Lecture 1: Overview of Machine Learning

Homework 1 due on Tue, Sep 9. (download file operations.m )

Lecture 2: Supervised learning; Standard accuracy measures; Optimal predictors; Equivalence between optimal regression and classification; Extreme approaches to minimizing MSE (nearest neighbor algorithm and linear regression).

Lecture 3: Linear regression (solution, statistical results); Nonlinear regression (gradient descent optimization); Logistic regression (by minimizing MSE); Maximum Likelihood (ML) approach for unsupervised learning (density estimation) and regression.

Homework 2 due on Tue, Sep 23. (download file hw2.m)

Lecture 4: ML for classification; Neural Networks (NN) - neuron, architecture of NN, backpropagation algorithm, representational power of NN.

Homework 3 due on Tue, Sep 30.

Lecture 5: Neural Networks (NN) - practical issues.

Homework 4 due on Tue, Oct 07.

Lecture 6: Bootstraping; Decision Trees

Lecture 7: Decision Trees; Support Vector Machines

Lecture 8: Support Vector Machines; Clustering

Muller, K.-R.; Mika, S.; Ratsch, G.; Tsuda, K.; Scholkopf, B. An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks, 12, 2,  181-201 , 2001.

Homework 5 due on Fri 2pm, Oct 31. (downoload letter recognition data)

Lecture 9: Clustering; Association Rules, Midterm Overview

Midterm, Class Project Themes

Lecture 11: Contrast Classifiers, Bayesian Networks, Naive Bayes Classification (temporary lecture notes)

Pointer: Bayesian Network lecture notes from Milos Hauskrecht UPitt (download lectures from Feb 26, March 10 amd March 12)

Homework 6 due on Tue, Nov 18.

Opitz, D., Maclin, R, Popular ensemble methods: an empirical study. Artificial Intelligent Research, Vol. 11 (1999), 169-198.

Fern, X. Z. and Brodley, C.E., Random Projection for high dimensional data clustering: A cluster ensemble approach,  Twentieth International Conference on Machine Learning 2003.

Lecture 12: Decision Making under Uncertainty, Markov Decision Processes, Reinforcement Learning (temporary lecture notes)

Pointer: Lectures 19, 20, 22 from MIT 6.825 Techniques in Artificial Intelligence Fall 2002 course, taught by Leslie Kaebling

Schedule for 15-minute class presentations:

1. Yilian Qin, "Progressive sampling for learning from large data sets" 
2. Abhiruchi Lanjewar, "Text classification with naive Bayes and support vector machines"
3. Poonam Buch, "Feature selection for text classification" 
4. Vladimir Vacic, "Approximate nearest neighbor search using vantage points approach"
5. Pooja Hedge, "Collaborative filtering"
6. Xiaoying Huang, "Spectrum kernels for classification of sequence data" 
7. Hao Sun, "Classification of microarray data" 
8. Michael Slifker, "Clustering of microarray data" 
9. Bo Han, "Y. Li, Z.A. Bandar, D. McLean, An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, 2003"

Schedule for 15-minute class presentations:

1. Liting Wen, "C. Rosenberg and M. Hebert, Training Object Detection Models with Weakly Labeled Data, British Machine Vision Conference, 2002." 
2. Archana Gupta, "S.K. Lam, D.M. Pennock, D. Cosley, S. Lawrence,1 Billion Pages = 1 Million Dollars? Mining the Web to Play ``Who Wants to be a Millionaire?'', Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 2003" 
3. Qifang Xu, "B. Zadrozny and C. Elkan. Transforming classifier scores into accurate multiclass probability estimates, Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining, 2002" 
4. Yong Li, "M.A. Maloof, P. Langley, T.O. Binford, R. Nevatia, S. Sage. Improved rooftop detection in aerial images with machine learning. Machine Learning, 2002." 
5. Xiaoming Duan, "S. Zempke, On Developing a Financial Prediction System: Pitfalls and Possibilities, First International Workshop on Data Mining Lessons Learned at ICML'02, 2002" 
6. Tom Gradel, "G. Das, K.-I. Lin, H. Mannila, G. Renganathan, P. Smyth. Rule discovery from time series. Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, 1998." 
7. Troy Schrader, "Acoustic signal recognition using neural networks"
8. Meghneel Gore, "Genetic Algorithms"