|1|| Reimplementation of source localization in an ocean waveguide using supervised learning
||Jinzhao Feng, Zhuoxi Zeng, Yu Zhang||Poster||Paper|
|2||Machine learning methods for ship detection in satelite images
||Yifan Li, Huadong Zhang, Xiaoshi Li, Quianfeng Guo||Poster||Paper|
|3||Transparent Conductor Prediction
||Yan Sun, Yiyuan Xing, Xufan Xiong, Tianduo Hao||Poster||Paper|
|4||Ship identification in sateklite Images
||Weilun Zhang, Zhaoliang Zheng, Mingchen Mao,||Poster||Paper|
||Eskil Jarslkog, Richard Wang, Joel Andersson||Poster||Paper|
|6|| RSNA Bone Age Prediction
||Juan Camilo Castillo, Yitian Tong, Jiyang Zhao, Fengcan Zhu||Poster||Paper|
|7|| Facial Expression Classification into Emotions
||David Orozco, Christopher Lee, Yevgeniy Arabadzhi, Deval Gupta||Poster||Paper|
|8||Urban Scene Segmentation for Autonomous Vehicles
||Hsiao-Chen Huang, Eddie Tseng, Ping-Chun Chiang, Chih-Yen Lin||Poster||Paper|
|9|| Face Detection Using Deep Learning
||Yu Shen, Kuan-Wei Chen, Yizhou Hao, Min Hsuan Wu||Poster||Paper|
|10|| Understanding the Amazon Rainforest using Neural Networks
||Naveen Dharshana Ketagoda, Christian Jonathan Koguchi, Niral Lalit Pathak, Samuel Sunarjo||Poster||Paper|
|11|| Mercedes-Benz Bench Test Time Estimation
||Lanjihong Ma, Kexiong Wu, Bo Xiao, Zihang Yu||Poster||Paper|
|12|| Vegetation Classification in Hyperspectral Image
||Osman Cihan Kilinc, Kazim Ergun, Yuming Qiao, Fengjunyan Li||Poster||Paper|
|13|| Threat Detection Using AlexNet on TSA scans
||Amartya Bhattacharyya, Christine H Lind, Rahul Shirpurkar||Poster||Paper|
|14|| Flagellates Classification via Transfer Learning
||Eric Ho, Brian Henriquez, Jeffrey Yeung||Poster||Paper|
|15|| Biomedical Image Segmentation
||Lucas Tindall, Amir Persekian, Max Jiao||Poster||Paper|
|16|| “Deep Fakes” using Generative Adversarial Networks (GAN)
||Tianxiang Shen, Ruixian Liu, Ju Bai, Zheng Li||Poster||Paper|
|17|| Dog Breed Classification via Convolutional Neural Network
||Yizhou Chen; Xiaotong Chen; Xuanzhen Xu||Poster||Paper|
|18|| Dog Breed Identification
||Wenting Shi, Jiaquan Chen, Fangyu Liu, Muyun Liu||Poster||Paper|
|19|| Impact of Skewed Distributions on an Automated Plankton Classifier
||Will Chapman, Emal Fatima, William Jenkins, Steven Tien, Shawheen Tosifian||Poster||Paper|
|20|| Blood Cell Detection using Single shot MultiBox Detector
Professor Peter Gerstoft, Spiess Hall 462,
TA Mark Wagner, Spiess Hall, firstname.lastname@example.org
TA Paolo Gabriel, Jacobs Hall, email@example.com
TA Nima Mirzaee firstname.lastname@example.org
Location: CENTR 109
Time: Monday and Wednesday 5-6:20pm
Sylabus: Machine learning has received enormous interest. To learn from data we use probability theory, which has been the mainstay of statistics and engineering for centuries. The class will focus on implementations for physical problems. Topics: Gaussian probabilities, linear models for regression, linear models for classification, neural networks, kernel methods, support vector machines, graphical models, mixture models, sampling methods, sequential estimation. Prerequisites: graduate standing.
It is not a computer science class, so we go slowly through the fundamentals to appreciate the methods, implement these. We will discuss their use in Physical Sciences. In the first part of the class we focus on theory and implementations. We will then transition to focus on machine learning final projects.
|1||April 2||Introduction, probability theory [Slides]
||Bishop CH 1.0-1.2|
|2||April 4|| Gaussian probability theory [Slides, Annotated Slides ]
||Bishop CH 1.5, 1.6, 2.3|
|3||April 9||Non Parametric methods, linear models for Regression [Slides Annotated Slides]
||Bishop CH 2.5, 3.1, 3.2||Homework 1 due before class|
|4||April 11||Linear models for Regression [Slides Annotated Slides]||Bishop CH 3.0-3.3||Homework 2 due before class|
|5||April 16||Sparse models I, Lecture by Santosh Nannuru [Slides Annotated Slides]
Paolo Gabriel (TA): Neural brain control.
After class we install Tensorflow, Python, Jupyter notebook
Read Chapter 2 for an introduction to sparse problems
Murphy 13.1, 13.3, 13.6.1
|Homework 3 due before class|
|6||April 18||Sparse models II, Lecture by Santosh Nannuru [Slides Annotated Slides]
Emma Ozanich: Tracking ships using acoustics. Based on our paper: Niu et al, 2017
|Murphy 13.7, 13.8||
Homework 1 and 2 due 4/20
|7||April 23||Linear models for Classification [Slides Annotated Slides]
||Bishop CH 4.0-4.3.2||Homework 4 due before class|
|8||April 25||Backpropagation [Slides Annotated Slides]
Eric Orenstein: Automatic Analysis of Planktonic Image Data Sets
|Bishop CH 4.3.4, 5.0-5.2|
|9||April 30||Kernel methods [Slides Annotated Slides]
||Bishop CH 6 and from last lecture 5.3 to 5.3.2||Homework 5 due before class|
Support vector machines [Slides Annotated Slides]
||Bishop CH 7|| Homework 6 due before class
|11||May 7||K-means, EM and Mixture models[Slides Annotated Slides]||Bishop CH 9||Project proposals due|
Mike Bianco: Dictionary leaning
|Read Bianco's Dictionary learning paper|| Homework 7 due before class |
Trees, Random Forrest, Gradient boosting> [Slides Annotated Slides]
Hastie CH 9
Bishop CH 8.1, 8.2
|14||May 16||Graphs, maybe PCA [Slides Annotated Slides]||Bishop CH 8.2 12||Homework 8 due before class|
|15||May 21||mid-project presentation of final-projects|
|16||May 23|| Sequential estimation[Slides Annotated Slides]
Jiuyuan Nie and Chongze Hu
"Predict and Design Novel Materials using Machine Learning: in Respect of Experiment and Theory".
In prepapration, Read the following paper
|Bishop CH 13||Homework 9 due before class|
|17||May 30||presentation of final-projects II|
|18||June 4|| Introduction to Machine learning
Nima Mirzaee: Data assimilation
|19||June 6||Final project poster presentation, Jacobs Hall Lobby, 5 pm||Final report due Saturday 16 June.|
Books: Main book: Chris M Bishop Pattern
Recognition and Machine Learning . A third party Matlab implementation of many of the
algorithms in the book.
Other good books:
Hastie and Tibshirani The Elements of Statistical Learning
Kevin P. Murphy: Machine Learning: A Probabilistic Perspective. UCSD license . Matlab codes used in Murphy's book.
Online resources: While not required, I recommend taking these classes. Both
are online classes are excellent.
Statistical Learning by Hastie and Tibshirani. My favorite class.
Andrew Ng's Coursera class, Machine learning. This was the first class offered by Coursera.
Grading: Full scale of the letter grade. Grade consists of About
50 % homework and 50% final-project. Your and my purpose
is to lean, so a good effort is sufficient.
Homework Cody homework will be graded. I'm a strong believer in leaning by doing. Thus we will have computer-based homework each week.
Final project: Propose a topic before May 1. Otherwise it will be based
on my paper: Niu et al, 2017 on arXiv.
We will make teams on April 26. Report due June 16.