Applied Machine Learning - Week 4
Monday the 17th - Friday the 21rd of May 2021
Monday 17th of May (afternoon):
Lectures:
Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Backpropagation (TP).
    
The ImageNet Competition - a revolution in ML (TP).
Zoom:
Link to lecture.
     Recording in
Lecture video I (222 MB),
Lecture video II (76 MB), and
Lecture chat (2 kB).
Exercise: Exercise: Using an RNN/LSTM/GRU, predict the next entries in a sinus (periodic) and Mackay (non-periodic) sequence:
SequenceTraining.ipynb.
     Once you become familiar with LSTMs, try to make
FlightPassengerPredictions.ipynb on
1949-1961 airline data.
     Also, the exercise will be used for coordination/group discussion of
final project.
Wednesday 19th of May (morning):
Lectures: An introduction to
Convolutional Neural Networks (Aleksandar Topic).
    
Very cool visualisation of how a CNN works.
Zoom:
Link to lecture (Recorded!).
     Recording in
Lecture video (112 MB).
Exercise: See if you can recognise handwritten numbers with a Convolutional Neural Network:
CNN_MNISTdata.ipynb.
     The data can (also) be obtained from (famous) Yann LeCun's webpage:
Link to MNIST dataset.
Wednesday 19th of May (afternoon):
Lectures: Graph Neural Networks - analysing geometric data (Rasmus Oersoe).
Zoom:
Link to lecture (Recorded!).
     Recording in
Lecture video (78 MB).
Exercise: We will use the afternoon to work on the
Small project, which is due by Monday the 24th of May.
Housing cleaning, clustering, and estimating exercise:
The housing data consists of about 50000 housing sales, where 90+ features are provided along with the actual sales price.
The original data
HousingPrices_Org.csv (21 MB) first needs cleaning.
Following cleaning, one can add features (here GPS coordinates and distance to sea) using the additional data files:
GPS_data.csv (1.3 MB)
SEA_DIST.csv (680 kB)
Example code for doing this can be found here:
RegressionOnHousing_Clean_Data.py (3.7 kB)
RegressionOnHousing_Feature_Adding.py (11 kB)
An example of the resulting data file can be found here:
HousingPrices_Cleaned.csv (8.8 MB),
and example code for actual sales price estimates here:
RegressionOnHousing.py (34 kB).
Last updated: 10th of May 2021 by Troels Petersen.