CSL465/603  Machine Learning


 Roland Gardner
 3 years ago
 Views:
Transcription
1 CSL465/603  Machine Learning Fall 2016 Narayanan C Krishnan Introduction CSL465/603  Machine Learning 1
2 Administrative Trivia Course Structure Lecture Timings Monday am Tuesday am Wednesday 11.45am pm Lab hours Monday pm Tuesday pm TA Sanatan Sukhija Second TA  TBD Office Hours Instructor Monday afternoon during the lab hours or by appointment TA Monday and Tuesday lab hours Course google group Preregistered students will be automatically added. Others, please send an by Friday July 29 th. Pseudonym your 5 character key by July 29 th. Else we will assign a random one for you. Introduction CSL465/603  Machine Learning 2
3 Reference Material No fixed textbook. Primary reference books source will be announced Other reference material Copies of reference material is available in the library Introduction CSL465/603  Machine Learning 3
4 Prerequisites Officially CSL201 Data Structures However, we will be using concepts from Probability Statistics Linear Algebra Optimization (operations research) Revision might be helpful Introduction CSL465/603  Machine Learning 4
5 Tentative Course Schedule Introduction CSL465/603  Machine Learning 5
6 Quizzes 30% Almost every Thursday am Room  L3 Covers material discussed from the previous quiz till the current week Duration 3045m Top 6 out of 8 will be considered towards the final grade. Additional quizzes will not be conducted. Quiz Date Q1 4/8 Q2 11/8 Q3 25/8 Q4 1/9 Q5 6/10 Q6 13/10 Q7 27/10 Q8 3/11 Introduction CSL465/603  Machine Learning 6
7 Labs 30% Due every third Friday 11.55pm Programming assignments Start early, experiments will take time to run!!! Individual labs TA is available for any assistance Students are encouraged to contact the TA for clarifications regarding the labs Labs Date L1 19/8 L2 9/9 L3 30/9 L4 21/10 L5 11/11 Introduction CSL465/603  Machine Learning 7
8 Project 10%  Tentative If project is included, contribution to the overall grade from quizzes will reduce to 20% Will be decided after the add and drop period is over. Teams of 2 students. Introduction CSL465/603  Machine Learning 8
9 Grading Scheme Tentative Breakup Quizzes (6 out of 8) 2030% Labs (5) 30% Midsemester exam 20% Endsemester exam  20% Attendance Bonus 1% Attendance is not mandatory, however attendance will be taken for every class and will count towards the bonus points. Passing criteria A student must secure an overall score of 40(out of 100) and a combined exam score of 60 (out of 200) to pass the course. Introduction CSL465/603  Machine Learning 9
10 Honor Code Unless explicitly stated otherwise, for all labs Strictly individual effort Group discussions at a high level are encouraged You are forbidden from trawling the web for answers/code etc. Any infraction will be dealt with the severest terms allowed. I reserve the right to question you with regards to your submission, if I suspect any misconduct. Introduction CSL465/603  Machine Learning 10
11 Course Website 3.html All class related material will be accessible from the webpage Labs will be uploaded incrementally and will be notified through Lab submission is only on moodle No separate handouts, encourage you to take notes during the class. PDF version of lecture slides will be available on the class website. Introduction CSL465/603  Machine Learning 11
12 What is Machine Learning? Herbert Simon (1970) Any process by which a system improves its performance Tim Mitchell (1990) A computer program that improves its performance at some task through experience Wikipedia Deals with the construction and study of systems that can learn from data, rather than follow only explicity programmed instructions Introduction CSL465/603  Machine Learning 12
13 Why study machine learning? Artificial Intelligence design and analysis of intelligent agents For an agent to exhibit intelligent behavior requires knowledge Explicitly specifying knowledge needed for specific tasks is hard, and often infeasible Learning an automated way to acquire knowledge. Introduction CSL465/603  Machine Learning 13
14 Why study machine learning? Introduction CSL465/603  Machine Learning 14
15 Related Disciplines Probability and Statistics Applied Mathematics Operations Research Pattern Recognition Artificial Intelligence Data Mining Cognitive Science Neuroscience Big Data Introduction CSL465/603  Machine Learning 15
16 General Architecture Pedro Domingos Hundreds (if not thousands) of machine learning algorithms Generic architecture has three components Representation How would you like to characterize what is being learned? Evaluation How would you like to measure the goodness of what is being learned Optimization Given the evaluation and characterization, find the optimum representation. Introduction CSL465/603  Machine Learning 16
17 General Architecture  Representation Decision Trees Instances Bayes Networks Neural Networks Support Vector Machines Ensembles Gaussian Clusters Introduction CSL465/603  Machine Learning 17
18 General Architecture  Evaluation Accuracy Precision and recall Sum of Squared Error Likelihood Posterior Probability Margin KL Divergence Entropy Introduction CSL465/603  Machine Learning 18
19 General Architecture Optimization Combinatorial optimization Greedy search Convex optimization Gradient descent Constrained optimization Linear programming Introduction CSL465/603  Machine Learning 19
20 Learning Paradigms and Applications 1. Introduction Supervised Learning Classification LeCun et. al., IEEE prostate specific antigen (PSA) and a number of clinical measures, in 97 men who were about to receive a radical prostatectomy. The goal is to predict the log of PSA (lpsa) from a number of measurements including log cancer volume (lcavol), log prostate weight lweight, age, log of benign prostatic hyperplasia amount lbph, seminal vesicle invasion svi, log of capsular penetration lcp, Gleason score gleason, and percent of Gleason scores 4 or 5 pgg45. Figure 1.1 is a scatterplot matrix of the variables. Some correlations with lpsa are evident, but a good predictive model is diﬃcult to construct by eye. This is a supervised learning problem, known as a regression problem, because the outcome measurement is quantitative. Example 3: Handwritten Digit Recognition Introduction The data from this example come from the handwritten ZIP codes on envelopes from U.S. postal mail. Each image is a segment from a five digit ZIP code, isolating a single digit. The images are eightbit grayscale maps, with each pixel ranging in intensity from 0 to 255. Some sample images are shown in Figure 1.2. The images have been normalized to have approximately the same size and orientation. The task is to predict, from the matrix of pixel intensities, the identity of each image (0, 1,..., 9) quickly and accurately. If it is accurate enough, the resulting algorithm would be used as part of an automatic sorting procedure for envelopes. This is a classification problem for which the error rate needs to be kept very low to avoid misdirection of Krizhevsky et. al., nips 2012 FIGURE 1.2. Examples of handwritten digits from U.S. postal envelopes. 20 the Figure 4: (Left) Eight ILSVRC2010 test images and CSL465/603  Machine Learning
21 Learning Paradigms and Applications Supervised Learning Classification Regression Introduction CSL465/603  Machine Learning 21
22 Learning Paradigms and Applications Supervised Learning Classification Regression Unsupervised Learning Clustering Wiwie et.al., nature 2015 Introduction CSL465/603  Machine Learning 22
23 Learning Paradigms and Applications Supervised Learning Classification Regression Unsupervised Learning Clustering Rule Mining Introduction CSL465/603  Machine Learning 23
24 Learning Paradigms and Applications Supervised Learning Classification Regression Unsupervised Learning Clustering Rule Mining Semisupervised Learning Shah et.al., bioinformatics 2015 Introduction CSL465/603  Machine Learning 24
25 Reminder If you have decided to credit this course and have not preregistered Send me an at the earliest to add you to the google group. PG(MS, M.Tech, and PhD) students who are crediting the course, please meet me after today s class. There is no audit option in the course You can credit the course, or just attend the lectures If you have preregistered and have decided to drop the course Please do so at the earliest, as it will help us organize the course and the TAs. Introduction CSL465/603  Machine Learning 25
26 Learning Paradigms and Applications Supervised Learning Classification Regression Unsupervised Learning Clustering Rule Mining Semisupervised Learning Dimensionality Reduction Tenenbaum et.al., science 2000 Introduction CSL465/603  Machine Learning 26
27 Learning Paradigms and Applications Supervised Learning Classification Regression Unsupervised Learning Clustering Rule Mining Semisupervised Learning Dimensionality Reduction Reinforcement Learning Kormushev et.al., robotics 2013 Introduction CSL465/603  Machine Learning 27
28 Other Learning Paradigms Transfer Learning Transfer of knowledge between multiple domains Active Learning Learning algorithm interactively queries an oracle to obtain the desired outputs for new data points Online Learning Learning on the fly Zero shot learning Representation Learning Automatically learning the representation from raw data Deep Learning Introduction CSL465/603  Machine Learning 28
29 Topics to be covered in this course* Supervised Learning Decision trees, Naïve Bayes classifier, Instance based learning (knn), Linear and Logistic regression, Artificial neural networks, Kernel methods, Ensembles. Unsupervised Learning Clustering Dimensionality reduction Temporal models Hidden Markov model Design and Analysis of Experiments *Tentative Introduction CSL465/603  Machine Learning 29
30 Machine Learning in Practice Understanding the domain, prior knowledge, and goals Data collection, integration, selection, cleaning, preprocessing, Learning models Interpreting results Consolidating and delpoying discovered knowledge Loop... Pedro Domingos Introduction CSL465/603  Machine Learning 30
31 Machine Learning Challenges Curse of Dimensionality Intuition fails in high dimensional spaces Overfitting Things look rosy while training, but fail miserably when testing Sample size (number of examples) Often obtaining good examples is a hard, cumbersome, and errorprone process What algorithm to choose? No clear answer on what approach to select from the different options. Too many knobs (hyperparameters) to turn Carefully conducted experiments that search through the hyperparameter space for the optimal setting Introduction CSL465/603  Machine Learning 31
32 Machine Learning Resources Data Repositories UCI ML repository Challenges Kaggle, KDD cup, Software Weka (Java) R (~ Python) Machine learning open source software (mloss.org/software) LibSVM Conferences and Journals ICDM, ICML, KDD, IJCAI, AAAI, UAI, AISTATS, COLT,... ACM TKDD, IEEE TKDE, JMLR, MLJ,... Introduction CSL465/603  Machine Learning 32
33 Supervised Learning Supervised Learning CSL465/603  Machine Learning 33
34 Supervised Learning Given a set of training examples x, f x = y, for some unknown function f Estimate a good approximation to f Example applications Face recognition x: raw intensity face image f(x): name of the person. Loan approval x: properties of a customer (like age, income, liability, job, ) f(x): loan approved or not. Autonomous Steering x: image of the road ahead f(x): Degrees to turn the steering wheel. Introduction CSL465/603  Machine Learning 34
35 Example: Family Car Learning Task Learn to classify cars into one of two classes family car or otherwise Representation Each car is represented by two features (attributes) engine power and price Training set Several training examples of already classified cars Goal Learn a classifier that accurately classified (new unseen) cars Supervised Learning CSL465/603  Machine Learning 35
36 Example: Cars x 2 : Engine power x 2 t x 1 t x 1 : Price Introduction CSL465/603  Machine Learning 36
37 Definitions (1) Feature (attribute): x ) A property of the object to be classified Discrete or continuous E.g., engine power, price Instance: x = [x,, x ,, x / ] The feature values for a specific object E.g., engine power = 100, price = high Instance space: I Space of all possible instances Class: Y Categorical feature of an object Set of instances of objects in this category E.g., family car Introduction CSL465/603  Machine Learning 37
38 Example: Family Car : Engine power x 2 e 2 C e 1 p 1 p 2 x 1 : Price Introduction CSL465/603  Machine Learning 38
39 Definitions (2) Example: (x, y) Instance along with its class membership Positive example: member of class (y = 1) Negative example: not a member of class (y = 0) Training set: X = {x 7, y 7 }, 1 t N Set of N examples Target concept (C) Correct expression of class E.g., (e 1 engine power e 2 ) and (p 1 price p 2 ) Concept class Space of all possible target concepts E.g., axisaligned rectangles in instance space E.g., power set of instance space Introduction CSL465/603  Machine Learning 39
40 Definitions (3) Hypothesis: h x {0,1} Approximation to target concept Hypothesis class: H Space of all possible hypotheses E.g., axisaligned rectangles E.g., axisaligned ellipses Learning goal Find hypothesis h H that closely approximates target concept C h is the output classifier Target concept may not be in H Introduction CSL465/603  Machine Learning 40
41 Example: Hypothesis Error Introduction CSL465/603  Machine Learning 41
42 Definitions (4) Empirical error How well h classifies training set X D E h X = 1 N B 1 h x 7 y 7 EF, Generalization error How well h classifies instances not in X True error How well h classifies entire instance space E h = 1 I B 1 h x 7 y 7 I J Most specific hypothesis  S Consistent hypothesis covering fewest instances Most general hypothesis  G Consistent hypothesis covering most instances Version space All hypothesis between S and G Introduction CSL465/603  Machine Learning 42
43 Example: Version Space : Engine power x 2 G S C x 1 : Price Introduction CSL465/603  Machine Learning 43
44 Thinking of Supervised Learning Learning is the removal of our remaining uncertainty Suppose we know that the concept is a rectangle, we can use the training data to infer the correct rectangle. In general Model (hypothesis): h x θ Loss function: E θ X = L y 7, h x 7 θ E Optimization procedure: θ = argmin W E θ X Introduction CSL465/603  Machine Learning 44
45 Learning under noisy conditions Sources for noise Incorrect feature values Incorrect class labels Hidden or latent features (missing) Impact Overfitting trying too hard to fit the hypothesis h to the noisy data. Introduction CSL465/603  Machine Learning 45
46 Underfitting vs Overfitting x 2 h 2 h 1 Introduction CSL465/603  Machine Learning 46 x 1
47 Bias vs Variance Low Variance High Variance High Bias Low Bias Domingos, cacm 2012 Introduction CSL465/603  Machine Learning 47
48 Characterization of Hypothesis Space Is the hypothesis deterministic or stochastic? Deterministic  Training example is either consistent (correctly predicted) or inconsistent (incorrectly predicted) Stochastic Training example is more or less likely (probabilistic output) Parametrization discrete or continuous? (or mixed) Discrete space perform combinatorial search Continuous space perform numerical search Introduction CSL465/603  Machine Learning 48
49 Framework for Learning Algorithms Pedro Domingos Search procedure Direct computation solve for hypothesis directly Local search start with an initial hypothesis, make small improvements until a local optimum Timing Eager Analyze training data and construct an explicit hypothesis Online analyze each training example as it is presented Batch collect training examples and analyze them together Lazy Store the training data and wait until a test data point is presented to construct the hypothesis Introduction CSL465/603  Machine Learning 49
Lecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
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 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 information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationLecture 1: Basic Concepts of Machine Learning
Lecture 1: Basic Concepts of Machine Learning Cognitive Systems  Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010
More informationCS Machine Learning
CS 478  Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationActive Learning. Yingyu Liang Computer Sciences 760 Fall
Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,
More informationCourse Outline. Course Grading. Where to go for help. Academic Integrity. EE589 Introduction to Neural Networks NN 1 EE
EE589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:0012:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers
More informationRule Learning with Negation: Issues Regarding Effectiveness
Rule Learning with Negation: Issues Regarding Effectiveness Stephanie Chua, Frans Coenen, and Grant Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tuchemnitz.de Ricardo BaezaYates Center
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationSoftprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA Email: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
More informationGenerative models and adversarial training
Day 4 Lecture 1 Generative models and adversarial training Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University What is a generative model?
More informationLahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017
Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics
More informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationExploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
More informationFoothill College Summer 2016
Foothill College Summer 2016 Intermediate Algebra Math 105.04W CRN# 10135 5.0 units Instructor: Yvette Butterworth Text: None; Beoga.net material used Hours: Online Except Final Thurs, 8/4 3:30pm Phone:
More informationIterative CrossTraining: An Algorithm for Learning from Unlabeled Web Pages
Iterative CrossTraining: An Algorithm for Learning from Unlabeled Web Pages Nuanwan Soonthornphisaj 1 and Boonserm Kijsirikul 2 Machine Intelligence and Knowledge Discovery Laboratory Department of Computer
More informationWelcome to. ECML/PKDD 2004 Community meeting
Welcome to ECML/PKDD 2004 Community meeting A brief report from the program chairs JeanFrancois Boulicaut, INSALyon, France Floriana Esposito, University of Bari, Italy Fosca Giannotti, ISTICNR, Pisa,
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIANLEARNING BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIANLEARNING BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationThe 9 th International Scientific Conference elearning and software for Education Bucharest, April 2526, / X
The 9 th International Scientific Conference elearning and software for Education Bucharest, April 2526, 2013 10.12753/2066026X13154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,
More informationSemiSupervised Face Detection
SemiSupervised Face Detection Nicu Sebe, Ira Cohen 2, Thomas S. Huang 3, Theo Gevers Faculty of Science, University of Amsterdam, The Netherlands 2 HP Research Labs, USA 3 Beckman Institute, University
More informationUnsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model Xinying Song, Xiaodong He, Jianfeng Gao, Li Deng Microsoft Research, One Microsoft Way, Redmond, WA 98052, U.S.A.
More informationSystem Implementation for SemEval2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks
System Implementation for SemEval2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 TzuHsuan Yang, 2 TzuHsuan Tseng, and 3 ChiaPing Chen Department of Computer Science and Engineering
More informationACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014
UNSW Australia Business School School of Risk and Actuarial Studies ACTL5103 Stochastic Modelling For Actuaries Course Outline Semester 2, 2014 Part A: CourseSpecific Information Please consult Part B
More informationQuickStroke: An Incremental Online Chinese Handwriting Recognition System
QuickStroke: An Incremental Online Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents
More informationAgents and environments. Intelligent Agents. Reminders. Vacuumcleaner world. Outline. A vacuumcleaner agent. Chapter 2 Actuators
s and environments Percepts Intelligent s? Chapter 2 Actions s include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: f : P A The agent program runs
More informationarxiv: v1 [cs.lg] 15 Jun 2015
Dual Memory Architectures for Fast Deep Learning of Stream Data via an OnlineIncrementalTransfer Strategy arxiv:1506.04477v1 [cs.lg] 15 Jun 2015 SangWoo Lee MinOh Heo School of Computer Science and
More informationA CaseBased Approach To Imitation Learning in Robotic Agents
A CaseBased Approach To Imitation Learning in Robotic Agents Tesca Fitzgerald, Ashok Goel School of Interactive Computing Georgia Institute of Technology, Atlanta, GA 30332, USA {tesca.fitzgerald,goel}@cc.gatech.edu
More informationDeep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach
#BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying
More informationMGT/MGP/MGB 261: Investment Analysis
UNIVERSITY OF CALIFORNIA, DAVIS GRADUATE SCHOOL OF MANAGEMENT SYLLABUS for Fall 2014 MGT/MGP/MGB 261: Investment Analysis Daytime MBA: Tu 12:00p.m.  3:00 p.m. Location: 1302 Gallagher (CRN: 51489) Sacramento
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationState University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 11:50 NSC 210
1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 11:50 NSC 210 Dr. Michelle Benson mbenson2@buffalo.edu Office: 513 Park Hall Office Hours: Mon & Fri 10:3012:30
More informationSwitchboard Language Model Improvement with Conversational Data from Gigaword
Katholieke Universiteit Leuven Faculty of Engineering Master in Artificial Intelligence (MAI) Speech and Language Technology (SLT) Switchboard Language Model Improvement with Conversational Data from Gigaword
More informationExperiment Databases: Towards an Improved Experimental Methodology in Machine Learning
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning Hendrik Blockeel and Joaquin Vanschoren Computer Science Dept., K.U.Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium
More informationWord Segmentation of Offline Handwritten Documents
Word Segmentation of Offline Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationUsing Web Searches on Important Words to Create Background Sets for LSI Classification
Using Web Searches on Important Words to Create Background Sets for LSI Classification Sarah Zelikovitz and Marina Kogan College of Staten Island of CUNY 2800 Victory Blvd Staten Island, NY 11314 Abstract
More informationLearning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models
Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za
More informationIntroduction. Chem 110: Chemical Principles 1 Sections 4052
Introduction Chem 110: Chemical Principles 1 Sections 4052 Instructor: Dr. Squire J. Booker 302 Chemistry Building 8148658793 squire@psu.edu (sjb14@psu.edu) Lectures: Monday (M), Wednesday (W), Friday
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 20082009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms GeneticsBased Machine Learning
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationCalibration of Confidence Measures in Speech Recognition
Submitted to IEEE Trans on Audio, Speech, and Language, July 2010 1 Calibration of Confidence Measures in Speech Recognition Dong Yu, Senior Member, IEEE, Jinyu Li, Member, IEEE, Li Deng, Fellow, IEEE
More informationIntelligent Agents. Chapter 2. Chapter 2 1
Intelligent Agents Chapter 2 Chapter 2 1 Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types The structure of agents Chapter 2 2 Agents
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II  Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP2016 October 1112 Natalia Tomashenko 1,2,3 natalia.tomashenko@univlemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationPHY2048 Syllabus  Physics with Calculus 1 Fall 2014
PHY2048 Syllabus  Physics with Calculus 1 Fall 2014 Course WEBsites: There are three PHY2048 WEBsites that you will need to use. (1) The Physics Department PHY2048 WEBsite at http://www.phys.ufl.edu/courses/phy2048/fall14/
More informationProbability and Statistics Curriculum Pacing Guide
Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods
More informationConstructive Inductionbased Learning Agents: An Architecture and Preliminary Experiments
Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS95) Ibrahim F. Imam and Janusz Wnek (Eds.), pp. 3851, Melbourne Beach, Florida, 1995. Constructive Inductionbased
More informationExperiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling
Experiments with SMS Translation and Stochastic Gradient Descent in Spanish Text Author Profiling Notebook for PAN at CLEF 2013 Andrés Alfonso Caurcel Díaz 1 and José María Gómez Hidalgo 2 1 Universidad
More informationTruth Inference in Crowdsourcing: Is the Problem Solved?
Truth Inference in Crowdsourcing: Is the Problem Solved? Yudian Zheng, Guoliang Li #, Yuanbing Li #, Caihua Shan, Reynold Cheng # Department of Computer Science, Tsinghua University Department of Computer
More informationAxiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
More informationOCR for Arabic using SIFT Descriptors With Online Failure Prediction
OCR for Arabic using SIFT Descriptors With Online Failure Prediction Andrey Stolyarenko, Nachum Dershowitz The Blavatnik School of Computer Science Tel Aviv University Tel Aviv, Israel Email: stloyare@tau.ac.il,
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationModel Ensemble for Click Prediction in Bing Search Ads
Model Ensemble for Click Prediction in Bing Search Ads Xiaoliang Ling Microsoft Bing xiaoling@microsoft.com Hucheng Zhou Microsoft Research huzho@microsoft.com Weiwei Deng Microsoft Bing dedeng@microsoft.com
More informationNotes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1
Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial
More informationOffice Hours: Mon & Fri 10:0012:00. Course Description
1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 4 credits (3 credits lecture, 1 credit lab) Fall 2016 M/W/F 1:001:50 O Brian 112 Lecture Dr. Michelle Benson mbenson2@buffalo.edu
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 20082009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms GeneticsBased Machine Learning
More informationADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF
Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSRJCE) eissn: 22780661,pISSN: 22788727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationMining Association Rules in Student s Assessment Data
www.ijcsi.org 211 Mining Association Rules in Student s Assessment Data Dr. Varun Kumar 1, Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama
More informationEvolutive Neural Net Fuzzy Filtering: Basic Description
Journal of Intelligent Learning Systems and Applications, 2010, 2: 1218 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:
More informationHuman Emotion Recognition From Speech
RESEARCH ARTICLE OPEN ACCESS Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 0014
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationIndian Institute of Technology, Kanpur
Indian Institute of Technology, Kanpur Course Project  CS671A POS Tagging of Code Mixed Text Ayushman Sisodiya (12188) {ayushmn@iitk.ac.in} Donthu Vamsi Krishna (15111016) {vamsi@iitk.ac.in} Sandeep Kumar
More informationSTA 225: Introductory Statistics (CT)
Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic
More informationBusiness Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence
Business Analytics and Information Tech COURSE NUMBER: 33:136:494 COURSE TITLE: Data Mining and Business Intelligence COURSE DESCRIPTION This course presents computing tools and concepts for all stages
More informationCS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University
CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9
More informationCS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus
CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts
More informationTHE UNIVERSITY OF SYDNEY Semester 2, Information Sheet for MATH2068/2988 Number Theory and Cryptography
THE UNIVERSITY OF SYDNEY Semester 2, 2017 Information Sheet for MATH2068/2988 Number Theory and Cryptography Websites: It is important that you check the following webpages regularly. Intermediate Mathematics
More informationSyllabus for CHEM 4660 Introduction to Computational Chemistry Spring 2010
Instructor: Dr. Angela Syllabus for CHEM 4660 Introduction to Computational Chemistry Office Hours: Mondays, 1:00 p.m. 3:00 p.m.; 5:00 6:00 p.m. Office: Chemistry 205C Office Phone: (940) 5654296 Email:
More informationAGN 331 Soil Science Lecture & Laboratory Face to Face Version, Spring, 2012 Syllabus
AGN 331 Soil Science Lecture & Laboratory Face to Face Version, Spring, 2012 Syllabus Contact Information: J. Leon Young Office number: 9364684544 Soil Plant Analysis Lab: 9364684500 Agriculture Department,
More informationFirms and Markets Saturdays Summer I 2014
PRELIMINARY DRAFT VERSION. SUBJECT TO CHANGE. Firms and Markets Saturdays Summer I 2014 Professor Thomas Pugel Office: Room 1153 KMC Email: tpugel@stern.nyu.edu Tel: 2129980918 Fax: 2129954212 This
More informationWHEN THERE IS A mismatch between the acoustic
808 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 14, NO. 3, MAY 2006 Optimization of Temporal Filters for Constructing Robust Features in Speech Recognition JeihWeih Hung, Member,
More informationChapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA. 1. Introduction. Alta de Waal, Jacobus Venter and Etienne Barnard
Chapter 10 APPLYING TOPIC MODELING TO FORENSIC DATA Alta de Waal, Jacobus Venter and Etienne Barnard Abstract Most actionable evidence is identified during the analysis phase of digital forensic investigations.
More informationChallenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley
Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling
More informationMath 96: Intermediate Algebra in Context
: Intermediate Algebra in Context Syllabus Spring Quarter 2016 Daily, 9:20 10:30am Instructor: Lauri Lindberg Office Hours@ tutoring: Tutoring Center (CAS504) 8 9am & 1 2pm daily STEM (Math) Center (RAI338)
More informationData Structures and Algorithms
CS 3114 Data Structures and Algorithms 1 Trinity College Library Univ. of Dublin Instructor and Course Information 2 William D McQuain Email: Office: Office Hours: wmcquain@cs.vt.edu 634 McBryde Hall see
More informationUsing focal point learning to improve human machine tacit coordination
DOI 10.1007/s1045801091265 Using focal point learning to improve human machine tacit coordination InonZuckerman SaritKraus Jeffrey S. Rosenschein The Author(s) 2010 Abstract We consider an automated
More informationAustralian Journal of Basic and Applied Sciences
AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:19918178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy CMean
More informationMath 150 Syllabus Course title and number MATH 150 Term Fall 2017 Class time and location INSTRUCTOR INFORMATION Name Erin K. Fry Phone number Department of Mathematics: 8453261 email address erinfry@tamu.edu
More informationAlgebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview
Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best
More informationEECS 571 PRINCIPLES OF REALTIME COMPUTING Fall 10. Instructor: Kang G. Shin, 4605 CSE, ;
EECS 571 PRINCIPLES OF REALTIME COMPUTING Fall 10 Instructor: Kang G. Shin, 4605 CSE, 7630391; kgshin@umich.edu Number of credit hours: 4 Class meeting time and room: Regular classes: MW 10:30am noon
More informationTop US Tech Talent for the Top China Tech Company
THE FALL 2017 US RECRUITING TOUR Top US Tech Talent for the Top China Tech Company INTERVIEWS IN 7 CITIES Tour Schedule CITY Boston, MA New York, NY Pittsburgh, PA UrbanaChampaign, IL Ann Arbor, MI Los
More informationCS 100: Principles of Computing
CS 100: Principles of Computing Kevin Molloy August 29, 2017 1 Basic Course Information 1.1 Prerequisites: None 1.2 General Education Fulfills Mason Core requirement in Information Technology (ALL). 1.3
More informationMathematics Scoring Guide for Sample Test 2005
Mathematics Scoring Guide for Sample Test 2005 Grade 4 Contents Strand and Performance Indicator Map with Answer Key...................... 2 Holistic Rubrics.......................................................
More informationCHAPTER 4: REIMBURSEMENT STRATEGIES 24
CHAPTER 4: REIMBURSEMENT STRATEGIES 24 INTRODUCTION Once state level policymakers have decided to implement and pay for CSR, one issue they face is simply how to calculate the reimbursements to districts
More informationAnalysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier
IOSR Journal of Electronics and Communication Engineering (IOSRJECE) eissn: 22782834,p ISSN: 22788735.Volume 10, Issue 2, Ver.1 (Mar  Apr.2015), PP 5561 www.iosrjournals.org Analysis of Emotion
More informationSyllabus ENGR 190 Introductory Calculus (QR)
Syllabus ENGR 190 Introductory Calculus (QR) Catalog Data: ENGR 190 Introductory Calculus (4 credit hours). Note: This course may not be used for credit toward the J.B. Speed School of Engineering B. S.
More informationMachine Learning from Garden Path Sentences: The Application of Computational Linguistics
Machine Learning from Garden Path Sentences: The Application of Computational Linguistics http://dx.doi.org/10.3991/ijet.v9i6.4109 J.L. Du 1, P.F. Yu 1 and M.L. Li 2 1 Guangdong University of Foreign Studies,
More informationSyllabus  ESET 369 Embedded Systems Software, Fall 2016
Syllabus  ESET 369 Embedded Systems Software, Fall 2016 Contact Information: Professor: Dr. Byul Hur Office: 008A Fermier Telephone: (979) 8455195 Facsimile: Email: byulmail@tamu.edu Web: www.tamuresearch.com
More informationCOMPUTERASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS
COMPUTERASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)
More informationApplications of data mining algorithms to analysis of medical data
Master Thesis Software Engineering Thesis no: MSE2007:20 August 2007 Applications of data mining algorithms to analysis of medical data Dariusz Matyja School of Engineering Blekinge Institute of Technology
More informationSemisupervised methods of text processing, and an application to medical concept extraction. Yacine Jernite TextasData series September 17.
Semisupervised methods of text processing, and an application to medical concept extraction Yacine Jernite TextasData series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More information