# SB2b Statistical Machine Learning Hilary Term 2017

Save this PDF as:

Size: px
Start display at page:

Download "SB2b Statistical Machine Learning Hilary Term 2017"

## 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 12:00-13:00, LG.01. Thursdays 16:00-17:00, 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 K-nearest 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 Backward-forward 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 input-output pair classification, regression Goal: prediction on new examples

16 Overview Types of Machine Learning Types of Machine Learning Semi-supervised Learning A database of examples, only a small subset of which are labelled. Multi-task 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 self-driving 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 introduction-neural-machine-translation-gpus-part-3/ 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 Self-driving cars 27 million connections and 250 thousand parameters devblogs.nvidia.com/parallelforall/ deep-learning-self-driving-cars/

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: scikit-learn, 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

### CS545 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

### Session 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

### Lecture 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

### CSE 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

### Introduction 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

### What is Machine Learning?

What is Machine Learning? INFO-4604, Applied Machine Learning University of Colorado Boulder August 29-31, 2017 Prof. Michael Paul Definition Murphy: a set of methods that can automatically detect patterns

### Machine 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

### MACHINE 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

### Machine 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

### Government 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

### M. 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

### Machine 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 sit-ins: You may sit in on the course without

### Statistical 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

### CS534 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

### Welcome 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 1:30-2:30, Thursday 4:15-5:00 TA: Aaron Michelony, amichelo@soe.ucsc.edu Web page: www.soe.ucsc.edu/classes/cmps242/fall13/01

### Statistics and Machine Learning, Master s Programme

DNR LIU-2017-02005 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

### Large 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

### A 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

### In-depth: Deep learning (one lecture) Applied to both SL and RL above Code examples

Introduction to machine learning (two lectures) Supervised learning Reinforcement learning (lab) In-depth: Deep learning (one lecture) Applied to both SL and RL above Code examples 2017-09-30 2 1 To enable

### Programming 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 Zheng-Hua Tan Dept. of Electronic Systems, Aalborg Univ., Denmark zt@es.aau.dk, http://kom.aau.dk/~zt

### EECS 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

### EECS 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

### Applied 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

### CS540 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/cs540-fall08

### 10701: 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

### Bird 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

### Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 11, 2011

Machine Learning 10-701 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

### CPSC 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: 8:30-11 (WESB 100).

### Welcome 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

### INTRODUCTION 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:

### Machine 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?

### W4240 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

### Python 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

### Era 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

### Machine 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

### Stay Alert!: Creating a Classifier to Predict Driver Alertness in Real-time

Stay Alert!: Creating a Classifier to Predict Driver Alertness in Real-time Aditya Sarkar, Julien Kawawa-Beaudan, Quentin Perrot Friday, December 11, 2014 1 Problem Definition Driving while drowsy inevitably

### Machine 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

### CS 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?

### INTRODUCTION 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

### Machine 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

### Introduction 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

### Load 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

### COMP 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/

### The 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

About This Specialization The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended

### E9 205 Machine Learning for Signal Processing

E9 205 Machine Learning for Signal Processing Introduction to Machine Learning of Sensory Signals 14-08-2017 Instructor - Sriram Ganapathy (sriram@ee.iisc.ernet.in) Teaching Assistant - Aravind Illa (aravindece77@gmail.com).

### Accelerating 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

### Learning 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

### Syllabus Data Mining for Business Analytics - Managerial INFO-GB.3336, Spring 2018

Syllabus Data Mining for Business Analytics - Managerial INFO-GB.3336, Spring 2018 Course information When: Mondays and Wednesdays 3-4:20pm Where: KMEC 3-65 Professor Manuel Arriaga Email: marriaga@stern.nyu.edu

### CS519: 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

### Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University. January 12, 2015

Machine Learning 10-601 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

### CS519: 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

### Lecture 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

### Deep 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?

### PG 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

### CSC 411 MACHINE LEARNING and DATA MINING

CSC 411 MACHINE LEARNING and DATA MINING Lectures: Monday, Wednesday 12-1 (section 1), 3-4 (section 2) Lecture Room: MP 134 (section 1); Bahen 1200 (section 2) Instructor (section 1): Richard Zemel Instructor

### Introduction 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

### Lecture 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.rwth-aachen.de/ leibe@vision.rwth-aachen.de Organization Lecturer

### Introducing 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

### CSC 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: 01-Introduction 1 / 44 Today Administration details Why is

### COLLEGE OF SCIENCE. School of Mathematical Sciences. NEW (or REVISED) COURSE: COS-STAT-747 Principles of Statistical Data Mining.

ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM COLLEGE OF SCIENCE School of Mathematical Sciences NEW (or REVISED) COURSE: COS-STAT-747 Principles of Statistical Data Mining 1.0 Course Designations

### Deep (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 non-linear information

### P(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,

### It 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

### Machine 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

### CS4780/ 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

### COMS 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

### Intro 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 E-String.com @JulioBarros http://e-string.com 1 Core ML "With Core ML, you can

### Computer 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

### Deep 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

### Machine Learning with Weka

Machine Learning with Weka SLIDES BY (TOTAL 5 Session of 1.5 Hours Each) ANJALI GOYAL & ASHISH SUREKA (www.ashish-sureka.in) CS 309 INFORMATION RETRIEVAL COURSE ASHOKA UNIVERSITY NOTE: Slides created and

### Unsupervised 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, McGraw-Hill, 1997 http://www-2.cs.cmu.edu/~tom/mlbook.html

### L1: 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

### 18 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

### Recommender 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

### Introduction 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

### COMP 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

### Data 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 Meta-learners Unsupervised Methods 215 Bruno Ribeiro Understanding Ensembles The

### 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 Pedro Domingos Department of Computer Science and Engineering University of Washington 2012 A Few Useful Things to Know about Machine Learning Machine

### Introduction to Machine Learning

1, DATA11002 Introduction to Machine Learning Lecturer: Teemu Roos TAs: Ville Hyvönen and Janne Leppä-aho Department of Computer Science University of Helsinki (based in part on material by Patrik Hoyer

### CS 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

### Introduction 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)

### Introduction 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

### Lecture 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

### Master 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

### Appliance-specific power usage classification and disaggregation

Appliance-specific power usage classification and disaggregation Srinikaeth Thirugnana Sambandam, Jason Hu, EJ Baik Department of Energy Resources Engineering Department, Stanford Univesrity 367 Panama

### Introduction 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.

### Module 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

### CS 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 human-assisted analysis of data sets. These

### Introduction 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,

### Naive 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

### Machine Learning. Basic Concepts. Joakim Nivre. Machine Learning 1(24)

Machine Learning Basic Concepts Joakim Nivre Uppsala University and Växjö University, Sweden E-mail: nivre@msi.vxu.se Machine Learning 1(24) Machine Learning Idea: Synthesize computer programs by learning

### CS 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: 4:15pm-5:45pm Talk at 3pm: http://www.sam.pitt.edu/arc-

### - Introduzione al Corso - (a.a )

Short Course on Machine Learning for Web Mining - Introduzione al Corso - (a.a. 2009-2010) Roberto Basili (University of Roma, Tor Vergata) 1 Overview MLxWM: Motivations and perspectives A temptative syllabus

### Artificial 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

### Linear 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

### Azure Machine Learning. Designing Iris Multi-Class Classifier

Media Partners Azure Machine Learning Designing Iris Multi-Class Classifier Marcin Szeliga 20 years of experience with SQL Server Trainer & data platform architect Books & articles writer Speaker at numerous