---
# System prepended metadata

title: 'Meeting #1 (09/11/2022)—Introduction and Timeline'

---

# Meeting #1 (09/11/2022)—Introduction and Timeline
13:00-14:00, B54/9001

## Topics:

0. Introduction
1. Timeline, future meeting arrangements
2. Project topics/ideas
3. Report write-up and presentation
4. After this meeting


## 0. Introduction
...

## 1. Timeline and Arrangement
...

## 2. Project Topics/Ideas

...


## 3. Report Write-Up



Learn LaTeX in 30 minutes
https://www.overleaf.com/learn/latex/Learn_LaTeX_in_30_minutes

List of Math Symbols 
https://www.caam.rice.edu/~heinken/latex/symbols.pdf


Overleaf (for online and collaborating projects) 
http://www.overleaf.com/


R markdown:
Introduction: https://rmarkdown.rstudio.com/articles_intro.html
Cheatsheet: https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

Writting Academic Thesis:
https://www.personal.soton.ac.uk/cz1y20/Teaching/MATH3092/report_write_up.html


## After this meeting

Think about your own project topics and search/read relevant references. We will discuss and finalise your project directions in the next individual meeting.


<!-- ## 4. Unsupervised Learning

Difference machine learning problems: 
* Supervised Learning
* Reinforcement Learning
* Unsupervised Learning


### - Clustering
$K$-Means, density-based clustering, hierarchical clustering, spectral clustering,....

Modern Development: semi-supervised/contrastive learning,....





### - Dimension Reduction/Principal Component Analysis
PCA:
\begin{align}
\text{minimise}\quad &||X-L||_2\\
\text{subject to } \quad &rank(L)\le k,
\end{align}
MDL, random projection,...

Modern Development: sufficient dimension reduction, SIR, robust/sparse PCA, PCA for high-dimensional data, manifold learning,....


### - Applications
Video Surveillance
Face Recognition
Computational Vision
...... -->