Teaching     Home

CSE 6363 (Fall 2016)
Machine Learning

Heng Huang
[ Administrative Basics | Course Description | Assignments | Outline of Lectures ]

Administrative Basics

Lecture

WH 221 | Tuesday and Thursday 2-3:20 PM
Instructor

Heng Huang | ERB 533 | Office hours: Tuesday and Thursday 3:20-5:00 PM
Textbook

Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, T. Hastie, R. Tibshirani, J. H. Friedman.
Pattern Classification, 2nd edition, Richard Duda, Peter Hart, David Stork.
Work

Homework sets. (75%)
Class presentations. (25%)
Request

Good math, statistics, and programming background.
TA

Zhouyuan Huo| Office hours: Tuesday and Thursday 10am-noon | Office: ERB 414 | Email: zhouyuan.huo@mavs.uta.edu

Course Description

This course is for the graduate-level students to study the background in the methodologies, mathematics and algorithms in machine learning or who may need to apply machine learning techniques to scientific applications (e.g. computer vision, bioinformatics, data mining, information retrieval, natural language processing, etc). The following broad categories will be covered:
1) Introduction to Pattern Recognition and Machine Learning
2) Regression
2) Bayesian Learning
3) Linear Discriminants
4) Neural Networks
5) Support Vector Machines and Kernel Method
6) Decision Trees
7) Feature selection
8) Model selection
9) Unsupervised Learning
10) Graphical Model
11) Semi-supervised Learning Methods

Assignments

Homeworks

Homework sets will be assigned here.
Homework 1 (Due Sept. 27)
Homework 2 (Due Oct. 20)
Homework 3 (Due Dec. 8)
Paper presentations

Each student is expected to present one paper in the course of the semester and lead the discussion on the paper. The paper will be distributed to students later.

Outline of Lectures

Week 1.

Tue Aug 25: Introduction to Machine Learning (slides)

Week 2.

Tue Aug 30: Probability Theory, Probability Distributions (slides)
Thu Sept 1: Decision Theory, Information Theory, Bayesian Learning Theory (slides 1, slides 2)

Week 3.

Tue Sep 6: Naive Bayes Classifier (slides)
Thu Sep 8: Logistic Regressions, Linear and Quadratic Discriminant Analysis Classifiers (slides)

Week 4.

Tue Sep 13: Dimensionality Reduction: Fisher Linear Discriminant Analysis (slides)
Thu Sep 15: Linear Algebra Review (slides)

Week 5.

Tue Sep 20: Linear Support Vector Machine (slides)
Thu Sep 22: Kernel Support Vector Machine, Kernel Methods, and Support Vector Regression (slides)

Week 6.

Tue Sep 27: Neural Network I (slides)
Thu Sep 29: Neural Network II (slides)

Week 7.

Tue Oct 4: Dimensionality Reduction: PCA and SVD (slides)
Thu Oct 6: Dimensionality Reduction: Kernel PCA, Tensor Decomposition (slides)

Week 8.

Tue Oct 11: Validations, KNN, and Clustering (slides)
Thu Oct 13: K-means and EM Algorithm (slides)

Week 9.

Tue Oct 18: Feature Selection (slides)
Thu Oct 20: Nonegative Matrix Factorization (slides)

Week 10.

Tue Oct 25: Spectral Clustering (slides)
Thu Oct 27: Hidden Markov Models (slides)

Week 11.

Tue Nov 1: Semi-Supervised Learning (slides)
Thu Nov 3: Boosting and Decision Tree (slides)

Week 12.

Tue Nov 8: Conditional Random Field I (slides)
Thu Nov 10: Conditional Random Field II

Week 13.

Tue Nov 15: Markov Random Field (slides)
Thu Nov 17: Class Presentations:
ifile: An Application of Machine Learning to EMail Filtering (Information retrieval). (Zahra Anvari and Negin Fraidouni) Application of Dimensionality Reduction in Recommender System -- A Case Study (Information retrieval). (Kabra, Kabra Chetan Rajesh and Malgaonkar Ashutosh)
Intra-personal kernel space for face recognition (Computer Vision). (Ashay Rajimwale and Tasneem Burmawala)

Week 14.

Tue Nov 22: Class Presentations:
A Bayesian approach to filtering junk e-mail (Information retrieval). (Aishwarya Ashok and Jasmine Manoj)
Boosting the Feature Space: Text Classification for Unstructured Data on the Web (Data Mining). (Fatma Arslan and Abu Ayup Ansari Syed)
Spam Filtering with Naive Bayes - Which Naive Bayes? (Information retrieval). (Parisa Rabbani and Farnaz Farahanipad)
Sentiment Analysis: A New Approach for Effective Use of Linguistic Knowledge and Exploiting Similarities in a Set of Documents to be Classified (Information retrieval). (Akshit Singhal and Siddharth Shah)
Thu Nov 24: No class due to holiday

Week 15.

Tue Nov 29: Class Presentations:
Self-taught Learning: Transfer Learning from Unlabeled Data (Machine Learning). (Tasnim Makada and Sanika Sunil Gupta)
Support vector machine active learning for image retrieval (Information retrieval). (Shirong Xue and Chunhai Feng)
Multinomial Naive Bayes for Text Categorization Revisited (Data Mining). (Rohit Gaikwad and Mohammad Minhazul Haq)
Learning Social Networks from Web Documents Using Support Vector Classifiers (Social networks and web applications). (Shashank Madhav and Chetan There)
Thu Dec 1: Class Presentations:
Gene Selection for Cancer Classification using Support Vector Machines (Machine Learning). (Prachi Patel and Sarika Waje)
Effective Attacks and Provable Defenses for Website Fingerprinting (Information Security). (Tejas Shetti and Aayush Sharma)
Probabilistic frequent itemset mining in uncertain databases (Data Mining). (Abhishek Jain and Swati Joshi)
Robust Principal Component Analysis for Computer Vision (Computer Vision). (Reza Ghoddoosian and Omkar Ajay Pawar)

Week 16.

Tue Dec 6: Class Presentations
Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization (Machine Learning). (Lin Yan and Xin Miao)
Combining Topic Models and Social Networks for Chat Data Mining (Social networks and web applications). (Ankush Madhav Udyavar and Harsha Triyambaka Mysur)
Improved prediction of protein-protein binding sites using support vector machines (Bioinformatics). (Bhushan Yavagal and Amith Hegde)
Learning to parse images of articulated bodies (Computer Vision). (Arjun Vekariya and Neeraj Mishra)

Paper List for Presentation

Presentation:

Every team selects one paper from the following list and presents the paper (about 20 min) in class. Grade: slides preparation 50%, oral presentation 25%, and answer questions 25%. Please download the papers using UTA campus internet, because some digital libraries require the subscription.
Please select your presentation time from the following options: Nov. 17, 22, 29, Dec. 1, 6.

A Bayesian approach to filtering junk e-mail (Information retrieval). (Aishwarya Ashok and Jasmine Manoj)
Face Detection and Recognition Using Hidden Markov Models (Computer vision).
The Customized-Queries Approach to CBIR Using EM (Information retrieval).
ifile: An Application of Machine Learning to EMail Filtering (Information retrieval). (Zahra Anvari and Negin Fraidouni)
Spam Filtering with Naive Bayes - Which Naive Bayes? (Information retrieval). (Parisa Rabbani and Farnaz Farahanipad)
Sentiment Analysis: A New Approach for Effective Use of Linguistic Knowledge and Exploiting Similarities in a Set of Documents to be Classified (Information retrieval). (Akshit Singhal and Siddharth Shah)
Learning Social Networks from Web Documents Using Support Vector Classifiers (Social networks and web applications). (Shashank Madhav and Chetan There)
Support vector machine active learning for image retrieval (Information retrieval). (Shirong Xue and Chunhai Feng)
Application of Dimensionality Reduction in Recommender System -- A Case Study (Information retrieval). (Kabra, Kabra Chetan Rajesh and Malgaonkar Ashutosh)
Combining Topic Models and Social Networks for Chat Data Mining (Social networks and web applications). (Ankush Madhav Udyavar and Harsha Triyambaka Mysur)
Robust Principal Component Analysis for Computer Vision (Computer Vision). (Reza Ghoddoosian and Omkar Ajay Pawar)
Discriminant-EM Algorithm with Application to Image Retrieval (Computer Vision).
Learning to parse images of articulated bodies (Computer Vision). (Arjun Vekariya and Neeraj Mishra)
Gesture Recognition using Hidden Markov Models from Fragmented Observations (Computer Vision).
Robust and Accurate Shape Model Fitting using Random Forest Regression Voting (Computer Vision).
Intra-personal kernel space for face recognition (Computer Vision). (Ashay Rajimwale and Tasneem Burmawala)
Nonrigid Shape Recovery by Gaussian Process Regression (Computer Vision).
Style Machines (Graphics).
Boosting the Feature Space: Text Classification for Unstructured Data on the Web (Data Mining). (Fatma Arslan and Abu Ayup Ansari Syed)
Probabilistic frequent itemset mining in uncertain databases (Data Mining). (Abhishek Jain and Swati Joshi)
Multinomial Naive Bayes for Text Categorization Revisited (Data Mining). (Rohit Gaikwad and Mohammad Minhazul Haq)
Improved prediction of protein-protein binding sites using support vector machines (Bioinformatics). (Bhushan Yavagal and Amith Hegde)
SVM-RFE Peak Selection for Cancer Classification with Mass Spectrometry Data (Bioinformatics).
Generalized Discriminant Analysis Using a Kernel (Machine Learning).
Overview and Recent Advances in Partial Least Squares (Machine Learning).
A Probabilistic Interpretation of Canonical Correlation Analysis (Machine Learning).
Self-taught Learning: Transfer Learning from Unlabeled Data (Machine Learning). (Tasnim Makada and Sanika Sunil Gupta)
Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization (Machine Learning). (Lin Yan and Xin Miao)
Learning and Inferring Transportation Routines (Activity Recognition).
Location-Based Activity Recognition (Activity Recognition).
Gene Selection for Cancer Classification using Support Vector Machines (Machine Learning). (Prachi Patel and Sarika Waje)
Gaussian Mixture Language Models for Speech Recognition (Speech Recognition).
Classification and clustering via dictionary learning with structured incoherence and shared features (Machine Learning).
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities (Data Mining).
Effective Attacks and Provable Defenses for Website Fingerprinting (Information Security). (Tejas Shetti and Aayush Sharma)