## Main.LectureNotes History

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Required Texbook publisher's webpage

reference textbook: Python Scripting for Computational Science

Python-Scripting-Computational-Science-Engineering

Lec1_intro.pdf Δ Introduction to Python

Will be shared via dropbox.com.

1page-cheatsheet.pdf Δ 1 page python cheatsheet

1page-cheatsheet.pdf Δ 1 page python cheatsheet

Lec1_intro.pdf Δ Introduction to Python

Texbook publisher's webpage

Required Texbook publisher's webpage

reference textbook: Python Scripting for Computational Science

DimRed.ppt Δ dimension reduction

DimRed.ppt Δ dimension reduction

Texbook publisher's webpage

Texbook publisher's webpage

Python-Scripting-Computational-Science-Engineering

Lec1_intro.ppt Δ Introduction to Pattern Recognition

#### Week 1 (Chapter 1)

### Week14

### Week16

Chapter 1. Introduction (ppt) Chapter 2. Supervised Learning (ppt) Chapter 3. Bayesian Decision Theory (ppt) Chapter 4. Parametric Methods (ppt) Chapter 5. Multivariate Methods (ppt) Chapter 6. Dimensionality Reduction (ppt) Chapter 7. Clustering (ppt) Chapter 8. Nonparametric Methods (ppt) Chapter 9. Decision Trees (ppt) Chapter 10. Linear Discrimination (ppt) Chapter 11. Multilayer Perceptrons (ppt) Chapter 12. Local Models (ppt) Chapter 13. Hidden Markov Models (ppt) Chapter 14. Assessing and Comparing Classification Algorithms (ppt) Chapter 15. Combining Multiple Learners (ppt) Chapter 16. Reinforcement Learning (ppt)

## course slides

## Course slides

#### Week 2 (Chapter 2)

chapter2.ppt

#### Week 3

#### Week 4

### Week 5

### Week 6

### Week7

lec9.ppt Δ Linear discriminant

### Week8

### Week9

### Week10

Midterm March 15.

### Week11

### Week12

lec14.ppt Δ evaluation of classifers

### Week13

ensemble.ppt Δ Ensemble Learning Algorithms

### Week15

lec17.ppt Δ Reinforcement Learning

Richard Sutton's Talk on RL

Download Textbook lecture notes

VideoLectures Online video on RL

## Please download the above textbook slides. My slides are based on theirs with minor modification

Texbook publisher's webpage

## course slides

DimRed.ppt Δ dimension reduction

ensemble.ppt Δ Ensemble Learning Algorithms

Download Textbook lecture notes

Download Textbook lecture notes

VideoLectures Online video on RL

Richard Sutton's Talk on RL

### Week15

### Week10

midterm1

Midterm March 15.

### Week7

### Week7

### Week8

### Week 6

### Week 6

chapter2.ppt

Textbook lecture notes

Download Textbook lecture notes

#### Week 1

Chapter 1. Introduction (ppt) Chapter 2. Supervised Learning (ppt) Chapter 3. Bayesian Decision Theory (ppt) Chapter 4. Parametric Methods (ppt) Chapter 5. Multivariate Methods (ppt) Chapter 6. Dimensionality Reduction (ppt) Chapter 7. Clustering (ppt) Chapter 8. Nonparametric Methods (ppt) Chapter 9. Decision Trees (ppt) Chapter 10. Linear Discrimination (ppt) Chapter 11. Multilayer Perceptrons (ppt) Chapter 12. Local Models (ppt) Chapter 13. Hidden Markov Models (ppt) Chapter 14. Assessing and Comparing Classification Algorithms (ppt) Chapter 15. Combining Multiple Learners (ppt) Chapter 16. Reinforcement Learning (ppt)

#### Week 1 (Chapter 1)

#### Week 2

#### Week 2 (Chapter 2)

Midterm1 discussion

Midterm2

# Please download the above textbook slides. My slides are based on theirs with minor modification

## Please download the above textbook slides. My slides are based on theirs with minor modification

Please download the above textbook slides. My slides are based on theirs with minor modification

# Please download the above textbook slides. My slides are based on theirs with minor modification

### Week12 11/03, 11/05

No class on 11/03

### Week12

### Week13 11/10, 11/12

### Week13

### Week14 11/17, 11/19

### Week14

### Week15 11/24

### Week15

### Week15 12/01

### Week15

Please download the above textbook slides. My slides are based on theirs with minor modification

lect8.ppt Δ Divide and conquer, mergesort, quicksort

lect9.ppt Δ Divide and conquer

### Week 6 09/22, 09/24

lect11.ppt Δ Divide and conquer: Closest-Pair Problem, convex-hull

### Week8 10/06

### Week8

### Week9 10/13, 10/15

lect12.ppt Δ Decrease and conquer. a^n

lect13.ppt Δ DFS

### Week10 10/20, 10/22

lect13.ppt Δ BFS and Topological sorting

lect14.ppt Δ ch6a

### Week11 10/27, 10/29

lect15.ppt Δ ch6b

Textbook lecture notes

lecture notes from textbook

lecture notes from textbook

sumMotifScores.txt Δ Motivation, steps for algorithm design

### Week15 11/24

### Week15 11/24

### Week15 12/01

### Week15 12/01

lect13.ppt Δ BFS and Topological sorting

lect14.ppt Δ ch6a

lect15.ppt Δ ch6b

### Week13 11/10, 11/12

### Week13 11/10, 11/12

### Week14 11/17, 11/19

### Week14 11/17, 11/19

### Week8

### Week8 10/06

### Week9

### Week9 10/13, 10/15

### Week10

### Week11

Midterm2

### Week12 11/03, 11/05

No class on 11/03

### Week12

### Week13 11/10, 11/12

### Week8

### Week8

Midterm1 discussion

### Week 6

midterm1

lect11.ppt Δ Divide and conquer: Closest-Pair Problem, convex-hull

lect12.ppt Δ Decrease and conquer. a^n

lect13.ppt Δ DFS

### Week8

No class

sumMotifScores.pl Δ Motivation, steps for algorithm design

sumMotifScores.txt Δ Motivation, steps for algorithm design

sumMotifScores.txt Δ Motivation, steps for algorithm design

sumMotifScores.pl Δ Motivation, steps for algorithm design

sumMotifScores.pl Δ Motivation, steps for algorithm design

sumMotifScores.txt Δ Motivation, steps for algorithm design

sumMotifScores.pl Δ Motivation, steps for algorithm design

#### Week 3

Lecture1.ppt Δ Introduction to Parallel Computing

Lecture1b.ppt Δ PBS and Linux cluster

Download labsession1.tar.gz Δ Lab session program sample code

to uncompress: tar zxvf labsession1.tar.gz

Lecture2.ppt Δ Parallel Architecture

Lecture3.ppt Δ Parallel algorithm design model

Lecture5.ppt Δ 1D array problem decomposition strategy

Lecture4.ppt Δ MPI programming: the basics

Lecture6.ppt Δ 2D array problem decomposition strategy

MPIsamples.zip Δ Sample MPI source codes in text book

Lecture9.ppt Δ Manager/Worker model for embarassingly parallel problems. Document classification

Lab session 2 lab2.tar.gz Δ Lab session files to uncompress: tar zxvf lab2.tar.gz

gridcomputing.ppt Δ grid computing

Lecture10.ppt Δ Monte carlo parallel algorithm

Lecture8.ppt Δ Matrix vector multiplication

Lecture11.ppt Δ Matrix multiplication

Lecture7.ppt Δ Performance analysis

Lecture16.ppt Δ Combinatorial search

Lecture17.ppt Δ Hadoop and OpenMPI

Lecture18.ppt Δ Hybrid MPI and OpenMP programming

Lecture18.ppt Δ Hybrid MPI and OpenMP programming

### Week14

### Week15

### Week9

No class

### Week10

Lecture16.ppt Δ Combinatorial search

### Week11

Lecture17.ppt Δ Hadoop and OpenMPI

Lecture7.ppt Δ Performance analysis

Lecture8.ppt Δ Matrix vector multiplication

Lecture11.ppt Δ Matrix multiplication

Lecture10.ppt Δ Monte carlo parallel algorithm

gridcomputing.ppt Δ grid computing

Lecture5.ppt Δ 1D array problem decomposition strategy

Lecture5.ppt Δ 1D array problem decomposition strategy

### Week 6

### Week7

### Week 6

### Week7

### Week7

Lecture9.ppt Δ Manager/Worker model for embarassingly parallel problems. Document classification Lab session 2

### Week8

lab2.tar.gz Δ Lab session files to uncompress: tar zxvf lab2.tar.gz

MPIsamples.zip Δ Sample MPI source codes in text book

### Week 5

Lecture5.ppt Δ 1D array problem decomposition strategy

### Week 6

Lecture6.ppt Δ 2D array problem decomposition strategy

#### Week 4

Lecture4.ppt Δ MPI programming: the basics

Lecture3.ppt Δ Parallel algorithm design model

Lecture1.ppt Δ Introduction to Parallel Computing

Lecture2_paul.ppt Δ PBS and Linux cluster

Lecture1b.ppt Δ PBS and Linux cluster

Lecture2_paul.ppt Δ PBS and Linux cluster

Lecture2_paul.ppt Δ PBS and Linux cluster

Download labsession.tar.gz Δ Lab session program sample code

to uncompress: tar zxvf labsession.tar.gz

Download labsession1.tar.gz Δ Lab session program sample code

to uncompress: tar zxvf labsession1.tar.gz

Lecture2.ppt Δ PBS and Linux cluster

Lecture2_paul.ppt Δ PBS and Linux cluster

Download Lab session Attach:program code Δ

Download labsession.tar.gz Δ Lab session program sample code to uncompress: tar zxvf labsession.tar.gz

Download Lab session Attach:program code Δ

Lecture1.ppt Δ Data mining overview

Lecture2.ppt Δ K-nearest neighbor/weka

#### Week2

Lecture3.ppt Δ preprocessing

### Week 3

Lecture4.ppt Δ decision tree

Lecture5.ppt Δ Decision Tree & Classifier Evaluation

#### Week4

Lecture5.ppt Δ Model Evaluation

Lecture6.ppt Δ Bayesian Classifier and ANN

#### Week5

Lecture7.ppt Δ SVM

Lecture8.ppt Δ Ensemble algorithms

#### Week6

Lecture9.ppt Δ Clustering/K-means

Lecture10.ppt Δ Hiearchical/Density clustering

#### Week7

Lecture11.ppt Δ Clustering evaluation/validation Midterm

### Reference slides

Slides from Textbook(Tan)

Slides from Textbook (Han)

Statistical Data Mining Tutorials by Andrew Moore at CMU (recommended)

Midterm

#### Week6

Lecture9.ppt Δ Clustering/K-means

Lecture10.ppt Δ Hiearchical/Density clustering

#### Week7

Lecture11.ppt Δ Clustering evaluation/validation

Lecture1.ppt Δ Data mining overview

Lecture2.ppt Δ K-nearest neighbor/weka

Lecture3.ppt Δ preprocessing

Lecture4.ppt Δ decision tree

Lecture8.ppt Δ Ensemble algorithms

Lecture5.ppt Δ Decision Tree & Classifier Evaluation

Lecture5.ppt Δ Model Evaluation

Lecture6.ppt Δ Bayesian Classifier and ANN

Lecture7.ppt Δ SVM

#### Week 2

#### Week 3

#### Week 4

#### Week 5

Lecture9.ppt Δ (Pls download and rename to Lecture9.zip)

#### Week 6

#### Week 7

#### Week8

#### Week9

#### Week10

#### Week11

#### Week12

### Week13

Lecture23.ppt Δ Happy Thanksgiving

### Week14

### Week15

Lecture9.ppt Δ (Pls download and rename to Lecture9.zip)

#### Week 7

Slides from Textbook (Han)

Slides from Textbook (Han)

Statistical Data Mining Tutorials by Andrew Moore at CMU (recommended)

Slides from Textbook(Tan)

Slides from Textbook (Han)

### Reference slides

Slides from Textbook(Tan)

Slides from Textbook (Han)

Slides from Textbook(Tan)

Slides from Textbook (Han)

(:attachlist:)

#### Week 1

#### Week 2

#### Week 3

#### Week 4

#### Week 5

Lecture notes/slides will be uploaded during the course.

Lecture notes/slides will be uploaded during the course.

(:attachlist:)

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Lecture notes/slides will be uploaded during the course.

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