ECE228 Machine learning for physical applications, Spring 2020

Final Projects 2019 and Spring 2019 class

Professor Peter Gerstoft, Spiess Hall 462, Gerstoft@ucsd.edu
TA Ruixian Liu, rul188@eng.ucsd.edu
TA
Location: Peterson Hall 110
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.


Books (available online): Main book: Goodfellow, Ian, Yoshua Bengio, and Aaron Courville, Deep Learning https://www.deeplearningbook.org/
Other good books: In the past, I have used the Bishop book, some residue will remain
Chris M Bishop Pattern Recognition and Machine Learning . A third party Matlab implementation of many of the algorithms in the book.
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 (10% poster, 10 % code and 30 % Report). Your and my purpose is to lean, so a good effort is sufficient.
Homework 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. We will make teams on ~April 26.
Suggested topics (see last years projects Spring 2019 , Spring 2018, and Spring 2017 ):

Schedule (15 Lectures. poster session and discussion )

Question to lecture 3-4 in Stanford CNN lecture Hastie CH 9
Lecture Date Topic Required reading Assignments
1March 30 Introduction, probability theory [Slides Annotated Slides ]
Bishop CH 1.0-1.2
2April 2 Question to lecture 2 in Stanford CNN lecture Bishop CH 1.5, 1.6, 2.3
3April 6 Bishop CH 2.3, 6.4
Murphy ch15
Homework 1 due 4/22
4April 8 [Slides Annotated Slides]
Question to lecture 5 in Stanford CNN lecture
Bishop CH 3.0-3.1 Homework 2 due 4/22
5April 13 [Slides Annotated Slides]
Question to lecture 6 in Stanford CNN lecture
Bishop CH 3.2-3.3
6April 15 Question to lecture 7 in Stanford CNN lecture
[Slides Annotated Slides]
GPU Jupiter homework relased
due 5/10
7April 20 Question to lecture 8 in Stanford CNN lecture
[Slides Annotated Slides]
Bishop 4.0-4.3.2, 4.3.4, 5.0-5.2 Homework 3 (NN) due 5/5.
Subset of MNIST Data to use for HW3 on you laptop
8April 22 Question to lecture 9 in Stanford CNN lecture

[Slides Annotated Slides]
Bishop CH
9April 27 Question to lecture 10 in Stanford CNN lecture
Christopher Johnson: Machine learning with CNN in seismology.
[Slides Annotated Slides]
Bishop CH 6
10April 29 Question to lecture 11 in Stanford CNN lecture
[Slides Annotated Slides]
Bishop CH 7
May 4 No Class start on Project [Slides Annotated Slides] Bishop CH 9
Homework 5(SVM +Dictionary Learning) due 5/29
May 6 No Class, work on Final project. Mike Bianco: Dictionary leaning Read Bianco's Dictionary learning paper
11May 11 Question to lecture 12 in Stanford CNN lecture
Question to lecture 13 in Stanford CNN lecture
12May 13
13May 18 Question to lecture 13 and 14 in Stanford CNN lecture

[Slides Annotated Slides]
Bishop CH 13
14 May 22
May 27 Project poster presentation, Qualcomm Auditorium and Jacobs Lobby, 5-8 pm Poster Review due June 30
15June 1 Question to lecture 15 in Stanford CNN lecture

[Slides Annotated Slides]
Homework 6 (Kalman Filter and Sparse signal recovery) due 6/10
June 3 Final report due Friday 12 June.

Spring 2019 class

Spring 2018 class

Spring 2017 class