Machine Learning & Discovery

ECS 271

Instructor:

Hamed Pirsiavash, Kemper Hall 3061, hpirsiav AT ucdavis DOT edu, Office hours: Thursdays at 2-3pm on Zoom.

TA:

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