srihari
Recommender Systems Sargur N. srihari@cedar.buffalo.edu This is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/cse676 1 Topics in Recommender Systems Types of Recommender
More informationIntroduction to Machine Learning for NLP I
Introduction to Machine Learning for NLP I Benjamin Roth CIS LMU München Benjamin Roth (CIS LMU München) Introduction to Machine Learning for NLP I 1 / 49 Outline 1 This Course 2 Overview 3 Machine Learning
More informationCOMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection.
COMP 551 Applied Machine Learning Lecture 6: Performance evaluation. Model assessment and selection. Instructor: Herke van Hoof (herke.vanhoof@mail.mcgill.ca) Slides mostly by: Class web page: www.cs.mcgill.ca/~hvanho2/comp551
More informationData Mining. CS57300 Purdue University. Bruno Ribeiro. February 15th, 2018
Data Mining CS573 Purdue University Bruno Ribeiro February 15th, 218 1 Today s Goal Ensemble Methods Supervised Methods Metalearners Unsupervised Methods 215 Bruno Ribeiro Understanding Ensembles The
More informationA Few Useful Things to Know about Machine Learning. Pedro Domingos Department of Computer Science and Engineering University of Washington" 2012"
A Few Useful Things to Know about Machine Learning Pedro Domingos Department of Computer Science and Engineering University of Washington 2012 A Few Useful Things to Know about Machine Learning Machine
More informationIntroduction to Machine Learning
1, DATA11002 Introduction to Machine Learning Lecturer: Teemu Roos TAs: Ville Hyvönen and Janne Leppäaho Department of Computer Science University of Helsinki (based in part on material by Patrik Hoyer
More informationCS 6140: Machine Learning Spring 2017
CS 6140: Machine Learning Spring 2017 Instructor: Lu Wang College of Computer and Informa@on Science Northeastern University Webpage: www.ccs.neu.edu/home/luwang Email: luwang@ccs.neu.edu Time and Loca@on
More informationIntroduction to Pattern Recognition
Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Fall 2017 CS 551, Fall 2017 c 2017, Selim Aksoy (Bilkent University)
More informationIntroduction to Foundations of Graphical Models
Introduction to Foundations of Graphical Models David M. Blei Columbia University September 2, 2015 Probabilistic modeling is a mainstay of modern machine learning and statistics research, providing essential
More informationLecture 1. Introduction. Probability Theory
Lecture 1. Introduction. Probability Theory COMP90051 Machine Learning Sem2 2017 Lecturer: Trevor Cohn Adapted from slides provided by Ben Rubinstein Why Learn Learning? 2 Motivation We are drowning in
More informationMaster of Science in ECE  Machine Learning & Data Science Focus
Master of Science in ECE  Machine Learning & Data Science Focus Core Coursework (16 units) ECE269: Linear Algebra ECE271A: Statistical Learning I ECE 225A: Probability and Statistics for Data Science
More informationAppliancespecific power usage classification and disaggregation
Appliancespecific power usage classification and disaggregation Srinikaeth Thirugnana Sambandam, Jason Hu, EJ Baik Department of Energy Resources Engineering Department, Stanford Univesrity 367 Panama
More informationIntroduction to Deep Learning
Introduction to Deep Learning M S Ram Dept. of Computer Science & Engg. Indian Institute of Technology Kanpur Reading of Chap. 1 from Learning Deep Architectures for AI ; Yoshua Bengio; FTML Vol. 2, No.
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationCS Data Science and Visualization Spring 2016
CS 207  Data Science and Visualization Spring 2016 Professor: Sorelle Friedler sorelle@cs.haverford.edu An introduction to techniques for the automated and humanassisted analysis of data sets. These
More informationIntroduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep Learning Conor Daly 2015 The MathWorks, Inc. 1 Machine learning in action CamVid Dataset 1. Segmentation and Recognition Using Structure from Motion Point Clouds,
More informationNaive Bayes Classifier Approach to Word Sense Disambiguation
Naive Bayes Classifier Approach to Word Sense Disambiguation Daniel Jurafsky and James H. Martin Chapter 20 Computational Lexical Semantics Sections 1 to 2 Seminar in Methodology and Statistics 3/June/2009
More informationMachine Learning. Basic Concepts. Joakim Nivre. Machine Learning 1(24)
Machine Learning Basic Concepts Joakim Nivre Uppsala University and Växjö University, Sweden Email: nivre@msi.vxu.se Machine Learning 1(24) Machine Learning Idea: Synthesize computer programs by learning
More informationCS 2750: Machine Learning. Neural Networks. Prof. Adriana Kovashka University of Pittsburgh February 28, 2017
CS 2750: Machine Learning Neural Networks Prof. Adriana Kovashka University of Pittsburgh February 28, 2017 HW2 due Thursday Announcements Office hours on Thursday: 4:15pm5:45pm Talk at 3pm: http://www.sam.pitt.edu/arc
More information Introduzione al Corso  (a.a )
Short Course on Machine Learning for Web Mining  Introduzione al Corso  (a.a. 20092010) Roberto Basili (University of Roma, Tor Vergata) 1 Overview MLxWM: Motivations and perspectives A temptative syllabus
More informationArtificial Neural Networks. Andreas Robinson 12/19/2012
Artificial Neural Networks Andreas Robinson 12/19/2012 Introduction Artificial Neural Networks Machine learning technique Learning from past experience/data Predicting/classifying novel data Biologically
More informationLinear Models Continued: Perceptron & Logistic Regression
Linear Models Continued: Perceptron & Logistic Regression CMSC 723 / LING 723 / INST 725 Marine Carpuat Slides credit: Graham Neubig, Jacob Eisenstein Linear Models for Classification Feature function
More informationAzure Machine Learning. Designing Iris MultiClass Classifier
Media Partners Azure Machine Learning Designing Iris MultiClass Classifier Marcin Szeliga 20 years of experience with SQL Server Trainer & data platform architect Books & articles writer Speaker at numerous
More informationCS Data Mining. Introductions What Is It? Cultures of Data Mining
CS345  Data Mining Introductions What Is It? Cultures of Data Mining 1 Course Staff Instructors: Anand Rajaraman Jeff Ullman TA: Jeff Klingner 2 Requirements Homework (Gradiance and other) 20% Gradiance
More informationDetection of Insults in Social Commentary
Detection of Insults in Social Commentary CS 229: Machine Learning Kevin Heh December 13, 2013 1. Introduction The abundance of public discussion spaces on the Internet has in many ways changed how we
More information