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This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market.
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 regression: very easy yet very powerful and widely used machine learning technique. We will talk about Naive Bayes classification and tree based algorithms such as decision trees and random forests. These are more sophisticated algorithms, sometimes works, sometimes not. The last chapters will be about SVM and Neural Networks: the most important approaches in machine learning.
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|Section 1: Introduction|
Introduction to machine learningPreview
|Section 2: Regression|
Linear regression introductionPreview
Linear regression example
Logistic regression introduction
Logistic regression example I - sigmoid function
Logistic regression example II
Logistic regression example III - credit scoring
|Section 3: K-Nearest Neighbor Classifier|
K-nearest neighbor introduction
K-nearest neighbor introduction - normalize data
K-nearest neighbor example I
K-nearest neighbor example II
|Section 4: Naive Bayes Classifier|
Naive Bayes introduction
Naive Bayes example I
Naive Bayes example II - text clustering
|Section 5: Support Vector Machine (SVM)|
Support vector machine introduction
Support vector machine example I
Support vector machine example II - character recognition
|Section 6: Tree Based Algorithms|
Decision trees introduction
Decision trees example I
Decision trees example II - iris data
Pruning and bagging
Random forests introduction
Random forests example I
Random forests example II - enhance decision trees
|Section 7: Clustering|
Principal component anlysis introduction
Principal component analysis example
K-means clustering introduction
K-means clustering example
Hierarchical clustering introduction
Hierarchical clustering example
|Section 8: Neural Networks|
Neural network introduction
Feedfordward neural networks
Training a neural network
Applications of neural networks
Neural network example I - XOR problem
Neural network example II - face recognition
|Section 9: Face Detection|
Face detection introduction
Tuning the parameters
|Section 10: Outro|
|Section 11: Source Code & Data|
Source code & CSV files
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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.