About Markus

• Originally from Austria

About Markus

About Markus and Marcela

About You

• Name, Program?

• Games, Communities, Interests?

• Fun facts?

Class Resources

• Website: http://bit.ly/PF-3115

Project

• In addition to the labs, you will work on a semester-long project of your choice

• Work in groups of two: One statistician and one computer scientist

• We will provide several ideas for project, but feel free to propose your own

• Important: We encourage creative ideas, but the more "interesting" your proposal, the more important it is to coordinate it with us beforehand

Project

• 31/3: Proposal: 10 mins presentation and a document.

• 28/4: Update 1: 7 mins presentation and a document.

• 26/5: Update 2: 3 mins presentation and a document.

• 23/6: Q&A

• 30/6: Presentations: 15 mins presentation and a document.

Project Ideas

• I (Markus) have about 16000 game logs from Hearthstone games (and a python script to download more)

• Games come tagged with deck type, player name, winner, and some other information

• Ideas: Analyze deck type development over time, predict winner after n turns, etc.

Project Ideas

• Collect data from a game (like PUBG, Fortnite): Game logs, network traffic, maybe CPU/GPU usage

• There are people that say that the game starts to lag when someone is coming from behind them

• Investigate if there is a correlation

Project Ideas

• The Smithsonian recently made 3 Million pieces of art digitally available

• For example, there is a collection of stamps

• We could classify stamps by type, year, price, etc.

• Or we could use a GAN to generate more stamps of a particular type

Textbook

• The 100-page ML Book, Burkov

• Deep Learning with PyTorch, Stevens et al

• Elements of Statistical Learning, Hastie et al

• If you are completely lost with Python, you might want to start here

Definitions - Brainstorming

Artificial Intelligence | Machine Learning | Statistics | Data Science | Computer Science

Source: https://jamesmccaffrey.wordpress.com/2016/09/29/machine-learning-data-science-and-statistics/machinelearningdatasciencestatistics/

Why This Course?

Observe

Machine Learning

• Supervised Learning: $\{(x_1, y_1), . . . ,(x_n, y_n)\}$ Learn a mapping from examples.

• Unsupervised Learning: $\{x_1, . . . , x_m\}$ Learn an interesting thing about data.

• Semi-supervised Learning: $\{(x_1, y_1), . . . ,(x_n, y_n)\} \cup \{x_1, . . . , x_m\}$

• Reinforcement Learning: Learn what to do in an environment, given feedback information.

We will cover some algorithms from each of these areas in this course!

Machine Learning

Say there is a function $f(\vec{x}) = y$

• Supervised Learning: We know x and y, and are trying to find f

• Unsupervised Learning: We know x and are trying to find "interesting" f and y

• Reinforcement Learning: We know f*, and are trying to get "the best y" by choosing x

*: Terms and Conditions may apply

Review of Statistical Learning

http://www.mit.edu/~9.520/fall18/slides/class02_SLT.pdf

Python: How to start

• To develop in Python, use your favorite text editor

• Save the file with the extension ".py"

• Then you can run the file from the command line with python <filename>.py

• There are some ways to make this more "comfortable"

PyCharm

• PyCharm is an IDE for Python

• You can write your code, as with any other editor

• To run it, you can use a button in the top right corner that will open a console on the bottom and run your code

• You will need to tell PyCharm which file to run, though

Python: Hello World

import sys
def main(who):
    print("Hello", who)
    for i in range(100):
        if (i+1)%10 == 0:
            print("Iteration %d"%(i+1))
if __name__ == "__main__":
    if len(sys.argv) > 1:
        main(sys.argv[1])
    else:
        main("World")

Save as helloworld.py, run with:

python helloworld.py Universe

Python at Home

• Install Python 3.7 or 3.8, 64 bit from https://www.python.org/

• You can install additional packages using the command line with pip install <packagename>

• For example, numpy can be installed with pip install numpy

• If you don't have administrator rights, use pip install --user numpy

• pip will automatically install all dependencies

PyTorch

• PyTorch is a python library for Machine Learning

• At every major Machine Learning conference last year, the majority of papers used PyTorch

• The other "big one" is Tensorflow, which is a bit older and has therefore more adoption in industry

• PyTorch is easier to get started with, and once you know the core concepts you can easily pick up Tensorflow, too

PyTorch vs. Tensorflow

Tensors in PyTorch

Let x be a tensor with shape [12,2]

• x[0,0] is the first element

• x[0] (or x[0,:]) is the first row (a tensor of shape [2])

• x[:,0] is the first column (a tensor of shape [12])

• x.T is a tensor with shape [2,12] (the transpose)

Tensors in PyTorch

Let x be a tensor with shape [24]

• x > 0 produces a tensor that tells you for each element if it is greater than 0 or not

• x[x > 0] produces a tensor with all values that are greater than 0

• x[labels == "C"] produces a tensor with all x values which have label "C" (labels has to be a tensor containing the labels for each entry in x)

Note: In many cases our x will have more than one dimension. x[labels == "C",:] does the same for tensors with dimensionality 2, etc.

Numpy and PyTorch

• A very popular library for linear algebra in Python is numpy

• PyTorch interacts very nicely with numpy

• You can convert a tensor x to a numpy array with x.numpy()

• You can convert a numpy array y to a torch tensor with torch.from_numpy(y)

Pandas

• Pandas is a library for data manipulation and analysis

• It is particularly nice for reading data from a variety of formats

• R programmers will also like the dataframes it provides (which are similar to the ones in R)

• Is built on top of numpy, and therefore plays nicely with PyTorch

An Example

import pandas as pd
import numpy as np
import torch
import sys
def read_csv(fname, colx, coly):
    data = pd.read_csv(fname)
    x = torch.tensor(data[colx])
    y = torch.tensor(data[coly])
    return x,y
def main(datafile):
    x,y = read_csv(datafile, "ActionLatency", "APM")
    print("mean x: %.2f, max y: %.2f"%(x.mean(), y.max()))
    ymax,ymaxat = y.max(0)
    print("max y: %.2f at index %d"%(ymax, ymaxat))
    print(x.shape, x[x > 125]) 
    import pdb; pdb.set_trace()
if __name__ == "__main__":
    main(sys.argv[1])

Squeeze

Squeeze is used to remove one/all dimension(s) of size 1:

• If x.shape is [12,2], x.squeeze() does nothing

• If x.shape is [24,1], x.squeeze() produces a tensor of shape [24]

• If x.shape is [24,1,1], x.squeeze() produces a tensor of shape [24]

• If x.shape is [24,1,1], x.squeeze(1) produces a tensor of shape [24,1]

Unsqueeze

Unsqueeze is used to insert a dimension of size 1:

• If x.shape is [12,2], x.unsqueeze(0) produces a tensor of shape [1,12,2]

• If x.shape is [12,2], x.unsqueeze(1) produces a tensor of shape [12,1,2]

• If x.shape is [12,2], x.unsqueeze(2) produces a tensor of shape [12,2,1]

Data Types

• Torch is a bit picky about data types (for optimization reasons)

• Each tensor has a data type associated with it

• If you have an integer tensor x, you can get a floating point tensor with x.float()

• You can also specify an extra parameter dtype=torch.float64 for torch.tensor(...)

CUDA

• CUDA ("Compute Unified Device Architecture") allows you to program your (Nvidia) graphics card

• Computer graphics needs many vector operations in parallel, which the GPU can perform

• Someone noticed that performing many vector operations in parallel is useful in other contexts as well

Matplotlib and Seaborn

• To make plots with python, we use matplotlib (and seaborn for prettier graphs)

• The usual convention is to import matplotlib.pyplot as plt and import seaborn as sns

• plt has many different plot types available, and you can manipulate axis, labels, etc.

• sns integrates into that by adding more plot types (and nicer colors, styles, etc.)

Matplotlib Scatterplot

x = [1,2,3,4,5]
y = torch.randn(5)
# line plot
plt.figure()
plt.plot(x,y) 
plt.show()
# scatter plot and line in the same figure
plt.figure()
plt.scatter(x,y)
plt.plot(x,y)
plt.savefig("scatterplot.png")
# Hexbin plot with marginal distributions
plt.figure()
seaborn.jointplot(x,y,kind="hex")
plt.show()

Hexbin Plots

References

• Starting with Pandas and Numpy

• Numpy, Pandas, and Matplotlib

• CUDA Toolkit (Requires an Nvidia card)

• Matplotlib Sample Gallery

• Seaborn Sample Gallery