Week 16:

Tuesday, 5/1
Ong, CS and Zien, A. An Automated Combination of Kernels for Predicting Protein Subcellular Localization. In: Proceedings of the 8th Workshop on Algorithms in Bioinformatics (WABI), pp. 186-179, Springer. Lecture Notes in Bioinformatics.

Thursday, 5/3
No class - we will meet during finals week for class presentations instead.

Week 15:

Week 14:

Tuesday, 4/24
We will continue the discussion of the Thursday paper.

Thursday, 4/26
Borgwardt K.M., Ong C.S., Schönauer S., Vishwanathan S.V.N., Smola A.J., Kriegel H.-P. Protein Function Prediction via Graph Kernels. Intelligent Systems in Molecular Biology" (ISMB 2005), Detroit, USA, 2005 and Bioinformatics 2005 21(suppl_1):i47-i56.

Thursday, 4/19
P. Kuksa, P.H. Huang, and V. Pavlovic. Efficient use of unlabeled data for protein sequence classification: a comparative study.

G. Schweikert, C. Widmer, B. Schölkopf et al.An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. In: Advances in Neural Information Processing

G. Schweikert, C. Widmer, B. Schölkopf et al.An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. 1433-1440. In: Advances in Neural Information Processing

Week 14:

Tuesday, 4/17
G. Schweikert, C. Widmer, B. Schölkopf et al. [[http://www.fml.tuebingen.mpg.de/raetsch/suppl/genomedomainadaptation/nips.pdf|An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis]]. 1433-1440. In: Advances in Neural Information Processing Systems (NIPS) 21, 2009.

Thursday, 4/12
Michael Hamilton, A.S.N. Reddy, and Asa Ben-Hur. Towards a plant splicing code: conserved splicing regulatory elements from SVM-weighted features.

Xiaojin Zhu and Andrew B. Goldberg. Introduction to Semi-Supervised Learning. Morgan & Claypool, 2009.

Week 13:

Tuesday, 4/10
Read chapters 2 and 6 in: Xiaojin Zhu and Andrew B. Goldberg. [[http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006

 | Introduction to Semi-Supervised Learning]]. Morgan & Claypool, 2009. 

Week 12:

Tuesday, 4/3
A. Ben-Hur and W.S. Noble. Kernel methods for predicting protein-protein interactions. In: Proceedings, thirteenth international conference on intelligent systems for molecular biology. Bioinformatics 21(Suppl. 1): i38-i46, 2005.

Thursday, 4/5
F. ul Amir Afsar Minhas and A. Ben-Hur. Multiple instance learning of Calmodulin binding sites. Submitted.

Thursday, 3/29
We will continue to discuss the paper
A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Week 10:

Week 11:

Tuesday, 3/27
Wei Bi and James Kwok. Multi-Label Classification on Tree- and DAG-Structured Hierarchies. International Conference on Machine Learning (ICML-11), 2011. (Indika)

Y. Sun, S. Todorovic, and S. Goodison. Local Learning Based Feature Selection for High Dimensional Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010. (Nand)
Wei Bi and James Kwok. Multi-Label Classification on Tree- and DAG-Structured Hierarchies. International Conference on Machine Learning (ICML-11), 2011. (Indika)

Week 10:

Tuesday, 3/20
Hui Zou and Trevor Hastie. Regularization and Variable Selection via the Elastic Net. JRSSB (2005) 67(2) 301-320. An R package elasticnet is available from CRAN. (Majdi)
Feiping Nie, Heng Huang, Cai Xiao, Chris Ding. Efficient and Robust Feature Selection via Joint L2,1-Norms Minimization. NIPS 2010. (Rehab)

Thursday, 3/22
Y. Sun, S. Todorovic, and S. Goodison. Local Learning Based Feature Selection for High Dimensional Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 32, no. 9, pp. 1610-1626, 2010. (Nand)

Week 9:

Spring break

Tuesday, 3/6
Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005. (Joel)
Yeung K, Bumgarner R. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology. 4:R83, 2003. (Simon)

Tuesday, 3/6

Week 8:

Tuesday, 3/6
* Chris Ding and Hanchuan Peng. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005. (Joel)

• Yeung K, Bumgarner R. Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology. 4:R83, 2003. (Simon)

Thursday, 3/8
Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani. 1-norm support vector machines. In: Neural Information Processing Systems (NIPS) 16, 2004. 1-norm classifiers create very sparse representations, so are useful for feature selection. (Prathamesh)

Week 7:

Tuesday, 2/28
Lecture: Discussion of evaluation of feature selection; model selection; more discussion of kernels
Thursday, 3/1
Lecture: Protein function prediction.
Reading: A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Lecture: Embedded feature selection methods (from the notes) and discussion of experimental issues in feature selection and model selection.

Week 6:

Tuesday, 2/21
Lecture: Embedded feature selection methods (from the notes) and discussion of experimental issues in feature selection and model selection. Reading: C Ambroise and GJ McLachlan. Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS 99 (10) 6562-6566, 2002.

Thursday, 2/23
Lecture: Discussion of NIPS 2003 feature selection competition.
Reading: I. Guyon, S.R. Gunn, A. Ben-Hur and G. Dror. Results analysis of the NIPS 2003 feature selection challenge. Advances in Neural Information Processing Systems 17, 2004.

Reading: Isabelle Guyon, André Elisseeff. An Introduction to variable and feature selection. Journal of Machine Learning Research, 3(7-8), 2003.

Week 5:

Tuesday, 2/14
Lecture: SVMs and SVM training algorithms, SVM demo.

Thursday, 2/16
Lecture: Feature selection [ notes ]
Reading: Isabelle Guyon, André Elisseeff. . Journal of Machine Learning Research, 3(7-8), 2003.

Week 4:

Week 3:

Tuesday, 2/7
Lecture: Maximum margin classifiers and constrained optimization problems.
Reading: Chapter 7 in Learning with Kernels.

Thursday, 2/9
Lecture: Maximum margin classifiers (cont).
Reading: Chapter 7 in Learning with Kernels.

Lecture: Demo of PyML.

Tuesday, 2/2
Lecture: Maximum margin classifiers.

Lecture: Linear classifiers and kernels (continued).

Week 3:

Tuesday, 1/31
Lecture: Demo of PyML. Maximum margin classifiers.
Reading: Chapter 7 in Learning with Kernels.

Week 2:

Tuesday, 1/24
Lecture: Linear classifiers and kernels [ notes ]
Reading: Sections 1.1 and 1.2 in Learning with Kernels or chapter 2 in Kernel methods for pattern analysis.

Thursday, 1/26
Lecture: Linear classifiers and kernels (continued). A short demo of PyML.

Tuesday, 1/17

Thursday, 1/19

Week 16:

Tuesday, 5/3
Lecture: Semi-supervised learning.
Reading: Jerry Zhu has a two very good tutorials on semi-supervised learning:
Xiaojin Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin, Madison, 2005.
Xiaojin Zhu and Andrew B. Goldberg. Introduction to Semi-Supervised Learning. Morgan & Claypool, 2009.

Thursday, 4/28
Lecture: Machine learning experiment design.
Reading: Christophe Ambroise and Geoffrey J. McLachlan. Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS 2002 99 (10) 6562-6566.

Week 15:

Week 14:

Tuesday, 4/26
Lecture: The 1-norm SVM.
Reading: Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshiran. 1-norm support vector machines. Neural Information Processing Systems (NIPS) 16, 2004.

Journal of Computational Biology, 7(1,2):95--114, 2000.
Thursday, 4/21

Lecture: Feature selection methods.

Reading: Notes on feature selection [ pdf ].

Tuesday, 4/21
Lecture: Feature selection methods. Reading: Notes on feature selection [ pdf ].

Week 14:

Tuesday, 4/19
Lecture: Hidden Markov models and the Fisher kernel.
Reading: T. Jaakkola, M. Diekhans, and D. Haussler. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7(1,2):95--114, 2000.

Lecture: Hidden Markov models for biological sequences

Thursday, 4/7
Lecture: Fayyaz and Mo wrap up their talks.

Week 13:

Tuesday, 4/12
Lecture: Hidden Markov models for biological sequences and the Fisher kernel.
Reading: T. Jaakkola, M. Diekhans, and D. Haussler. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7(1,2):95--114, 2000.

Lecture: The diffusion kernel (kiley) and Training alignment models (Mo).

Support Vector Training of Protein Alignment Models. Journal of Computational Biology, 15(7): 867-880, 2008.

Diffusion Kernels on Graphs and Other Discrete Input Spaces. (ICML 2002).

Support Vector Training of Protein Alignment Models.

Week 12:

Tuesday, 4/5
Lecture: The diffusion kernel.
R. Kondor and J. Lafferty. [[http://www.its.caltech.edu/~risi/papers/diffusion-kernels.pdf

[[http://www.liebertonline.com/doi/pdfplus/10.1089/cmb.2007.0152

 | Support Vector Training of Protein Alignment Models]].
 Journal of Computational Biology, 15(7): 867-880, 2008.

Thursday, 3/31
Lecture: Protein complex prediction (Jake) and Protein metal ion binding (Fayyaz).
Reading:: P. Frasconi and A. Passerini (2009). Predicting the Geometry of Metal Binding Sites from Protein Sequence. Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS'08) (pp. 465-472)
Jian Qiu and William Stafford Noble. Predicting co-complexed protein pairs from heterogeneous data. PLoS Computational Biology. 4(4):e1000054, 2008.

J.-P. Vert, H. Saigo, T. Akutsu, Local alignment kernels for biological sequences, in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.131-154, 2004.

Week 11:

Lecture: Local alignment kernel.

Tuesday, 3/29
Lecture: Local alignment kernel. Reading:: J.-P. Vert, H. Saigo, T. Akutsu, Local alignment kernels for biological sequences, in Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda and J.-P. Vert (Eds.), MIT Press, p.131-154, 2004.

Week 10:

Lecture: Sequence alignment

Week 9:

Reading: Chapter 6 of Introduction to bioinformatics algorithms

Reading: [[http://bix.ucsd.edu/bioalgorithms/book/excerpt-ch6.pdf | Chapter 6] of Introduction to bioinformatics algorithms

Week 10:

Tuesday, 3/22
Lecture: Prediction of Calmodulin interactions and binding sites.
Thursday, 3/24
Lecture: Sequence alignment Reading: Chapter 6 of Introduction to bioinformatics algorithms pdf

Week 8:

Spring break!

Week 8:

Lecture: Prediction of protein-protein interactions (cont). Structured SVMs for prediction of protein function.
Reading: A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Week 7:

Tuesday, 3/1

Lecture: Prediction of protein-protein interactions (cont).

Lecture: Prediction of protein protein interactions with kernel methods.
Reading:

Lecture: Prediction of protein-protein interactions (cont). Structured SVMs for prediction of protein function.
Reading: A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.

Week 7:

Thursday, 3/3
Lecture: Prediction of protein-protein interactions (cont).

Week 6:

Tuesday, 3/1
Lecture: Structured SVMs for prediction of protein function. Prediction of protein protein interactions with kernel methods.
Reading: A. Sokolov and A. Ben-Hur. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Computational Biology 8(2): 357-376, 2010.
A. Ben-Hur and W.S. Noble. Kernel methods for predicting protein-protein interactions. In: Proceedings, thirteenth international conference on intelligent systems for molecular biology. Bioinformatics 21(Suppl. 1): i38-i46, 2005.

Lecture: Tricks for constructing kernels. Introduction to microarray data.

Week 6:

Tuesday, 2/22
Lecture: SVMs for unbalanced data. Practical issues in SVM training
Reading: A. Ben-Hur, C-S. Ong, S. Sonnenburg, B. Schoelkopf, and G. Raetsch. Support vector machines and kernels for computational biology. PLoS Computational Biology 4(10): e1000173, 2008.
A. Ben-Hur and J. Weston. A User’s guide to Support Vector Machines. In Biological Data Mining. Oliviero Carugo and Frank Eisenhaber (eds.) Springer Protocols, 2009.

Thursday, 2/24
Lecture: Introduction to microarray data.
Reading: A.L. Tarca, R. Romero, and S. Draghici. Analysis of microarray experiments of gene expression profiling. See also this primer from NCBI.

Reading: I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large Margin Methods for Structured and Interdependent Output Variables, Journal of Machine Learning Research (JMLR), 6(Sep):1453-1484, 2005.

Tuesday, 2/15

Week 5:

Tuesday, 2/15
Lecture: Discussion of class projects.
Thursday, 2/17'
Lecture: Class projects - continued. Introduction to structured SVMs and possible structured SVM projects.
Reading: Chapter 7 in Learning with Kernels.

Week 4:

Tuesday, 2/8
Lecture: Optimization with constraints.
Reading: The book convex optimization is an excellent free resource. I will cover some material from chapter 5.
Thursday, 2/10
Lecture: Formulating and optimizing maximum margin classifiers.
Reading: Chapter 7 in Learning with Kernels.

Lecture: Linear classifier and kernels (continued).
Thursday, 2/3
Lecture: Maximum margin classifiers.
Reading: Chapter 7 in Learning with Kernels.

Week 3:

Tuesday, 2/1
Lecture: Linear classifier and kernels (continued).

Thursday, 1/27
Lecture: Linear classifiers and kernels (continued). A short demo of PyML.

Week 2:

Tuesday, 1/25
Lecture: Linear classifiers and kernels [ notes ]
Reading: Sections 1.1 and 1.2 in Learning with Kernels; chapter 2 in Kernel methods for pattern analysis.

Thursday, 1/20
Lecture: Course introduction - continued.

Reading: Alex Zien's A primer on molecular biology [ pdf ].
Martin Tompa's notes [ pdf ].

Lecture: Course introduction - some biology basics, the role of machine learning in bioinformatics [ slides ]
Reading: Alex Zien's A primer on molecular biology [ pdf ]. Martin Tompa's notes [ pdf ].

Reading: .

Tuesday, 1/18
Lecture: Course introduction [ slides ]
Reading: Chapter 1 in Kleinberg and Tardos.

Lecture: Plotting with matplotlib. Here's code for a simple plot?.

Wednesday, 4/28

Lecture: Plotting: Generating subplots?, histograms?, legends?.

Friday, 4/30

Lecture: Review for exam.

Reading: The matplotlib tutorial.

Lab: lab 14?

Lecture: Plotting with matplotlib. Here's code for a simple plot?. Generating subplots?.
Reading: The matplotlib tutorial.

Week 15:

Lecture: Plotting with matplotlib. Here's code for a simple plot?. Generating subplots?.

Week 14:

Monday, 4/26

Lecture: Finish motif finding with biopython.

Wednesday, 4/28

Lecture: More about motif finding.

Friday, 4/30

Lecture: Finish motif finding with biopython. Start plotting with matplotlib. Here's code for a simple plot?.

Lab: lab 13?

Lab: lab 12?

Week 14:

Monday, 4/26

Lecture: Using biopython for DNA sequence motifs motifs?. Example data: the E2F1 motif and human promoter sequences.

Week 13:

Lecture: We will use biopython to parse? sequence files.
Here's the data for these examples: Calmodulin sequences in fasta, uniprot, and genbank formats.

Here's the data for these examples: Calmodulin sequences in fasta, uniprot, and genbank formats. The E2F1 motif and human promoter sequences.

Here's the data for these examples: Calmodulin sequences in fasta, uniprot, and genbank formats. The E2F1 motif and human promoter sequences.

Friday, 4/23
Lecture: We will use biopython to parse? sequence files, and to hanld motifs?.
Reading: Chapter 2 in the biopython tutorial.

Reading: Chapter 3 in the biopython tutorial.

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions.

Wednesday, 4/21
Lecture: We will start playing with biopython sequence objects?.

Week 13:

Monday, 4/19
Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions. We will start playing with biopython sequence objects?.
Reading: Chapter 3 in the biopython tutorial.
Lab: lab 12?

Lecture: Methods for solving assignment 9?.

Monday, 4/19

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions. We will start playing with biopython sequence objects?.

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions. We will start playing with biopython sequence objects?.// Reading: Chapter 3 in the biopython tutorial.

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions. We will start playing with biopython sequence objects?.

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation. Also see the biopython install instructions.

Lecture: Installing packages?. As an example, we will install the biopython package. More info at the python documentation.

Friday, 4/16
Lecture: Installing packages?. As an example, we will install the biopython package.

Lecture: interfaces? in Python. New style classes example?. See also the

Lecture: Inheritance in python the shapes example?.

Wednesday, 4/14
Lecture: New style classes example?. See also the python documentation.

Lecture: Operator overloading example?; the cards? example.

Lecture: Inheritance in python the shapes example?. New style classes example?. See also the

Lecture: Python classes - representing shapes?; digression: random numbers?

Monday, 4/12

python documentation.

Week 12:

Monday, 4/12 Lecture: Inheritance in python example?. New style classes example?. See also the python documentation. Lab: lab 11?
Assignment: Assignment 11?

Assignment: Assignment 10?

Lecture: Python classes (cont)

Reading: Chapter 15.

Friday, 4/9
Lecture: New style classes example?. See also the python documentation. Inheritance example?.

Lecture: No lecture today due to future visions.

Friday, 4/2
Lecture: No lecture today due to .

Wednesday, 4/7
Lecture: Operator overloading example?; another example?.
Reading:: Chapter 15.

Lecture: Python classes - more examples?; digression: random numbers?

Week 11:

Monday, 4/5
Lecture: Python classes - more examples?.
Lab: lab 10?
Assignments: Assignment 10?

Wednesday, 3/31

Assignments: Assignment 9?

Lecture: Using Python object - continued.

Wednesday, 3/24
Lecture: Introduction to Python classes. code examples?.

Week 10:

Monday, 3/29
Lecture: Introduction to Python classes. code examples?.
Lab: lab 9?
Assignments: Assignment 9?
Reading: chapter 13 and chapter 15

Lecture: tuples (examples?); functions revisited: variable length argument lists using tuples? and dictionaries?.

Friday, 3/26
Lecture: Using python objects - object methods of strings, lists, and dictionaries?.

Wednesday, 3/24
Lecture: tuples (examples?); functions revisited: beyond fixed size argument lists. Using tuples? and dictionaries?.

Week 9:

Monday, 3/22
Lecture: dictionaries. code examples?.
Lab: lab 8?
Assignments: Assignment 8?
Reading: chapter 12

Friday, 3/12
Lecture: midterm

Wednesday, 3/10
Lecture: recursion - continued. More examples: factorial?, recursive binary search?.

Assignments: Assignment 7?
Reading: chapter 11

Week 8:

Monday, 3/8
Lecture: recursion [ slides ]. code examples?.
Lab: lab 7?
Assignments: Assignment 7?

Lecture: Exceptions code?; command line arguments example?.

Lecture: algorithm efficiency (cont).

Lecture: algorithm efficiency (cont).
Friday, 3/5
Lecture: Exceptions code?.

Lecture: algorithm efficiency (cont); Exceptions code?.

Wednesday, 3/3
Lecture: algorithm efficiency (cont); Exceptions code?. Reading: Exceptions section in chapter 11.

Lecture: binary search code?; measuring the efficiency of algorithms [ slides ]

Week 7:

Monday, 3/1
Lecture: binary search code?; measuring the efficiency of algorithms [ lab 6
Assignments: Assignment 6?

Lecture: Sorting algorithms, continued;

Lecture: Sorting algorithms [ slides ]; here's the code?.

Lecture: Sorting algorithms, continued; binary search code?.

Lecture: Sorting algorithms [ slides ]; here's the code?.
Friday, 2/26
Lecture: Sorting algorithms, continued; binary search code?.

Lecture: Sorting algorithms [ slides ]

Wednesday, 2/24
Lecture: Sorting algorithms ([ slides ])

Lecture: Strings and lists (code?), iterators (code?), files (code?).

Week 5:

Lecture: Strings (continued), string formatting (examples?).

Week 6:

Monday, 2/22

Lab: lab 5?
Assignments: Assignment 5?

Week 6:

Monday, 2/15
Lecture: Strings and lists (code?), iterators (code?), files (code?).

Lecture: Lists are mutable (code?), lists as function parameters (code?), nested lists (code?).

Lecture: Lists are mutable (code?), lists as function parameters (code?), nested lists (code?), strings and lists (code?).

Lecture: Lists are mutable (code?), lists as function parameters (code?), nested lists code?, strings and lists ( code? ).

Lecture: Lists are mutable code?, lists as function parameters code?, nested lists code?, strings and lists code?.

Reading: Chapter 9.

Friday, 2/19
Lecture: Lists are mutable code?, lists as function parameters code?, nested lists code?.

Lecture: Lists (lists?)

Lecture: Lists (examples?)

Lecture: Strings (continued), string formatting (examples?).

Wednesday, 2/17
Lecture: Lists (lists?)
Reading: Chapter 9.

Assignments: Assignment 4?

Week 5:

Monday, 2/15
Lecture: String formatting (examples?). Lists (lists?)
Reading: Chapter 9.
Lab: lab 4?
Assignments: Assignment 3?

Lecture: Strings (continued).

Reading: Chapter 7.

Friday, 2/12
Lecture: String formatting (examples?). Lists (lists?)
Reading: Chapter 9.

Lecture: For loops (cont): nested loops?. Strings (examples?).

Lecture: For loops (cont): nested loops?. Strings (examples?.
Reading: Chapter 7.

Lecture: While loops (cont): printing tables?. Iteration using for loops?.

Wednesday, 2/10
Lecture: For loops (cont): nested loops?.

Lecture: While loops (cont): printing tables?, nested loops?. Iteration using for loops?.
Lab: Lab 3?
Assignments: Assignment 3?

Lecture: While loops: input validation?.

Lecture: While loops (cont): printing tables?, nested loops?. Iteration using for loops?
.

Week 4:

Monday, 2/8
'Lecture:'' While loops (cont): printing tables?, nested loops?. Iteration using for loops?
.

Lecture: While loops: input validation?
.

Friday, 2/5
Lecture: While loops: input validation?, printing tables?, nested loops?. Iteration using for loops?
.

Iteration - the while statement. Here's an example?.

Lecture: Functions that return a value. Examples: area?, divisibility?, absolute value?.
Iteration - the while statement. Here's an example?. Reading: Chapter 5, chapter 6

Wednesday, 2/3

Reading: Chapter 5.

Monday, 2/3
Lecture: Functions that return a value. Examples: area?, divisibility?, absolute value?. Reading: Chapter 5.

Lab: Lab 2?
Assignments: Assignment 2?

Week 3:

Monday, 2/1
Lecture: Conditionals - the if statement boolean variables?, conditionals?, sort names?, letter grade? calculator, a better version of letter grade?.
Reading: Chapter 4.

Lecture: Functions - continued local variables?, conditionals - the if statement boolean variables?.

Lecture: Functions - continued local variables?, conditionals - the if statement boolean variables?, conditionals?, sort names?, letter grade? calculator, a better version of letter grade?.

Reading: Chapter 4.

Friday, 1/29
Lecture: Functions - continued local variables?, conditionals - the if statement boolean variables?, conditionals?, sort names?, letter grade? calculator, a better version of letter grade?.

Lecture: Functions [ slides ]. draw_rectangle?, celsius2fahrenheit?, functions?.

Wednesday, 1/27
Lecture: Functions [ slides ]. draw_rectangle?, celsius2fahrenheit?, functions?. Reading: Chapter 3.

Wednesday, 1/20

Friday, 1/22

Week 1:

Week 2:

Monday, 1/25
Lecture: Python expressions?, and input?. Putting it together: Celsius to fahreheit converter?
Lab: lab1?
Assignments: Assignment1?

Lab: lab1?
Assignments: Assignment1?

Lecture: Python expressions?, and input?.
Lab: lab1?

Lecture: Python expressions?, and input?.

Lecture: Our first program?, programming errors?, Python types?, variables?

Week 2:

Lecture: Pythonexpressions?, and input?.

Lecture: Our first program?, programming errors?, Python types?, variables?, expressions?, and input?.

Lecture: Course introduction; why programming? [ slides ]

Fri, 1/22
Lecture: Our FirstProgram?, programming errors?, Python types?, variables?, expressions?, and input?.
Reading: Chapter 2.

Week 1:

Wed, 1/20

Week 1:

Wed, 1/20

Lectures: Course introduction; why programming? [ slides ]

Lectures: Course introduction; why programming?
Reading: Chapter 1 in How to think like a computer scientist.

Week 1: 1/20

Lectures: Course introduction;
Reading:
Assignments: Coming soon

Week 15: 12/8

Lectures: Java Graphics, GUI and Applets - continued (tuesday). Review (thursday).
Recitation: Programming final.

Lectures: Java packages [ slides ]. JUnit and testing [ slides ]. Graphics, GUI and Applets in Java [ slides ] and [ code ]

Lectures: Java packages [ slides ]. JUnit and testing [ slides ]. Graphics, GUI and Applets in Java [ slides ]

Recitation: JUnit?

Lectures: Java packages [ slides ]. JUnit and testing [ slides ]

Recitation: Sorting?

Week 14: 12/1

Lectures: Java packages [ slides ]. JUnit and testing [ slides ]
Reading: Packages: Chapter 11 in Lewis, 6.7 in Savitch. Lewis covers version 3 of JUnit.
Recitation:

Lectures: Sorting [ slides ] (updated on thu) Here's a nice java applet that illustrates the sorting algorithms we looked at. Obama being asked about sorting

Lectures: Sorting [ slides ] (updated on thu) Here's a nice java applet that illustrates the sorting algorithms we looked at

Lectures: Sorting [ slides ] (updated on thu)

Recitation: Sorting?

Recitation: Sorting?

Week 13: 11/17

Lectures: Sorting [ slides ]
Reading: Chapter 13 in Lewis, Chapter 10 in Walls and Mirrors
Recitation:

Lectures: Linked lists [ slides ] (updated on wed). Linked list code? and doubly linked list code?.

Lectures: Linked lists [ slides ] (updated on wed). Linked list code?

Lectures: Linked lists [ slides ] (updated on wed)

Recitation: Linked Lists?

Week 12: 11/10

Lectures: Linked lists [ slides ]
Reading: Chapter 5 in Walls and Mirrors, 12.1 in Savitch, 14.4 in Lewis.

Recitation: Interfaces?

Week 11: 11/3

Lectures: Midterm on Tue. Static again [ slides ]. Linked lists [ slides ]
Reading: Chapter 5 in Walls and Mirrors, 12.1 in Savitch, 14.4 in Lewis.
Recitation: Programming quiz

Lectures: Interfaces [ slides ]. (updated after thursday's class) [ Code ] for the StaffMember/Employee etc. example. Quiz on Thu.

Recitation: Interfaces?

Lectures: Interfaces [ slides ]. [ Code ] for the StaffMember/Employee etc. example. Quiz on Thu.

Week 10: 10/27

Week 9: 10/27

Lectures: Interfaces [ slides ]. Quiz on Thu.
Reading: Chapter 8 in Savitch, chapter 9 in Lewis.

Lectures: Inheritance [ slides ]. Inheritance and polymorphism [ slides ] Quiz on Thu.

Recitation: Inheritance?

Week 9: 10/20

Lectures: Inheritance [ slides ]. Quiz on Thu.
Reading: Chapter 8 in either of the Java books.

Assignments: A written assignment on induction is available

Reading: Chapter 11 in Savitch, or Chapter 12 in Lewis, or Chapter 3 in Walls and Mirrors.

Reading: Chapter 5 in Rosen.

Reading: Chapter 5 in Rosen.

Week 8: 10/13

Lectures: Induction [ slides ]. Quiz on Thu.
Reading: Chapter 4.1,4.2 in Rosen.

Lectures: Permutations, r-permutation, combinations, and the traveling salesman problem [ slides ] (updated on thursday). Quiz on Thu.

Recitation: Enumeration and Counting?

Lectures: Permutations, r-permutation, combinations, and the traveling salesman problem [ slides ]. Quiz on Thu.

Week 7: 10/6

Lectures: Permutations, r-permutation, combinations, and the traveling salesman problem [ slides ]. Midterm on Thu.
Reading: Chapter 5 in Rosen.

Recitation: Recursion?

Week 6: 9/29

Lectures: Counting [ slides ]. Midterm on Thu.
Reading: Chapter 5 in Rosen.

Lectures: Recursion [ slides ]. Quiz on Thu. More recursion [ slides ]. The maze? example.

Lectures: Recursion [ slides ]. Quiz on Thu. More recursion [ slides ]

Recitation: Recursion?

Week 5: 9/22

Lectures: Recursion [ slides ]. Quiz on Thu.
Reading: Chapter 11 in Savitch, or Chapter 12 in Lewis, or Chapter 3 in Walls and Mirrors.

Lectures: Assertions, pre/post conditions [ slides ]. Javadoc commenting of code. Here's an example?. ArrayList [ slides ]. Here's the example? we worked on in class . Quiz on Thu.

Lectures: Assertions, pre/post conditions [ slides ]. Javadoc commenting of code. Here's an example?. ArrayList [ slides ]. Quiz on Thu.
Reading: Assertions - chapter 4.2 in Savitch. ArrayList - chapter 12.1 in Savitch;

Recitation: A Break from Classes?

Lectures: Assertions, pre/post conditions [ slides ]. Javadoc commenting of code. Here's an example?. Quiz on Thu.

Week 3: 9/8

Week 4: 9/15

Week 4: 9/8

Week 3: 9/15

Lectures: More on Java classes [ slides ]. Quiz on Thu.
Reading: Assertions - chapter 4.2 in Savitch.
Recitation:

Lectures: More on Java classes [ slides ] (updated thu). The code for the Account? class. Quiz on Thu.

Recitation: More on Classes?

Lectures: More on Java classes [ slides ]. The code for the Account? class. Quiz on Thu.

Recitation: Getting familiar with Classes?
Assignments: Programming assignment is available

Week 3: 9/8

Lectures: More on Java classes [ slides ]. Quiz on Thu.
Reading: Chapters 5-6 in Savitch (5 in Lewis).
Recitation:

[ slides ]. Here's the code for the Die? class. Quiz on Thu.

Recitation: Getting familiar with Classes?

[ slides ]. Here's the code for the Die? class.

[ slides ]

Week 2: 9/1

Week 1: 8/25

Lectures: Java classes, objects, and object oriented programming [ slides ] (updated)
Reading: Chapter 5 in the Java book.

[ slides ] (updated)

[ slides ]
Reading: 160 material: chapters 1-4, 7 in the Java book.
Recitation: cs160 recap?

Lectures: Course introduction; cs160 recap

Reading: Chapter 1 in Walls and Mirrors.

Week 1: 8/25

Lectures: Course introduction; cs160 recap [ slides ]

Recitation:

Week 16:

Wed -- special review session 1-3pm at CSB130. Thu -- final exam at 7am.

Week 15: 5/4 - 5/8

Lectures: hash tables (cont); discussion of final [ slides ]
Reading: Walls and Mirrors ch. 13
Recitation: Programming part of final.

hash tables [ slides ]

hash tables [ slides ] Reading: Walls and Mirrors ch. 14 (graphs), 13 (hash tables)

Lectures: Dijkstra's algorithm (slides in the graph algorithms set), more problems on graphs [ slides ]

Lectures: Dijkstra's algorithm (slides in the graph algorithms set), more problems on graphs [ slides ]

Lectures: Dijkstra's algorithm, more problems on graphs [ slides ]
Reading: Walls and Mirrors ch. 14

Week 14: 4/27 - 5/1

Week 13: 4/27 - 5/1

Lectures: Relations, Dijkstra's algorithm, more problems on graphs [ slides ] Reading: Walls and Mirrors ch. 14, Rosen 8.1-8.5 (relations)
Recitation: Review of relations and grammars.

Lectures: graphs - directed acyclic graphs and topological sorting of graphs; graph algorithms [ slides ], relations [ slides ]
Reading: Walls and Mirrors ch. 14, Rosen 8.1-8.5 (relations)

Lectures: graphs - directed acyclic graphs and topological sorting of graphs; graph algorithms [ slides ]

Week 12: 4/13 - 4/17

Lectures: graphs - directed acyclic graphs and topological sorting of graphs
Reading: Walls and Mirrors ch. 14
Recitation: Implementing graphs.

Lectures: Monday: graphs (cont). Wednesday: midterm. Friday: more graphs [ slides ]

Reading: Walls and Mirrors ch. 13; ch. 14.1-14.2

Lectures: Balanced search trees (cont). Friday: graphs [ slides ]

Lectures: Balanced search trees (cont). Friday: graphs
Reading: Walls and Mirrors ch. 13; beginning ch. 14

Week 11: 4/6 - 4/10

Lectures: Monday: graphs (cont). Wednesday: midterm. Friday: more graphs
Reading: Walls and Mirrors ch. 14
Recitation: help with programming assignment, go over written hw, command-line arguments, review for midterm

Reading: Walls and Mirrors ch. 6.2, ch. 12,13

Week 10: 3/30 - 4/3

Lectures: Balanced search trees (cont)
Reading: Walls and Mirrors ch. 13
Recitation: programming quiz. You will use the binary tree and tree node classes.

Lectures: Monday: grammars [ slides ] Wednesday: Working in teams - a talk by Debbie Bartlett. Friday: Balanced search trees [ slides ]

Lectures: Monday: grammars [ slides ] Wednesday: Working in teams - a talk by Debbie Bartlett Friday: Balanced search trees [ slides ]

Recitation: implementing a heap

Lectures: Priority queues and heaps [ slides ]
Reading: Walls and Mirrors ch. 12.2

Week 9: 3/23 - 3/27

Lectures: Monday: grammars [ slides ] Wednesday: Working in teams - a talk by Debbie Bartlett
Reading: Walls and Mirrors ch. 6.2
Recitation:

Lectures: Priority queues and heaps [ slides ] grammars [ slides ]
Reading: Walls and Mirrors ch. 12.2, ch. 6.2

Lectures: Priority queues and heaps [ slides ]

Recitation: recursion

Week 8: 3/9 - 3/13

Lectures: Priority queues and heaps slides ]
Reading: Walls and Mirrors ch. 12.2
Recitation: More iterators

Reading: Walls and Mirrors ch. 11

Week 7: 3/2 - 3/6

Lectures: Trees, binary search trees (cont)

 Reading:  Walls and Mirrors ch. 11
Recitation:  Iterators, recursion

Lectures: Wednesday: Midterm. Mon, Fri: Trees, binary search trees
[ slides ]

Lectures: Wednesday: Midterm. Mon, Fri: Trees, binary search trees ]

Week 6: 2/23 - 2/27

Lectures: Wednesday: Midterm. Mon, Fri: Trees, binary search trees [ slides ]

Week 5: 2/23 - 2/27

Lectures: Wednesday: Midterm. Mon, Fri: Trees, binary search trees slides ]
Reading: Walls and Mirrors ch. 11
Recitation: Review for midterm, recursion

Recitation: recursion Assignments: Second written assignment is available

Week 5: 2/16 - 2/20

Lectures: Sorting algorithms using divide and conquer strategies (cont)
Reading: Walls and Mirrors 10.2, Rosen 7.1, 7.3
Recitation:

Lectures: Queues (cont). Advanced sorting algorithms using divide and conquer strategies; evaluating complexity of recursive algorithms [ slides ]

Lectures: Queues (cont). Advanced sorting algorithms using divide and conquor strategies; evaluating complexity of recursive algorithms [ slides ]

Week 4: 2/9 - 2/13

Recitation: Programming a queue; work on programming assignment

Week 4: 2/2 - 2/6

Lecture: [ slides ] Reading: Walls and Mirrors 10.2, Rosen 7.1, 7.3

Lectures: Queues (cont). Advanced sorting algorithms using divide and conquor strategies; evaluating complexity of recursive algorithms
Reading: Walls and Mirrors 10.2, Rosen 7.1, 7.3
Recitation: Review of big-O analysis, generics, stacks

Recitation: Review of big-O analysis, generics, stacks

Assignments: Second programming assignment is available

Recitation: Review of big-O analysis, generics, stacks

Lectures: Course introduction; abstract data types (ADTs) [ slides ]

Lectures: Measuring the efficiency of algorithms [ slides ]

Week 3: 2/2 - 2/6

Lectures: Stacks and Queues [ slides ]
Reading: chapters 7 and 8 in Walls and Mirrors
Recitation: Review of big-O analysis, generics

Recitation: Using checkin, review of linked lists, help on programming assignment

Recitation: Using checkin, review of linked lists, file IO, help on programming assignment

Week 2: 1/26 - 1/30

Recitation: Using checkin, review of linked lists and file IO

Reading: Sections 3.2,3.3 in Rosen, Section 10.1 in Walls and Mirrors.

Week 1: 1/26 - 1/30

Reading: Sections 3.2,3.3 in Rose, Section 10.1 in Walls and Mirrors.
Lectures: Measuring the efficiency of algorithms [ slides ]
Recitation:

Lectures: Course introduction; abstract data types (ADTs) [ slides ]

Lectures: Course introduction; abstract data types (ADTs) slides

Reading: Chapter 4 in Walls and Mirrors.

Recitation: No recitations this week

Lectures: Course introduction; abstract data types (ADTs)

Reading: Chapter 4 in Walls and Mirrors. Lectures: Course introduction; abstract data types (ADTs) Recitation: No recitations this week Assignments: First programming assignment is available

Week 1: 1/21 - 1/23

Week 1: 1/21 - 8/23

Week 1: 8/25 - 8/29

Coming soon!