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Course page for Scientific Programming 2015

Course plan

Computational thinking

If your tool is a computer, everything begins to look like a list of numbers.

  • Recap of the syntax of Python.
  • How to start solving a problem using an algorithm.
  • Thinking about algorithms in terms of how many operations they require and how much memory they use.
  • Pancakes!

Descriptive statistics

Data is the new bacon.

  • How to organize a dataset.
  • Thinking about instances and variables.
  • Grouping data.
  • Intuition about significance testing

Homework I out.

Visualizing data

The eye is by far our most powerful tool of analysis.

  • What are the different types of plots, and what can you use them for?
  • Matplotlib essentials (= one hour of headaches).
  • Tufte's information density theory of plotting.
  • Plotting and cognition.

Linear algebra and numerical computing

You find yourself enrolled in a program to become a master of matrices.

  • How computers represent numbers.
  • Basic operations on vectors and matrices.

Homework I due.

Supervised learning I

Why computers can (theoretically) learn anything.

  • How is learning possible?
  • Smoke detectors and the linear model.
  • Implement the perceptron, a simple learning classification algorithm.

Homework II out.

Supervised learning II

Build a better perceptron

  • Probabalistic models: Logistic regression.
  • Energy-based learning, a general model.

The environment

Ninjas dress in black to hide in the background of your terminal window.

  • Files and the file system.
  • The philosophy of Unix.
  • Writing Python scripts and executing them in the shell

Homework II due.

Images I

Bob Ross wants to remind you that there are no mistakes in painting, only happy accidents.

  • Painting with numbers.
  • Morphological operations.

Homework III out.

Images II

How to recognize trees from quite far away.

  • Identifying and labeling parts of images.
  • Going from continous coordinate spaces to pixel spaces.

Graphs I

Graphs are everywhere!

  • Two strategies of unguided graph search: depth-first, breadth-first.
  • Using heuristics to cut corners: A*-search.
  • Graph search and finding your way on a map.

Homework III due.

Graphs II

  • Graphs as Markov chains.
  • Random walks, or what a drunken walk can find out.
  • Analysis of social graph data sets: data from social networks.

Homework IV out.

Information Retrieval

How can Google find anything in no time?

  • The inverted index data structure.
  • Auto-completion using the Trie data structure.

Neural networks I

Perceptrons are back, and they brought their friends.

  • Feed-forward neural networks.
  • Building blocks: layers, activation functions.
  • Back-propagation.

Homework IV due.

Neural networks II

  • Clever hacks using neural networks: auto-encoders, word2vec.

Homework V out.