Organization

This course is about computer science and artificial intelligence. The main topics are:

• python programming tools and methods (~16h)

• algorithmic (~16h) focussed on graphs and search problems

• deep learning basics (~16h) focussed on forward neural networks with physical informed net applications

• reinforcement learning (~16h) focused on Markov decision processes

The goal is to give you the fundamentals to build on, these subjects can not be covered in depth in only few hours.

Schedule

TOOLS & METHODS	session	duration
course ojectives, general information	1	0.5
uv		0.5
vscode		1
basics		2
profiling	2	1
testing		2
useful modules		1
useful modules	3	2
type hints		1
git		1
ALGORITHMS	session	duration
complexity	4	1
recursion		3
recursion	5	1
divide & conquer		3
dynamic programming	6	4
complexity	7	1
graphs		3
ARTIFICIAL INTELLIGENCE	session	duration
neural networks	8-9	8
physics informed neural networks	10-11	8
state models	12	4
Markov decision processes	13	4
reinforcement learning	14-15	8
OTHER	session	duration
projects	1st 1/2 group	4
documentation + lose ends	2nd 1/2 group	4
student presentations	16	4

In addition to all the non listed demos, below the exercise list (few at the beginning, a lot later on):

TOOLS & METHODS

Python built-in types

• student grade data type

• project group data type

Functions

• bank account function

Profiling

• profiling data size

• memory usage

Testing:

• fixture

• project assignment app

• test assignment algorithm

• type of testing

Useful modules:

• decorator

• wraps

• cache

• defaultdict

• heapq

• itertools

Type hints:

• abstract type first

• abstract type second

• abstract type third

Protocols:

• protocol

Aliases:

• type alias first

• type alias second

• new type

ALGORITHMIC

Complexity:

• Matrix product complexity

• Check uniqueness complexity

• Upper triangular system solving complexity

• Fairest assignment complexity

Recursion:

• Factorial

• Horner polynomial evaluation

• Laplace determinant

• Permutations

• Cartesian product

Divide & conquer:

• Quicksort

• Merge sort

• Bisection insertion

Dynamic programming:

• Length of the longest common subsequence

• Longest non-decreasing subsequence

Graphs:

• Graph structure

• Graph transpose

• Graph traversal

• Graph Depth First recursive and iterative

• Graph Breadth First and Uniform Cost

• Graph Uniform Cost Search

• Shortest path in DAG

• Topological order

ARTIFICIAL INTELLIGENCE

State models:

• Magic tram state problem

• Swamp isle state problem

Markov decision processes:

• MDP graph (game)

• MDP types/structures

• Expectation

• MDP value (game)

• MDP policy evaluation (game)

• MDP value iteration (game)

• MDP example game

• MDP example magic tram

• MDP example swamp isle