Content
We post a youtube video each week and you ask your questions in Q&A sessions (link see below). These are the dates for the sessions:
02 Nov 2020 - Introduction, notation, k-nearest neighbors
09 Nov 2020 - Clustering (kMeans, DBSCAN)
16 Nov 2020 - Linear and logistic regression
23 Nov 2020 - Model validation
30 Nov 2020 - The covariance matrix, PCA
07 Dec 2020 - Bagging, decision trees, random forests
14 Dec 2020 - Boosting (AdaBoost), Viola-Jones
21 Dec 2020* - Perceptron, multi-layer perceptron
11 Jan 2021 - Gradient Descent, Backprop, Optimizers (SGD, Adam, RProp)
18 Jan 2021 - ConvNets
25 Jan 2021 - Unsupervised representation learning I (VAEs, Glow)
01 Feb 2021 - Unsupervised representation learning II (GANs)
08 Feb 2021 - RNNs
15 Feb 2021 - Attention, Transformers
22 Feb 2021 - Attribution, Adversarial Examples (BONUS)
* optional Q&A session (Christmas holidays!)
Video Lectures on YouTube
https://youtube.com/playlist?list=PLs7Vp-pCDX7yu38RbJfuyMUrFZ5877uh1
Literature
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
http://www.deeplearningbook.org/
WebEx-Rooms
Discord Server
Eduflow
Hand in your assignments here (Create a group of two/three people, see discord):
https://app.eduflow.com/join/D7F5CD
Guideline for Reviews
Prerequisites
Basics in linear algebra, algorithms and data structures.