last updated: December 11, 3p

Instructor: Dr. Christoph F. Eick

office hours (573 PGH): MO 3:15-4:45p WE 2:30-3p

cancelled classes: We., October 21, Mo., November 16.

makeup class (for cancelled class): Fr., November 20, 9:45a-noon

class room: 204 AH

**Focus**: One focus of the course is to learn how to read, summarize, present, review, and evaluate scientific papers. The papers that will be discussed during the course originate from the following areas: Anomaly Dection, Density Estimation, Deep Learning, Ensemble Learning, and Spatial-Temporal Clustering. Moreover, you will get some exposure to current developments and research in machine learning and related fields.

There will be also some more general discussion on machine learning research methodology and on how to conduct a scientific research project. Finally, there will learn and practice how to write abstracts, introductions, conclusions, paper reviews, and executive summaries.

**Reasons to take the course**: The course is a good preparation for master thesis and PhD dissertation research in the areas machine learning, data mining, databases, data analysis, and artificial intelligence. Moreover, seeing how well-known scientists present their research results will hopefully help you to do a better job in conducting your own research and in presenting your research results in your future publications.

**Course Elements**: 2 quizes, 4-5 paper walkthroughs, student presentations summarizing the content of machine learning papers,
survey style lectures on some of the subfields covered in Fall 2015 and on (machine learning) reasarch methodology, 2 homeworks, discussion of papers and homework solutions, machine learning software demos.

Tentative Weights of the Different Components of the Course: Quizes: 46%, Presentations: 36%, Homeworks: 14%, Attendance and Class Particiaption: 4%.

Outlier Detection with Mixture Model (Student Presentation Paper; to be presented by Nguyen Pham on Sept. 16, 2015)

Orair's et al.'s Paper on Distance-based Outlier Detection (Student Presentation Paper; to be presented by Kajol Agarwal on We., Sept. 23, 2015)

Silverman's Classical Density Estimation Paper (for Paper Walkthough on September 9;

Kim and Scott's Paper on Robust Non-Parametric Density Estimation (companion paper to the paper, listed next!)

Vandermeulen et al.'s paper on Robust Non-Parametric Density Estimation that was presensented at NIPS 2014 (to be presented on Sept. 28 by Khalil, Nacer)

Survey Paper on Deep Learning Approaches (to be used for paper walkthrough on Sept. 30/Oct. 7)

Target Propagation (an alternative approach to back propagation; to be presented by Lifeng Yan on October 5)

Paper on Deep Convolutional Neural Networks (main source for Arthur's deep learning presentation on October 12, 2015)

Papers on Trajectory Clusterings: Visual Analytics Approaches, Patition and Group Approach, Survey paper.

Paper on Trajectory Classification using Region-based and Trajectory-based Clustering (main source for Kajol's presentation on Nov. 9/11, 2015)

Paper on Trajectory Classification with HMM (main source for Ha's presentation on Nov. 11, 2015)

Ensemble Learning Walkthough Paper

Original ADABOOST Paper (main source for Yongli's and Nacer's presentation on Nov. 18, 2015)

Dietterich's Classical Paper on Ensemble Learning (main source for Lifeng's presentation on Nov. 20, 2015)

Paper on Stacked Generalization Techniques for kNN (main source for Tianxiao's presentation on Nov. 20, 2015)

Paper on The Dynamics of AdaBoost (main source for Nguyen's presentation on Nov. 30)

Paper on an approach for the Netflix Contest; used in Arthur's presentation on Dec. 2.

Book Chapters on Sequential Prediction Problems and Recurrent Neural Networks, to be used in part in Kunal presentation on Nov. 30, 2015.

Paper Xiong et al. on Group Anomaly Detection which additionally introduces hierarchical Gaussian Models

Density Estimation Techniques in R

Clustering via Non-Parametric Density Estimation Techniques in R

Rice University Article on How to Read Scientific Papers

Introduction Anomaly Detection (covered on August 26)

Introduction to Parametric Density Estimation (covered on August 26/31)

Introduction to Non-parametric Density Estimation (covered on August 31)

Andrew Moore's Tutorial on (Gaussian) Mixture Models (will discuss a few slides of this tutorial; likely, on August 31)

Research is Exploring and Overcoming Boundaries (covered on September 14)

How to Read and Write Scientific Papers (update on October 14, 2015)

Reviewing of Scientific Papers (covered on October 14/19)

How to write the ‘Empirical Evaluation’ Section of Your Paper (covered in the lecture on October 19)

Kajol's Presentation on Distance-Based Outlier Detection

Ha and Nikoloas Presentation on Density Estimation Teachiques in R

Khalil Nacer's Presentation Slides

Khalil Nacer's Supplementary Material

Kunal's Presentation on Back Propagation

Kunal's backpropagation example

Dr. Eick's Backpropagation Slides

Lifeng's Presentation on an Alternative Backpropagation Approach

Arthurs's Presentation on Conv. NNs

Yongli's Presentation on Trajectory Clustering

Tianxiao's Presentation on Trajectory Clustering

Nikolaos's Presentation on Temporal Activity Clustering

Kajol's Presentation on Trajectory Classification Relying on Region and Trajectory Clustering

Ha's Presentation on Trajectory Classification with HMMS

Yongli's and Nacer's Presentation of AdaBoost

Lifeng's presentation covering Dietterich's article on ensembles

Tianxiao Presentation dicussing a stacked generalization approach for kNN-classifiers

Kunal's presentation on recurrent neural networks and sequential prediction problemsgeneralization

Nguyen presentation on the convergence of AdaBoost

Arthur's presentation on the Netflix Contest

A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70

C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

Only machine written solutions to homeworks and assignments are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.