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CS189/CS289AIntroduction to Machine Learning
Lecture 1: Overview
Alexei Efros and Peter Bartlett
January 20, 2015
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Organizational Issues
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Organizational Issues
Instructors: Peter Bartlett and Alyosha Efros.
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Organizational Issues
Instructors: Peter Bartlett and Alyosha Efros.
GSIs: Peter Gao, Yun Park, Faraz Tavakoli, Kevin Tee, Pat Virtue,Christopher Xie, Daniel Xu, Yuchen Zhang.
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Organizational Issues
Instructors: Peter Bartlett and Alyosha Efros.
GSIs: Peter Gao, Yun Park, Faraz Tavakoli, Kevin Tee, Pat Virtue,Christopher Xie, Daniel Xu, Yuchen Zhang.
Discussion sections: You choose. If the room is full, please go toanother one. (If necessary, we may offer some specialtysections—watch website for announcements.)
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Organizational Issues
Instructors: Peter Bartlett and Alyosha Efros.
GSIs: Peter Gao, Yun Park, Faraz Tavakoli, Kevin Tee, Pat Virtue,Christopher Xie, Daniel Xu, Yuchen Zhang.
Discussion sections: You choose. If the room is full, please go toanother one. (If necessary, we may offer some specialtysections—watch website for announcements.)
Office hours: see web site.
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Organizational Issues
Instructors: Peter Bartlett and Alyosha Efros.
GSIs: Peter Gao, Yun Park, Faraz Tavakoli, Kevin Tee, Pat Virtue,Christopher Xie, Daniel Xu, Yuchen Zhang.
Discussion sections: You choose. If the room is full, please go toanother one. (If necessary, we may offer some specialtysections—watch website for announcements.)
Office hours: see web site.
http://www-inst.eecs.berkeley.edu/∼cs189
bCourses (+ piazza, kaggle), office hours, syllabus, assignments,readings, lecture slides, announcements.
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Organizational Issues
Assessment:CS189 Homework 40%
Implementation and application of methods. (Kaggle)
Mathematical/reinforcement of concepts.Seven total.Late policy: 5 slip days total. That’s it.
Midterm 20%(Thursday, March 19, in the lecture slot.)Final Exam 40%
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Organizational Issues
Assessment:
CS289A Plus a project:
Homework 40%Midterm 20%Final Exam 20%Final Project 20%(due Friday, May 1. Proposal due Friday, April 3.)
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(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
Ethics:
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
Ethics:Discussion of homework problems with other students is encouraged.
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).Please read the department policy on academic dishonesty. We will be
actively checking for plagiarism.
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Organizational Issues
(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).
No screens in lectures. (To see why, google “laptops in class.”)
Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).Please read the department policy on academic dishonesty. We will be
actively checking for plagiarism.Questions: Use piazza. Public and private.
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T
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Texts
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CS189 I d i M hi L i
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CS189: Introduction to Machine Learning
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CS189 I d i M hi L i
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CS189: Introduction to Machine Learning
Machine Learning
Systems that learn to solveinformation processing problems.
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CS189 I t d ti t M hi L i
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CS189: Introduction to Machine Learning
Machine Learning
Systems that learn to solveinformation processing problems.
LearnUse experience to improve performance:data, queries, interaction, experiments
Statistical issues are central.
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CS189 I t d ti t M hi L i
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CS189: Introduction to Machine Learning
Machine Learning
Systems that learn to solveinformation processing problems.
LearnUse experience to improve performance:data, queries, interaction, experiments
Statistical issues are central.
Systems
Computational issues are also central.
Algorithms, optimization.
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An Overview of Machine Learning
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An Overview of Machine Learning
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An Overview of Machine Learning
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An Overview of Machine Learning
1 Problems
2
3
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An Overview of Machine Learning
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An Overview of Machine Learning
1 Problems
2 Methods3
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An Overview of Machine Learning
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An Overview of Machine Learning
1 Problems
2 Methods3 Concepts
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An Overview of Machine Learning
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An Overview of Machine Learning
1 Problems
2 Methods3 Concepts
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Classification Problems (Homework)
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Classification Problems (Homework)
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Classification Problems (Homework)
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Classification Problems (Homework)
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Classification
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Classification
microsoft.com
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Classification
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Classification
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Classification
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ISLR
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Classification
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ISLR
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Classification
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Regression
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g
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Regression
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Regression
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Regression
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Regression
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Density Estimation
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Density Estimation
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Dimensionality Reduction
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Dimensionality Reduction
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Dimensionality Reduction
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Clustering
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Clustering
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Clustering
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Clustering
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Machine Learning Problems
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Machine Learning Problems
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Machine Learning Problems
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Machine Learning Problems
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Machine Learning Problems
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Classification
Regression
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Machine Learning Problems
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Classification
Regression
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Ranking
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Machine Learning Problems
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Classification
Regression
Density estimation
Dimensionality reduction
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Ranking
Collaborative filtering
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Machine Learning Problems
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Classification
Regression
Density estimation
Dimensionality reduction
Clustering
Ranking
Collaborative filtering
Sequential decisionproblems:
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Machine Learning Problems
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Classification
Regression
Density estimation
Dimensionality reduction
Clustering
Ranking
Collaborative filtering
Sequential decisionproblems:
bandits
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Machine Learning Problems
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Classification
Regression
Density estimation
Dimensionality reduction
Clustering
Ranking
Collaborative filtering
Sequential decisionproblems:
banditscontextual bandits
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Machine Learning Problems
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Classification
RegressionDensity estimation
Dimensionality reduction
Clustering
Ranking
Collaborative filtering
Sequential decisionproblems:
banditscontextual banditsdynamic pricing
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Machine Learning Problems
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Classification
RegressionDensity estimation
Dimensionality reduction
Clustering
Ranking
Collaborative filtering
Sequential decisionproblems:
banditscontextual banditsdynamic pricingreinforcement learning
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An Overview of Machine Learning
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1 Problems
2 Methods
3 Concepts
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Methods
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Linear classifiers: Perceptron
Support vector machines
Gaussian class conditionals
Logistic regression
Naive Bayes
Linear discriminant analysisLinear regression
Decision trees, regression trees
Ensemble methods
Neural networksNearest neighbor
Principal components analysis
k-means clustering
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Methods
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Linear classifiers: Perceptron
Support vector machines
Gaussian class conditionals
Logistic regression
Naive Bayes
Linear discriminant analysisLinear regression
Decision trees, regression trees
Ensemble methods
Neural networksNearest neighbor
Principal components analysis
k-means clustering
1 Classification2 Regression
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Methods
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Linear classifiers: Perceptron
Support vector machines
Gaussian class conditionals
Logistic regression
Naive Bayes
Linear discriminant analysisLinear regression
Decision trees, regression trees
Ensemble methods
Neural networksNearest neighbor
Principal components analysis
k-means clustering
1 Probabilistic
modeling.
2 Prediction; not basedon a model.
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An Overview of Machine Learning
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1 Problems
2 Methods
3 Concepts
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Concepts
P di i b bili i d li
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1 Prediction versus probabilistic modeling.
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Concepts
P di i b bili i d li
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
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Concepts
1 P di ti b bili ti d li
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.
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Concepts
1 Prediction ers s probabilistic modeling
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.
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Concepts
1 Prediction versus probabilistic modeling
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
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Concepts
1 Prediction versus probabilistic modeling
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
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Concepts
1 Prediction versus probabilistic modeling
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
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Concepts
1 Prediction versus probabilistic modeling
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1 Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.
35/37
Concepts
1 Prediction versus probabilistic modeling
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Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
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Concepts
1 Prediction versus probabilistic modeling.
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Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
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Concepts
1 Prediction versus probabilistic modeling.
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Prediction versus probabilistic modeling.2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
Bias-variance/approximation-estimation trade-off.
35/37
Concepts
1 Prediction versus probabilistic modeling.
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p g2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
Bias-variance/approximation-estimation trade-off.Regularization
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Concepts
1 Prediction versus probabilistic modeling.
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p g2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
Bias-variance/approximation-estimation trade-off.Regularization
Priors
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Concepts
1 Prediction versus probabilistic modeling.
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2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
Bias-variance/approximation-estimation trade-off.Regularization
Priors5 Practical issues:
Train/validate/test. Over-fitting.
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Concepts
1 Prediction versus probabilistic modeling.
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2 Probabilistic modeling:
Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.
3 Optimization.
Convexity.
(Stochastic) gradient methods.Newton’s method.
4 Controlling complexity:
Bias-variance/approximation-estimation trade-off.Regularization
Priors5 Practical issues:
Train/validate/test. Over-fitting.Resampling methods.
35/37
Overview (Part I: Bartlett)
Linear classification
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Linear classification
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Overview (Part I: Bartlett)
Linear classification
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Linear classification
Statistical learning background
36/37
Overview (Part I: Bartlett)
Linear classification
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Linear classification
Statistical learning background
Decision theory
36/37
Overview (Part I: Bartlett)
Linear classification
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Linear classification
Statistical learning background
Decision theoryGenerative and discriminative models
36/37
Overview (Part I: Bartlett)
Linear classification
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Linear classification
Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.
36/37
Overview (Part I: Bartlett)
Linear classification
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Linear classification
Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
Linear Classification revisited
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
Linear Classification revisited
Logistic regression
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
Linear Classification revisited
Logistic regression
Linear Discriminant Analysis
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
Linear Classification revisited
Logistic regression
Linear Discriminant AnalysisSupport vector machines
36/37
Overview (Part I: Bartlett)
Linear classification
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Statistical learning background
Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.
Linear regression
Optimization
Linear Classification revisited
Logistic regression
Linear Discriminant AnalysisSupport vector machines
Statistical learning theory
36/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighbor
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high dimensional spaces
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Properties of high-dimensional spaces
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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Properties of high-dimensional spaces
distance learningEfficient indexing and retrieval methods
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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Properties of high dimensional spaces
distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression trees
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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Properties of high dimensional spaces
distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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p g p
distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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p g p
distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting4 Neural networks / Deep Learning
37/37
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Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting4 Neural networks / Deep Learning
Multilayer perceptronsVariations such as convolutional nets
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting4 Neural networks / Deep Learning
Multilayer perceptronsVariations such as convolutional netsExamples and applications
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting4 Neural networks / Deep Learning
Multilayer perceptronsVariations such as convolutional netsExamples and applications
5
Unsupervised methods
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3 Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications
5
Unsupervised methodsClustering
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods
2 Decision treesClassification and regression treesRandom Forests
3
Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications
5
Unsupervised methodsClusteringDensity estimation
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods2 Decision trees
Classification and regression treesRandom Forests
3
Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications
5
Unsupervised methodsClusteringDensity estimationDimensionality reduction
37/37
Overview (Part II: Efros)1 Memory-based/Instance-based learning
k-nearest-neighborProperties of high-dimensional spaces
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distance learningEfficient indexing and retrieval methods2 Decision trees
Classification and regression treesRandom Forests
3
Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications
5
Unsupervised methodsClusteringDensity estimationDimensionality reductionApplications: Collaborative filtering, etc.
37/37