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 
HsiaoChen Huang, Eddie Tseng, PingChun Chiang, ChihYen Lin  Poster  Paper 
9  Face Detection Using Deep Learning 
Yu Shen, KuanWei 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  MercedesBenz 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,
Gerstoft@ucsd.edu
TA Mark Wagner, Spiess Hall, m2wagner@eng.ucsd.edu
TA Paolo Gabriel, Jacobs Hall, pgabriel@eng.ucsd.edu
TA Nima Mirzaee nmirzaee@eng.ucsd.edu
Location: CENTR 109
Time: Monday and Wednesday 56:20pm
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 
1  April 2  Introduction, probability theory [Slides] 
Bishop CH 1.01.2  
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 3.03.3  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 4.04.3.2  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, 5.05.2  
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 
10  May 2 
Support vector machines [Slides Annotated Slides] 
Bishop CH 7  Homework 6 due before class solution 
11  May 7  Kmeans, EM and Mixture models[Slides Annotated Slides]  Bishop CH 9  Project proposals due 
12  May 9 
Mike Bianco: Dictionary leaning
Trees 
Read Bianco's Dictionary learning paper  Homework 7 due before class Solution 
13  May 14 
Trees, Random Forrest, Gradient boosting> [Slides Annotated Slides]
Bayes net 
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  midproject presentation of finalprojects  
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 finalprojects 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% finalproject. 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 computerbased 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: