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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
COMP5212
, , 1900
Machine Learning
COMP5212
, , 1900
| Slides | Date | Topic | Readings | Assignments |
|---|---|---|---|---|
| Lecture 0 | 31/01 Wed | Introduction | ||
| Lecture 1 | 02/02 Fri | Math basics | ||
| Lecture 2 | 07/02 Wed | Supervised learning basics | ||
| Lecture 3 | 09/02 Fri | Logistic regression | ||
| Lecture 4 | 14/02 Wed | Generalized linear models, classification | ||
| Lecture 5 | 16/02 Fri | Kernel methods | ||
| Lecture 6 | 21/02 Wed | SVM | ||
| Lecture 7 | 23/02 Fri | Naive Bayes | ||
| Lecture 8 | 28/02 Wed | MLE, MAP | ||
| Lecture 9 | 01/03 Fri | Gradient descent, SGD, Newton’s method | ||
| Lecture 10 | 06/03 Wed | Generalization, bias-variance tradeoff | ||
| Lecture 11 | 08/03 Fri | Clustering | ||
| Lecture 12 | 13/03 Wed | Expectation Maximization | ||
| Lecture 13 | 15/03 Fri | PCA/ICA | ||
| Lecture 14 | 20/03 Wed | mid-term exam | ||
| Lecture 15 | 22/03 Fri | Probabilistic Graphical Models | ||
| Lecture 16 | 27/03 Wed | HMM | ||
| 29/03 Fri | Good Friday holiday | |||
| 03/04 Wed | mid-term break | |||
| 05/04 Fri | mid-term break | |||
| Lecture 17 | 10/04 Wed | Neural Networks, backprop | ||
| Lecture 18 | 12/04 Fri | Neural Networks, architectures | ||
| Lecture 19 | 17/04 Wed | Neural architectures | ||
| Lecture 20 | 19/04 Fri | Variational autoencoder | ||
| Lecture 21 | 24/04 Wed | Generative adversarial networks | ||
| Lecture 22 | 26/04 Fri | Reinforcement Learning | ||
| 01/05 Wed | Labor day | |||
| Lecture 23 | 03/05 Fri | Languge models | ||
| Lecture 24 | 08/05 Wed | Pretraining | ||
| Lecture 25 | 10/05 Fri | Large language models |
COMP5212
, , 1900
Machine Learning
COMP5212
, , 1900
| Slides | Date | Topic | Readings | Assignments |
|---|---|---|---|---|
| Lecture 0 | 31/01 Wed | Introduction | ||
| Lecture 1 | 02/02 Fri | Math basics | ||
| Lecture 2 | 07/02 Wed | Supervised learning basics | ||
| Lecture 3 | 09/02 Fri | Logistic regression | ||
| Lecture 4 | 14/02 Wed | Generalized linear models, classification | ||
| Lecture 5 | 16/02 Fri | Kernel methods | ||
| Lecture 6 | 21/02 Wed | SVM | ||
| Lecture 7 | 23/02 Fri | Naive Bayes | ||
| Lecture 8 | 28/02 Wed | MLE, MAP | ||
| Lecture 9 | 01/03 Fri | Gradient descent, SGD, Newton’s method | ||
| Lecture 10 | 06/03 Wed | Generalization, bias-variance tradeoff | ||
| Lecture 11 | 08/03 Fri | Clustering | ||
| Lecture 12 | 13/03 Wed | Expectation Maximization | ||
| Lecture 13 | 15/03 Fri | PCA/ICA | ||
| Lecture 14 | 20/03 Wed | mid-term exam | ||
| Lecture 15 | 22/03 Fri | Probabilistic Graphical Models | ||
| Lecture 16 | 27/03 Wed | HMM | ||
| 29/03 Fri | Good Friday holiday | |||
| 03/04 Wed | mid-term break | |||
| 05/04 Fri | mid-term break | |||
| Lecture 17 | 10/04 Wed | Neural Networks, backprop | ||
| Lecture 18 | 12/04 Fri | Neural Networks, architectures | ||
| Lecture 19 | 17/04 Wed | Neural architectures | ||
| Lecture 20 | 19/04 Fri | Variational autoencoder | ||
| Lecture 21 | 24/04 Wed | Generative adversarial networks | ||
| Lecture 22 | 26/04 Fri | Reinforcement Learning | ||
| 01/05 Wed | Labor day | |||
| Lecture 23 | 03/05 Fri | Languge models | ||
| Lecture 24 | 08/05 Wed | Pretraining | ||
| Lecture 25 | 10/05 Fri | Large language models |