Machine Learning & Discovery
Hamed Pirsiavash, Kemper Hall 3061, hpirsiav AT ucdavis DOT edu, Office hours: Thursdays at 2-3pm on Zoom.
Parsa Nooralinejad, pnooralinejadeslamloo AT ucdavis DOT edu, Office hours: Mondays and Friday 3-4:30, Location: TBD
There will be 3-4 homeworks: 70% total.
There will be a major final project: 30%.
There "may" be a final exam. If so the grade will be distributed differently: homework: 60%, project: 30%, exam: 10%
Each student has five penalty-free late days for the whole semester that can be used for the homework only. Any extra late day will be penalized.
The homeworks will involve both written problem sets and MATLAB or Python coding.
1: A Course in Machine Learning by Hal Daume III (Main)
2: Machine Learning: A Probabilistic Perspective by Kevin Murphy
3: Pattern Recognition and Machine Learning by Christopher M. Bishop
Machine learning course by Andrew Ng
Decision trees by Martial Hebert
CNNs by Svetlana Lazebnik
09/22 : Introduction (slides)
09/27 : Decision trees (slides)
09/29 : Decision trees
10/04 : K-nearest neighbors (slides)
10/06 : Perceptron (slides)
10/11 : Linear models (slides)
10/13 : No Class
10/18 : Linear models
10/20 : Linear models
10/25 : Neural networks (slides)
10/27 : Neural networks
11/01 : Convolutional neural networks and deep learning (slides) (slides)
11/03 : Convolutional neural networks and deep learning
11/08 : Support Vector Machines (slides included in linear models)
11/10 : Support Vector Machines
11/15 : Clustering (slides)
11/17 : Dimensionality reduction (slides)
11/22 : Probabilistic modeling (slides)
11/24 : Thanksgiving Holiday
11/29 : Self-supervised deep learning (slides)
12/01 : Generative adversarial networks (GANs) (slides)
Homework 1, due 10/13/2022 at 11:59pm: pdf, tex, Matlab code, Python code, data, mydefs.sty, notes.sty
Homework 2, due 11/14/2022 at 11:59pm: pdf, tex
Homework 3, due 12/01/2022 at 11:59pm: pdf, tex