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CMU CS 10701 - midsemester-feedback

School name Carnegie Mellon University

Course Cs 10701- Introduction to Machine Learning

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Mid-semester feedback for 10701 – Machine Learning – Spring 06Your feedback is valuable and will be kept anonymous. Please tell us what you think!Demographics:Program: Year:Feedback about content:What do you think about the content of the class? Should we add or remove anything?Instructor:Please provide feedback about lecturing and interactions with instructorTAs:Please provide feedback about recitations and interactions with each TA(Andreas, Anton, Jure, Stano)Homeworks:How long on average do you spend on each assignment?Do you feel the assignments so far have helped you learn the material?Any other feedback about assignments:Overall:How do you feel about the class overall? What do you like? What can we improve?Do you have any other feedback to give

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CMU CS 10701 - midsemester-feedback

School: Carnegie Mellon University

Course: Cs 10701- Introduction to Machine Learning

Pages: 2

Documents in this Course

lecture

12 pages

Introduction to Regression

Introduction to Regression

16 pages

lecture

17 pages

Predicting Real-valued outputs

Predicting Real-valued outputs

51 pages

Bayesian Networks (Structure) Learning

Bayesian Networks (Structure) Learning

11 pages

HMMs

40 pages

Bayesian Networks – Representation

Bayesian Networks – Representation

46 pages

lecture

15 pages

lecture

20 pages

Machine Learning, Function Approximation and Version Spaces

Machine Learning, Function Approximation and Version Spaces

33 pages

HMM MDP RL

30 pages

Notes

10 pages

EM for HMMs a.k.a. The Baum-Welch Algorithm

EM for HMMs a.k.a. The Baum-Welch Algorithm

28 pages

Bayesian Networks: Independencies and Inference

Bayesian Networks: Independencies and Inference

22 pages

Notes

15 pages

Co-Training for Semi- supervised learning (cont.)

Co-Training for Semi- supervised learning (cont.)

51 pages

Neural Networks

Neural Networks

58 pages

Markov Decision Processes (MDPs)

Markov Decision Processes (MDPs)

51 pages

Bayesian Networks – Inference

Bayesian Networks – Inference

41 pages

Graphical Models and Bayesian Networks

Graphical Models and Bayesian Networks

11 pages

Support Vector Machines

Support Vector Machines

20 pages

Gaussian Naïve Bayes

Gaussian Naïve Bayes

16 pages

Lecture

22 pages

EM for HMMs a.k.a. The Baum-Welch Algorithm

EM for HMMs a.k.a. The Baum-Welch Algorithm

35 pages

Logistic Regression, cont. Decision Trees

Logistic Regression, cont. Decision Trees

32 pages

Machine Learning, Decision Trees, Overfitting

Machine Learning, Decision Trees, Overfitting

31 pages

Data likelihood for BNs

Data likelihood for BNs

42 pages

Lecture

13 pages

mmmn-comments

45 pages

decisiontrees

51 pages

learning-theory-mid-review

learning-theory-mid-review

59 pages

MLE_MAP_1-18-11-ann

MLE_MAP_1-18-11-ann

20 pages

Lecture

24 pages

lecture24-annotated

lecture24-annotated

21 pages

NNets-9-28-06

25 pages

Lecture9

38 pages

lecture2-annotated

lecture2-annotated

20 pages

lecture

26 pages

lecture

13 pages

Neural Networks

Neural Networks

44 pages

Support Vector Machines

Support Vector Machines

33 pages

Semi-Supervised Learning and Text Analysis

Semi-Supervised Learning and Text Analysis

53 pages

EM for Bayes Nets

EM for Bayes Nets

36 pages

Lecture

5 pages

lecture-annotated

lecture-annotated

26 pages

Neural Networks

Neural Networks

18 pages

lecture

18 pages

SVM as a Convex Optimization Problem

SVM as a Convex Optimization Problem

10 pages

Neural Networks

Neural Networks

11 pages

lecture

22 pages

EM Algorithm

51 pages

Bayesian Networks – Inference (continued) Learning

Bayesian Networks – Inference (continued) Learning

36 pages

Recitation

14 pages

Bayes Nets Representation: joint distribution and conditional independence

Bayes Nets Representation: joint distribution and conditional independence

16 pages

lecture13-annotated

lecture13-annotated

24 pages

Instance-based Learning

Instance-based Learning

20 pages

Time series, HMMs, Kalman Filters

Time series, HMMs, Kalman Filters

30 pages

Boosting

11 pages

Unsupervised Learning Clustering K-means

Unsupervised Learning Clustering K-means

23 pages

lecture

16 pages

Markov Decision Processes (MDPs)

Markov Decision Processes (MDPs)

48 pages

lecture

20 pages

Recitation

62 pages

PAC-learning, VC Dimension and Margin- based Bounds (cont.)

PAC-learning, VC Dimension and Margin- based Bounds (cont.)

39 pages

An introduction to graphical models

An introduction to graphical models

19 pages

Lecture

20 pages

Simple Model Selection Cross Validation Regularization

Simple Model Selection Cross Validation Regularization

15 pages

Linear Regression

Linear Regression

30 pages

Matrix MLE for Linear Regression

Matrix MLE for Linear Regression

4 pages

Lecture

39 pages

Feature Selection for fMRI Classification

Feature Selection for fMRI Classification

8 pages

Inference in Bayesian Networks

Inference in Bayesian Networks

31 pages

HMM and (may be) CRF

HMM and (may be) CRF

40 pages

Lecture

14 pages

Support vector machines

Support vector machines

19 pages

NONPARAMETRIC DENSITY ESTIMATION

NONPARAMETRIC DENSITY ESTIMATION

23 pages

Lecture

18 pages

Reinforcement Learning

Reinforcement Learning

37 pages

SVMs, Duality and the Kernel Trick (cont.)

SVMs, Duality and the Kernel Trick (cont.)

59 pages

Computational biology: Sequence alignment and profile HMMs

42 pages

Bayesian point estimation Gaussians Linear Regression Bias-Variance Tradeoff

Bayesian point estimation Gaussians Linear Regression Bias-Variance Tradeoff

20 pages

Lecture

13 pages

Graphical Models meet Margin- based Learning

Graphical Models meet Margin- based Learning

45 pages

Exam

10 pages

Decision Trees

26 pages

Unsupervised Learning Clustering K-means

Unsupervised Learning Clustering K-means

22 pages

Dimensionality reduction (cont.)

Dimensionality reduction (cont.)

58 pages

Recitation

17 pages

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

62 pages

Lecture

27 pages

Expectation Maximization

Expectation Maximization

50 pages

Lecture

15 pages

What’s learning, revisited Overfitting Bayes optimal classifier Naïve Bayes

What’s learning, revisited Overfitting Bayes optimal classifier Naïve Bayes

43 pages

Support Vector Machines

Support Vector Machines

48 pages

Markov Decision Processes (MDPs)

Markov Decision Processes (MDPs)

26 pages

Lecture

24 pages

Lecture

16 pages

Instance-based Learning

Instance-based Learning

40 pages

Bayesian Networks – Representation (cont.) Inference

Bayesian Networks – Representation (cont.) Inference

50 pages

Lecture

23 pages

lecture17-annotated

lecture17-annotated

20 pages

lecture26-annotated

lecture26-annotated

26 pages

Lecture6

28 pages

Hypothesis testing

Hypothesis testing

8 pages

What’s learning? Point Estimation

What’s learning? Point Estimation

34 pages

What’s learning? Point Estimation

What’s learning? Point Estimation

22 pages

Bayesian Networks – Representation

Bayesian Networks – Representation

31 pages

Simple Model Selection Cross Validation Regularization Neural Networks

Simple Model Selection Cross Validation Regularization Neural Networks

53 pages

Computational Learning Theory Part 2

Computational Learning Theory Part 2

16 pages

Notes

34 pages

lecture

15 pages

SVMs and Kernels

SVMs and Kernels

29 pages

SVMs, Duality and the Kernel Trick

SVMs, Duality and the Kernel Trick

61 pages

Homework

3 pages

What’s learning? Point Estimation

What’s learning? Point Estimation

18 pages

Recitation: SVD and dimensionality reduction

Recitation: SVD and dimensionality reduction

21 pages

Active Learning

Active Learning

58 pages

Expectation Maximization

Expectation Maximization

50 pages

Midterm

11 pages

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

34 pages

lecture

11 pages

Homework

7 pages

Graphical Models

Graphical Models

9 pages

lecture

23 pages

Support Vector Machines Kernel Methods

Support Vector Machines Kernel Methods

22 pages

Bayesian Networks – (Structure) Learning

Bayesian Networks – (Structure) Learning

85 pages

Markov Decision Processes (MDPs)

Markov Decision Processes (MDPs)

48 pages

EM (cont.) Machine Learning

EM (cont.) Machine Learning

35 pages

Practical Issues in Learning -- Overfitting and Model Selection

Practical Issues in Learning -- Overfitting and Model Selection

23 pages

PAC-learning, VC Dimension and Margin-based Bounds

PAC-learning, VC Dimension and Margin-based Bounds

32 pages

Learning a probabalistic model of rainfall using graphical models

Learning a probabalistic model of rainfall using graphical models

8 pages

Boosting

35 pages

The Optimized Physical Model for Real Rover Vehicle

The Optimized Physical Model for Real Rover Vehicle

8 pages

Lecture- Machine Learning

Lecture- Machine Learning

52 pages

Reducing Data Dimension

Reducing Data Dimension

7 pages

Co-Training for Semi- supervised learning (cont.)

Co-Training for Semi- supervised learning (cont.)

52 pages

Machine Learning, Decision Trees, Overfitting

Machine Learning, Decision Trees, Overfitting

38 pages

Lecture

49 pages

Lecture

22 pages

When to Picnic?

When to Picnic?

7 pages

Neural Networks

Neural Networks

14 pages

Recitation

31 pages

Lecture

16 pages

Lecture

18 pages

Visualizing Probabilities

Visualizing Probabilities

21 pages

Tree Augmented Naïve Bayesian Classifier with Feature Selection for fMRI Data

Tree Augmented Naïve Bayesian Classifier with Feature Selection for fMRI Data

8 pages

Time series, HMMs, Kalman Filters

Time series, HMMs, Kalman Filters

30 pages

EM for HMMs a.k.a. The Baum-Welch Algorithm

EM for HMMs a.k.a. The Baum-Welch Algorithm

28 pages

Lecture

35 pages

Bayes Nets D-Separation & Inference

Bayes Nets D-Separation & Inference

22 pages

lecture

22 pages

Graph analysis: laws & tools

Graph analysis: laws & tools

37 pages

Bayesian Networks – Representation

Bayesian Networks – Representation

31 pages

Recitation

5 pages

Recitation

47 pages

EM for HMMs a.k.a. The Baum-Welch Algorithm

EM for HMMs a.k.a. The Baum-Welch Algorithm

36 pages

PAC learning, VC Dimension

PAC learning, VC Dimension

27 pages

lecture

24 pages

NONPARAMETRIC CLASSIFICATION AND ERROR ESTIMATION

NONPARAMETRIC CLASSIFICATION AND ERROR ESTIMATION

10 pages

Bayes optimal classifier Naïve Bayes What’s learning, revisited

Bayes optimal classifier Naïve Bayes What’s learning, revisited

12 pages

Simple Model Selection Cross Validation Regularization

Simple Model Selection Cross Validation Regularization

14 pages

Never Ending Learning

Never Ending Learning

22 pages

Naive Bayes and Logistic Regression

Naive Bayes and Logistic Regression

27 pages

Naïve Bayes with Continuous (variables) Logistic Regression

Naïve Bayes with Continuous (variables) Logistic Regression

51 pages

Computational Learning Theory and Model Selection

Computational Learning Theory and Model Selection

19 pages

Computational Learning Theory

Computational Learning Theory

10 pages

PAC-learning, VC Dimension and Margin-based Bounds

PAC-learning, VC Dimension and Margin-based Bounds

59 pages

Artificial Neural Networks to learn f: X  Y

Artificial Neural Networks to learn f: X  Y

13 pages

Midterm

17 pages

exam

15 pages

Gaussians Linear Regression Bias-Variance Tradeoff

Gaussians Linear Regression Bias-Variance Tradeoff

17 pages

Lecture12

32 pages

Bayesian Networks – Inference (continued) Learning

Bayesian Networks – Inference (continued) Learning

27 pages

Bayesian Networks –(Structure) Learning

Bayesian Networks –(Structure) Learning

85 pages

HMMs- HMMs

42 pages

Inductive Programming by Expectation Maximization Algorithm

Inductive Programming by Expectation Maximization Algorithm

8 pages

lecture

19 pages

Neural Networks

Neural Networks

20 pages

SVMs, Duality and the Kernel Trick

SVMs, Duality and the Kernel Trick

14 pages

Clustering

19 pages

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

Logistic Regression (Continued) Generative v. Discriminative Decision Trees

34 pages

Neural Networks

Neural Networks

25 pages

Bayesian Networks – Inference (continued) Learning

Bayesian Networks – Inference (continued) Learning

27 pages

Linear Regression Bias-Variance Tradeoff

Linear Regression Bias-Variance Tradeoff

15 pages

Bayesian Networks – Structure Learning (cont.)

Bayesian Networks – Structure Learning (cont.)

84 pages

Learning from Labeled and Unlabeled Data

Learning from Labeled and Unlabeled Data

54 pages

Logistic Regression

Logistic Regression

16 pages

Logistic Regression

Logistic Regression

25 pages

Lecture

32 pages

Bayesian Networks (Structure) Learning

Bayesian Networks (Structure) Learning

17 pages

Recitation

27 pages

boosting

11 pages

epxing_naivebayes-annotated

epxing_naivebayes-annotated

16 pages

pca-mdps

56 pages

bns

45 pages

mdps

42 pages

boosting-xvalidation-regularization

boosting-xvalidation-regularization

47 pages

stephens

6 pages

svms

10 pages

bns-inference-annotated

bns-inference-annotated

50 pages

Neural Networks

Neural Networks

14 pages

Notes

12 pages

Recitation

9 pages

Recitation

24 pages

PAC-learning, VC Dimension (cont.)

PAC-learning, VC Dimension (cont.)

23 pages

Recitation

15 pages

lecture

42 pages

lecture

29 pages

lecture

15 pages

Expectation Maximization, and Learning from Partly Unobserved Data

Expectation Maximization, and Learning from Partly Unobserved Data

45 pages

gaussians-regression-annotated

gaussians-regression-annotated

15 pages

Algorithm

5 pages

Instance-based Learning

Instance-based Learning

51 pages

Support Vector Machines

Support Vector Machines

15 pages

Linear Regression and Artificial Neural Networks

Linear Regression and Artificial Neural Networks

16 pages

Recitation

3 pages

Dimensionality Reduction PCA

Dimensionality Reduction PCA

54 pages

Expectation Maximization

Expectation Maximization

74 pages

Logistic Regression, Generative and Discriminative Classifiers

Logistic Regression, Generative and Discriminative Classifiers

28 pages

HMM and Neural Network

HMM and Neural Network

26 pages

Reinforcement Learning

Reinforcement Learning

37 pages

Reinforcement Learning

Reinforcement Learning

37 pages

Bayesian Networks – Representation

Bayesian Networks – Representation

45 pages

Decision Trees, cont. Boosting

Decision Trees, cont. Boosting

18 pages

Homework

8 pages

Unsupervised learning or Clustering (cont.) – K-means

Unsupervised learning or Clustering (cont.) – K-means

63 pages

Simple Model Selection Cross Validation Regularization Neural Networks

Simple Model Selection Cross Validation Regularization Neural Networks

20 pages

What’s learning? Point Estimation

What’s learning? Point Estimation

18 pages

Decision Trees Boosting

Decision Trees Boosting

68 pages

Textual entailment in the domain of physics

Textual entailment in the domain of physics

7 pages

Inferring Depth from Single Images in Natural Scenes

Inferring Depth from Single Images in Natural Scenes

8 pages

Logistic regression

Logistic regression

13 pages

Markov Decision Processes (MDPs)

Markov Decision Processes (MDPs)

29 pages

Lecture

12 pages

Graphical Models

Graphical Models

11 pages

Recitation

14 pages

EM for Bayes Nets

EM for Bayes Nets

35 pages

Recitation

36 pages

Neural Networks

Neural Networks

14 pages

Hierarchical Bayesian Models for Text Classification

Hierarchical Bayesian Models for Text Classification

7 pages

Instance-based Learning (a.k.a. non-parametric methods)

Instance-based Learning (a.k.a. non-parametric methods)

16 pages

What’s learning, revisited Overfitting

What’s learning, revisited Overfitting

26 pages

The Boosting Approach to Machine Learning An Overview

The Boosting Approach to Machine Learning An Overview

23 pages

Lecture

24 pages

Bayesian Networks – Structure Learning

Bayesian Networks – Structure Learning

84 pages

Recitation

36 pages

Lecture

22 pages

The General GMM assumption

The General GMM assumption

28 pages

Feature selection for grasp classification

Feature selection for grasp classification

8 pages

Dimensionality Reduction PCA

Dimensionality Reduction PCA

20 pages

Recitation

37 pages

Dimensionality Reduction_03_31_2011_ann

Dimensionality Reduction_03_31_2011_ann

11 pages

DTrees And Over fitting-1-11-2011_final

DTrees And Over fitting-1-11-2011_final

20 pages

Midterm

5 pages

Logistic regression

Logistic regression

13 pages

Recitation

49 pages

agarwal_arora

8 pages

lecture10-boost

lecture10-boost

13 pages

mdps-rl

26 pages

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