Dr. Sergey Kosov
University Lecturer and Entrepreneur
Machine Learning
Dr. Sergey Kosov
Your Teacher
University Lecturer Data Engineering
Computer Science & Electrical Engineering
Dr. Sergey Kosov
What is Machine Learning?
(Machine) Learning is any process by which a system improves performance from experience
-Herbert Simon
Herbert Alexander Simon
June 1916 - Feb. 2001
An economist, political scientist and cognitive psychologist
Turing Award 1975
Nobel Prize in Economics 1978
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Why Machine Learning?
- Develop systems that can automatically adapt and customise themselves to individual users
- Discover new knowledge from large databases (data mining)
- Ability to mimic human and replace certain monotonous tasks - which require some intelligence
- Develop systems that are too difficult / expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck)
Why Now?
-
Flood of available data (especially with the advent of the Internet)
-
Increasing computational power
-
Growing progress in available algorithms and theory developed by researchers
-
Increasing support from industries
General Information
up to 28 Lectures
up to 14 Practical Tutorials
up to 7 Home assignments
MACHINE LEARNING COURSE
by teaching assistants
Voluntary
Mandatory elective course for M.Sc. Data Engineering students
MAIN FOCUS
Modern supervised machine learning techniques:
- Generative and Discriminative classification models
- Probabilistic Graphical models
ORIENTATION
The course is highly oriented to practical applications:
- Study of main software libraries: OpenCV, DGM and TensorFlow
- Development of own machine learning techniques using C++ and/or Python
- Study of the underlying optimisation algorithms
Introduction to main modern Machine Learning techniques with examples
Supervised Learning
Unsupervised Learning
Reinforcement Learning
History of Machine Learning
PROBABILISTIC GRAPHICAL MODELS
DISCRIMINATIVE MODELS
GENERATIVE MODELS
INTRODUCTION
Repeat the basics of Probability Theory
e.g. product rule, Bayes law, etc.
4 Lectures
PROBABILISTIC GRAPHICAL MODELS
DISCRIMINATIVE MODELS
GENERATIVE MODELS
INTRODUCTION
p(a|b) = \frac{p(b|a)\cdot p(a)}{p(b)}
Repeat the basics of Probability Theory
e.g. product rule, Bayes law, etc.
4 Lectures
Introduction to main modern Machine Learning techniques with examples
Supervised Learning
Unsupervised Learning
Reinforcement Learning
History of Machine Learning
PROBABILISTIC GRAPHICAL MODELS
DISCRIMINATIVE MODELS
GENERATIVE MODELS
INTRODUCTION
Theory
- Bayes Model
- Gaussian Model
- Gaussian Mixture Model (GMM)
Repeat The Math
- Mutivariate Gaussian Distribution
6 Lectures & 4 Tutorials
Practice
- Maximum Likelihood algorithm
- Sequential algorithm for GMM parameters estimation
PROBABILISTIC GRAPHICAL MODELS
DISCRIMINATIVE MODELS
GENERATIVE MODELS
INTRODUCTION
13 Lectures & 7 Tutorials
Theory
- k-Nearest-Neighbours
- Support Vector Machines
- Decision Trees & Random Forests
- Artificial Neural Networks
- Perceptron
- Deep Neural Networks
Practice
- KD-Trees data structure
- Reservoir sampling algorithm
- Backpropagation algorithm
PROBABILISTIC GRAPHICAL MODELS
DISCRIMINATIVE MODELS
GENERATIVE MODELS
INTRODUCTION
5 Lectures & 3 Tutorials
Theory
- Introduction to Graphical Models
- Markov Random Fields
- Conditional Random Fields
Application
- Image semantic segmentation
- Optical Flow reconstruction
- Sequential data classification
Practical Assignments
Build your own Machine Learning algorithms with DGM library
Practical Assignments
Build your own Machine Learning algorithms with DGM library
DIRECT GRAPHICAL MODELS
A cross-platform open-source C++ library, which accompanies the course materials and may serve as a basis for your future projects
Practical Assignments
Build your own Machine Learning algorithms with DGM library
DIRECT GRAPHICAL MODELS
A cross-platform open-source C++ library, which accompanies the course materials and may serve as a basis for your future projects
int main()
{
auto nodeTrainer = CTrainNode::create(Bayes, nStates, nFeatures);
auto graphKit = CGraphKit::create(GraphType::dense, nStates);
CMarker marker(DEF_PALETTE_6);
CCMat confMat(nStates);
// ========================= Training =========================
nodeTrainer->addFeatureVecs(train_fv, train_gt);
nodeTrainer->train();
// ====================== Filling the Graph ===================
Mat nodePotentials = nodeTrainer->getNodePotentials(test_fv);
graphKit->getGraphExt().setGraph(nodePotentials);
graphKit->getGraphExt().addDefaultEdgesModel(100.0f, 3.0f);
graphKit->getGraphExt().addDefaultEdgesModel(test_fv, 300.0f, 10.0f);
// ========================= Decoding =========================
vec_byte_t optimalDecoding = graphKit->getInfer().decode(100);
// ======================== Evaluation ========================
Mat solution(imgSize, CV_8UC1, optimalDecoding.data());
confMat.estimate(test_gt, solution);
char str[255];
sprintf(str, "Accuracy = %.2f%%", confMat.getAccuracy());
printf("%s\n", str);
return 0;
}Practical Assignments
Build your own Machine Learning algorithms with DGM library
DIRECT GRAPHICAL MODELS
A cross-platform open-source C++ library, which accompanies the course materials and may serve as a basis for your future projects
After the Course
Win Kaggle Competitions
Choose a challenging problem at Kaggle.com
Apply Conditional Random Fields to that dataset
Get the best prediction accuracy
After the Course
Win Kaggle Competitions
Apply For a PhD Study
Write you master thesis on one of the Machine Learning topics
Continue study with PhD in Machine Learning in Jacobs or one of another leading Universities of the world
Make career in computer science
After the Course
Apply For a PhD Study
Apply For a Data Scientist Job
Start you career in one of the cutting-edge industrial companies like Microsoft, Amazon, Google, etc.
Make career in industry
Win Kaggle Competitions
After the Course
Apply For a PhD Study
Apply For a Data Scientist Job
Win Kaggle Competitions
Q&A
University Lecturer Data Engineering
Computer Science & Electrical Engineering
Dr. Sergey Kosov
Jacobs University Bremen gGmbH
Campus Ring 1 | 28759 Bremen | Germany
Machine Learning Course
By Dr. Sergey Kosov
Machine Learning Course
Introduction the the Machine Learning course for Master students at Jacobs University
