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Lecture 1: Introduction. Machine Learning Queens College. Today. Welcome Overview of Machine Learning Class Mechanics Syllabus Review. My research and background. Speech Analysis of Intonation Segmentation Natural Language Processing Computational Linguistics Evaluation Measures

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lecture 1 introduction
Lecture 1: Introduction

Machine Learning

Queens College

today
Today

Welcome

Overview of Machine Learning

Class Mechanics

Syllabus Review

my research and background
My research and background
  • Speech
    • Analysis of Intonation
    • Segmentation
  • Natural Language Processing
    • Computational Linguistics
  • Evaluation Measures
  • All of this research relies heavily on Machine Learning
slide4
You
  • Why are you taking this class?
  • What is your background and comfort with
    • Calculus
    • Linear Algebra
    • Probability and Statistics
  • What is your programming language of preference?
    • C++, java, or python are preferred
machine learning
Machine Learning

Data

Learning Algorithm

Behavior

Data

Programmer or Expert

Behavior

Automatically identifying patterns in data

Automatically making decisions based on data

Hypothesis:

machine learning in computer science
Machine Learning in Computer Science

Speech/Audio Processing

Planning

Robotics

Natural Language Processing

Locomotion

Machine Learning

Vision/Image Processing

Biomedical/Chemedical

Informatics

Financial Modeling

Human Computer Interaction

Analytics

major tasks
Major Tasks
  • Regression
    • Predict a numerical value from “other information”
  • Classification
    • Predict a categorical value
  • Clustering
    • Identify groups of similar entities
  • Evaluation
feature representations
Feature Representations

Our Focus

Entity in the World

Feature Representation

Machine Learning Algorithm

Feature Extraction

Web Page

User Behavior

Speech or Audio Data

Vision

Wine

People

Etc.

How do we view data?

classification
Classification

OR

Identify which of N classes a data point, x, belongs to.

xis a column vector of features.

target values
Target Values

Goal of Classification

Identify a function y, such that y(x) = t

In supervised approaches, in addition to a data point, x, we will also have access to a target value, t.

regression
Regression

Goal of Classification

Identify a function y, such that y(x) = t

  • Regression is a supervised machine learning task.
    • So a target value, t, is given.
  • Classification: nominal t
  • Regression: continuous t
differences between classification and regression
Differences between Classification and Regression
  • Similar goals: Identify y(x) = t.
  • What are the differences?
    • The form of the function, y (naturally).
    • Evaluation
      • Root Mean Squared Error
      • Absolute Value Error
      • Classification Error
      • Maximum Likelihood
    • Evaluation drives the optimization operation that learns the function, y.
clustering
Clustering
  • Clustering is an unsupervised learning task.
    • There is no target value to shoot for.
  • Identify groups of “similar” data points, that are “dissimilar” from others.
  • Partition the data into groups (clusters) that satisfy these constraints
    • Points in the same cluster should be similar.
    • Points in different clusters should be dissimilar.
mechanisms of machine learning
Mechanisms of Machine Learning
  • Statistical Estimation
    • Numerical Optimization
    • Theoretical Optimization
  • Feature Manipulation
  • Similarity Measures
mathematical necessities
Mathematical Necessities
  • Probability
  • Statistics
  • Calculus
    • Vector Calculus
  • Linear Algebra
  • Is this a Math course in disguise?
why do we need so much math
Why do we need so much math?
  • Probability Density Functions allow the evaluation of how likely a data point is under a model.
    • Want to identify good PDFs. (calculus)
    • Want to evaluate against a known PDF. (algebra)
gaussian distributions
Gaussian Distributions

We use Gaussian Distributions all over the place.

gaussian distributions1
Gaussian Distributions

We use Gaussian Distributions all over the place.

data data data
Data Data Data
  • “There’s no data like more data”
  • All machine learning techniques rely on the availability of data to learn from.
  • There is an ever increasing amount of data being generated, but it’s not always easy to process.
    • UCI
      • http://archive.ics.uci.edu/ml/
    • LDC (Linguistic Data Consortium)
      • http://www.ldc.upenn.edu/
    • Contact me for speech data.
  • Is all data equal?
class structure and policies
Class Structure and Policies
  • Course website:
    • http://eniac.cs.qc.cuny.edu/andrew/ml/syllabus.html
  • Email list
    • CUNY First has an email function – most students do not use the associated email address…
    • Put your email address on the sign up sheet.