ECE228 and SIO209 Machine learning for physical applications, Spring 2018

Final Projects

Group Topic Authors Poster Report
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
5 Fruit Recognition
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
Inyoung Huh Poster Paper

Professor Peter Gerstoft, Spiess Hall 462,
TA Mark Wagner, Spiess Hall,
TA Paolo Gabriel, Jacobs Hall,
TA Nima Mirzaee
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.


Lecture Date Topic Required reading Assignments
1April 2 Introduction, probability theory [Slides]
Bishop CH 1.0-1.2
2April 4 Gaussian probability theory [Slides, Annotated Slides ]
Bishop CH 1.5, 1.6, 2.3
3April 9 Non Parametric methods, linear models for Regression [Slides Annotated Slides]
Bishop CH 2.5, 3.1, 3.2 Homework 1 due before class
4April 11 Linear models for Regression [Slides Annotated Slides]
Bishop CH 3.0-3.3 Homework 2 due before class
5April 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
6April 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
7April 23 Linear models for Classification [Slides Annotated Slides]
Bishop CH 4.0-4.3.2 Homework 4 due before class
8April 25 Backpropagation [Slides Annotated Slides]
Eric Orenstein: Automatic Analysis of Planktonic Image Data Sets
Bishop CH 4.3.4, 5.0-5.2
9April 30 Kernel methods [Slides Annotated Slides]
Bishop CH 6 and from last lecture 5.3 to 5.3.2 Homework 5 due before class
10May 2 Support vector machines [Slides Annotated Slides]
Bishop CH 7 Homework 6 due before class
11May 7 K-means, EM and Mixture models[Slides Annotated Slides] Bishop CH 9 Project proposals due
12May 9 Mike Bianco: Dictionary leaning
Read Bianco's Dictionary learning paper Homework 7 due before class
13May 14 Trees, Random Forrest, Gradient boosting> [Slides Annotated Slides]
Bayes net
Hastie CH 9
Bishop CH 8.1, 8.2
14May 16 Graphs, maybe PCA [Slides Annotated Slides]
Bishop CH 8.2 12 Homework 8 due before class
15May 21 mid-project presentation of final-projects
16May 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
17May 30 presentation of final-projects II
18June 4 Introduction to Machine learning
Nima Mirzaee: Data assimilation
19June 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.
Suggested topics:

  1. Tracking ships using acoustics. Based on my paper: Niu et al, 2017 on arXiv. This can be solved in TensorFlow or SciKit-learn (in Python) or matlab . Data and SVM example
  2. Graph signal processing for localizing small events. Based on my paper Riahi 2017.
  3. Classifying plankton. This would be based on Jules Jaffe's underwater microscope. This might require convolutional networks, but random forest and support vector machines would also work.
  4. Identifying earthquakes from data on a mobile phone (example is in ipython). Extensive example with 3 Gb of data. See background paper in Science

Spring 2017 class