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This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or research&development.
In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together.
The first chapter is about backtracking: we will talk about problems such as N-queens problem or hamiltonian cycles and coloring problem. In the second chapter we will talk about dynamic programming, theory first then the concrete examples one by one: fibonacci sequence problem and knapsack problem.
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|Section 1: Introduction|
|Section 2: Backtracking|
N-queens problem introductionPreview
N-queens problem implementationPreview
Hamiltonian cycle introduction
Hamiltonian problem - NP-hard problems
Hamiltonian cycle implementation
Coloring problem introduction
Coloring problem implementation
Knight tour introduction
Knight tour implementation
Maze problem introduction
Maze problem implementation
|Section 3: Dynamic Programming|
Dynamic programming introduction
Fibonacci numbers introduction
Fibonacci numbers implementation
Knapsack problem introduction
Knapsack problem example
Knapsack problem implementation
Coin change problem introduction
Coin change problem implementation
Rod cutting problem introduction
Rod cutting problem implementation
|Section 4: Neural Network Theory|
---------- NEURAL NETWORKS INTRODUCTION ----------
Axons and neurons in the human brain
Modeling human brain
Artificial neurons - the model
Artificial neurons - activations functions
Artificial neurons - an example
Neural networks - the big picture
Applications of neural networks
---------- BACKPROPAGATION ----------
Feedforward neural networks
Optimization - cost function
Simplified feedforward network
Feedforward neural network topology
The learning algorithm
Gradient calculation I - output layer
Gradient calculation II - hidden layer
Applications of neural networks I - character recognition
Applications of neural networks II - stock market forecast
|Section 5: Neural Network Implementation|
|Section 6: Source Code|
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My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.