Szkolenia Sieci Neuronowe w Gorzów Wielkopolski

Sieci Neuronowe Training in Gorzów Wielkopolski
Neural Networks courses

Opinie uczestników

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease - Knowledgepool Group Ltd

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq - Tricentis GmbH

Introduction to the use of neural networks

the interactive part, tailored to our specific needs

Thomas Stocker - Tricentis GmbH

From Data to Decision with Big Data and Predictive Analytics

zakres materialu

Maciej Jonczyk - Orange Polska

From Data to Decision with Big Data and Predictive Analytics

usystematyzowanie wiedzy z dziedziny ML

- Orange Polska

Applied Machine Learning

ref material to use later was very good

PAUL BEALES - Seagate Technology

Introduction to Deep Learning

The topic is very interesting

Wojciech Baranowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training

Grzegorz Mianowski - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Topic. Very interesting!

Piotr - Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

- Dolby Poland Sp. z o.o.

Introduction to Deep Learning

Interesting subject

Wojciech Wilk - Dolby Poland Sp. z o.o.

Sieci Neuronowe Course Events - Gorzów Wielkopolski

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Plany Kursów

Kod Nazwa Czas trwania Spis treści
mlintro Introduction to Machine Learning 7 hours

This training course is for people that would like to apply basic Machine Learning techniques in practical applications.


Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work

Sector specific examples are used to make the training relevant to the audience.

  • Naive Bayes
  • Multinomial models
  • Bayesian categorical data analysis
  • Discriminant analysis
  • Linear regression
  • Logistic regression
  • GLM
  • EM Algorithm
  • Mixed Models
  • Additive Models
  • Classification
  • KNN
  • Ridge regression
  • Clustering
d2dbdpa From Data to Decision with Big Data and Predictive Analytics 21 hours


If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Quick Overview

  • Data Sources
  • Minding Data
  • Recommender systems
  • Target Marketing


  • Structured vs unstructured
  • Static vs streamed
  • Attitudinal, behavioural and demographic data
  • Data-driven vs user-driven analytics
  • data validity
  • Volume, velocity and variety of data


  • Building models
  • Statistical Models
  • Machine learning

Data Classification

  • Clustering
  • kGroups, k-means, nearest neighbours
  • Ant colonies, birds flocking

Predictive Models

  • Decision trees
  • Support vector machine
  • Naive Bayes classification
  • Neural networks
  • Markov Model
  • Regression
  • Ensemble methods


  • Benefit/Cost ratio
  • Cost of software
  • Cost of development
  • Potential benefits

Building Models

  • Data Preparation (MapReduce)
  • Data cleansing
  • Choosing methods
  • Developing model
  • Testing Model
  • Model evaluation
  • Model deployment and integration

Overview of Open Source and commercial software

  • Selection of R-project package
  • Python libraries
  • Hadoop and Mahout
  • Selected Apache projects related to Big Data and Analytics
  • Selected commercial solution
  • Integration with existing software and data sources
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours


Introduction and ANN Structure.

  • Biological neurons and artificial neurons.
  • Model of an ANN.
  • Activation functions used in ANNs.
  • Typical classes of network architectures .

Mathematical Foundations and Learning mechanisms.

  • Re-visiting vector and matrix algebra.
  • State-space concepts.
  • Concepts of optimization.
  • Error-correction learning.
  • Memory-based learning.
  • Hebbian learning.
  • Competitive learning.

Single layer perceptrons.

  • Structure and learning of perceptrons.
  • Pattern classifier - introduction and Bayes' classifiers.
  • Perceptron as a pattern classifier.
  • Perceptron convergence.
  • Limitations of a perceptrons.

Feedforward ANN.

  • Structures of Multi-layer feedforward networks.
  • Back propagation algorithm.
  • Back propagation - training and convergence.
  • Functional approximation with back propagation.
  • Practical and design issues of back propagation learning.

Radial Basis Function Networks.

  • Pattern separability and interpolation.
  • Regularization Theory.
  • Regularization and RBF networks.
  • RBF network design and training.
  • Approximation properties of RBF.

Competitive Learning and Self organizing ANN.

  • General clustering procedures.
  • Learning Vector Quantization (LVQ).
  • Competitive learning algorithms and architectures.
  • Self organizing feature maps.
  • Properties of feature maps.

Fuzzy Neural Networks.

  • Neuro-fuzzy systems.
  • Background of fuzzy sets and logic.
  • Design of fuzzy stems.
  • Design of fuzzy ANNs.


  • A few examples of Neural Network applications, their advantages and problems will be discussed.


  • The PAC Learning Framework
    • Guarantees for finite hypothesis set – consistent case
    • Guarantees for finite hypothesis set – inconsistent case
    • Generalities
      • Deterministic cv. Stochastic scenarios
      • Bayes error noise
      • Estimation and approximation errors
      • Model selection
  • Radmeacher Complexity and VC – Dimension
  • Bias - Variance tradeoff
  • Regularisation
  • Over-fitting
  • Validation
  • Support Vector Machines
  • Kriging (Gaussian Process regression)
  • PCA and Kernel PCA
  • Self Organisation Maps (SOM)
  • Kernel induced vector space
    • Mercer Kernels and Kernel - induced similarity metrics
  • Reinforcement Learning


This will be taught in relation to the topics covered on Day 1 and Day 2

  • Logistic and Softmax Regression
  • Sparse Autoencoders
  • Vectorization, PCA and Whitening
  • Self-Taught Learning
  • Deep Networks
  • Linear Decoders
  • Convolution and Pooling
  • Sparse Coding
  • Independent Component Analysis
  • Canonical Correlation Analysis
  • Demos and Applications
deeplearning1 Introduction to Deep Learning 21 hours This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
  • Backprop, modular models
  • Logsum module
  • RBF Net
  • MAP/MLE loss
  • Parameter Space Transforms
  • Convolutional Module
  • Gradient-Based Learning 
  • Energy for inference,
  • Objective for learning
  • PCA; NLL: 
  • Latent Variable Models
  • Probabilistic LVM
  • Loss Function
  • Handwriting recognition
sysagent Systemy wieloagentowe 7 hours

1. Wstęp systemy wieloagentowe

a. czym jest agent programowy

b. rodzaje agentów

c. platformawieloagentowa i społeczność agentów

d. analogia do systemów żywych

2. Teoria

a. Architektury systemów wieloagentowych

  • architektury logiczne
  • architektury reaktywne
  • architektury BDI (belief, desires, intentions)
  • architektury AGR (Agent/Group/Role)
  • inne architektury

b. Inteligencja agenta i interakcja z otoczeniem

  • pozyskiwanie i gromadzenie wiedzy
  • interakcja ze środowiskiem w którym funkcjonuje agent
  • komunikacja i interakcja z innymi agentami (wymiana wiedzy)
  • rozwiązywanie konfliktów (negocjacje)
  • planowanie i podejmowanie decyzji

c. Wybrane algorytmy społecznościowe

  • kolonia mrówek
  • stado (ławica, rój cząstek)
pjn Przetwarzanie języka naturalnego 7 hours

1. Wprowadzenie

2. Zastosowania NLP

  • Podstawowe pojęcia
  • Narzędzia NLP

3. Podstawy języka Perl

  • Struktury danych
  • Wyrażenia regularne
  • Parsowanie i tokenizacja

4 . Podstawy narzędzi RDBMS

  • Pająki internetowe
  • Korpus tekstowy
  • Własności statystyczne
  • Listy stopsłów
  • Indeksowanie dokumentów

5. Wyszukiwarka dokumentów

  • analiza leksykalna
  • wyszukiwanie wzorca
  • słowniki i automaty słownikowe
  • analiza morfologiczna
  • techniki ngramów
  • podobieństwo dokumentów
iop Inteligencja obliczeniowa w praktyce 7 hours

1. Obszary zastosowań

  • klasyfikacja (metody jakościowe, np. WTA)
  • regresja (metody ilościowe, np. próg decyzyjny)

2. Surowe dane

3. Przetwarzanie wstępne danych, sygnałów (np. normalizacja, PCA, FFT itp.)

4. Dobór elementów do zbioru uczącego i testowego (np. walidacja krzyżowa)

5.  Wybór metody inteligencji obliczeniowej

6.  Optymalizacja parametrów treningu (np. algorytmy genetyczne)

7. Ocena uzyskanych wyników (np. krzywa ROC)

8. Przykładowe zastosowania MIO:

  • rozpoznawanie osób na podstawie gestów z ekranu
  • rozpoznawanie gestów, ruchów dłonią
  • identyfikacja rodzaju atramentu i papieru
  • diagnozowanie dysfunkcji mięśnia sercowego
  • analiza lotnych związków organicznych przy użyciu elektronicznego nosa (klasyfikacja gatunków herbaty i aproksymacja stężenia fenolu)
  • szacowanie wypracowania pompy wyporowej
mtdintob Metody Inteligencji Obliczeniowej 7 hours

1. Wstęp Sztuczna inteligencja

a. słaba i silna sztuczna inteligencja

b. sztuczna inteligencja a inteligencja obliczeniowa

c. klasyfikacja metod inteligencji obliczeniowej

d. analogie do systemów żywych

2. Metody inteligencji obliczeniowej

a. sztuczne sieci neuronowe

  • klasyfikacja i typy sieci neuronowych
  • model sztucznego neuronu
  • topologia
  • metody i algorytmy uczenia
  • sieci neuronowe: SOM, MLP, PNN, LVQ, RNN, RBF, GRNN

b. systemy rozmyte

  • logika rozmyta
  • zbiory rozmyte i funkcje przynależności
  • wnioskowanie przybliżone
  • zasada działania
  • model Mamdani i Sugeno

c. maszyna wektorów nośnych

  • zasada działania
  • typy funkcji jądra
  • typy wielokrotnej klasyfikacji
  • mocne i słabe strony

d. obliczenia ewolucyjne

  • algorytmy genetyczne
  • metody selekcji
  • skalowanie funkcji przystosowania
  • operatory genetyczne
  • porównanie algorytmów ewolucyjnych

e. inteligencja roju

f. inteligentne agenty

g. algorytm knajbliższych sąsiadów

h. systemy hybrydowe

  • ewolucyjnoneuronowe
  • neuronoworozmyte
  • ewolucyjnorozmyte
cntk Using Computer Network ToolKit (CNTK) 28 hours

Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images.


This course is directed at engineers and architects aiming to utilize CNTK in their projects.

Getting started

  • Setup CNTK on your machine
    • Enabling 1bit SGD
    • Developing and Testing
    • CNTK Production Test Configurations
    • How to contribute to CNTK
  • Tutorial
  • Tutorial II
  • CNTK usage overview
  • Examples
  • Presentations
  • Multiple GPUs¹ and machines

Configuring CNTK

  • Config file overview
  • Simple Network Builder
  • BrainScript Network Builder
  • SGD block
  • Reader block
  • Train, Test, Eval
  • Top-level configurations

Describing Networks

  • Basic concepts
  • Expressions
  • Defining functions
  • Full Function Reference

Data readers

  • Text Format Reader
    • CNTK Text Format Reader
    • UCI Fast Reader (deprecated)
  • HTKMLF Reader
  • LM sequence reader
  • LU sequence reader
  • Image reader

Evaluating CNTK Models

  • Overview
  • C++ Evaluation Interface
  • C# Evaluation Interface
  • Evaluating Hidden Layers
  • C# Image Transforms for Evaluation

Advanced topics

  • Command line parsing rules
  • Top-level commands
  • Plot command
  • ConvertDBN command

¹ The topic related to the use of CNTK with a GPU is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

aiintrozero From Zero to AI 35 hours

This course is created for people who have no previous experience in probability and statistics.

Probability (3.5h)

  • Definition of probability
  • Binomial distribution
  • Everyday usage exercises

Statistics (10.5h)

  • Descriptive Statistics
  • Inferential Statistics
  • Regression
  • Logistic Regression
  • Exercises

Intro to programming (3.5h)

  • Procedural Programming
  • Functional Programming
  • OOP Programming
  • Exercises (writing logic for a game of choice, e.g. noughts and crosses)

Machine Learning (10.5h)

  • Classification
  • Clustering
  • Neural Networks
  • Exercises (write AI for a computer game of choice)

Rules Engines and Expert Systems (7 hours)

  • Intro to Rule Engines
  • Write AI for the same game and combing solutions into hybrid approach
aiauto Artificial Intelligence in Automotive 14 hours

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

Current state of the technology

  • What is used
  • What may be potentially used

Rules based AI 

  • Simplifying decision

Machine Learning 

  • Classification
  • Clustering
  • Neural Networks
  • Types of Neural Networks
  • Presentation of working examples and discussion

Deep Learning

  • Basic vocabulary 
  • When to use Deep Learning, when not to
  • Estimating computational resources and cost
  • Very short theoretical background to Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

  • Preparing Data
  • Choosing loss function
  • Choosing appropriate type on neural network
  • Accuracy vs speed and resources
  • Training neural network
  • Measuring efficiency and error

Sample usage

  • Anomaly detection
  • Image recognition
  • ADAS





aiint Artificial Intelligence Overview 7 hours

Kurs ten został stworzony dla menadżerów, architektów, analityków biznesowych i systemowych, menedżerów oprogramowania oraz wszystkich zainteresowanych przeglądem stosowania sztucznej inteligencji i prognozą dla jej rozwoju.

Artificial Intelligence History

  • Intelligent Agents

Problem Solving

  • Solving Problems by Searching
  • Beyond Classical Search
  • Adversarial Search
  • Constraint Satisfaction Problems

Knowledge and Reasoning

  • Logical Agents
  • First-Order Logic
  • Inference in First-Order Logic
  • Classical Planning
  • Planning and Acting in the Real World
  • Knowledge Representation

Uncertain Knowledge and Reasoning

  • Quantifying Uncertainty
  • Probabilistic Reasoning
  • Probabilistic Reasoning over Time
  • Making Simple Decisions
  • Making Complex Decisions


  • Learning from Examples
  • Knowledge in Learning
  • Learning Probabilistic Models
  • Reinforcement Learning

Communicating, Perceiving, and Acting;

  • Natural Language Processing
  • Natural Language for Communication
  • Perception
  • Robotics


  • Philosophical Foundations
  • AI: The Present and Future
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours

This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

TensorFlow Basics

  • Creation, Initializing, Saving, and Restoring TensorFlow variables
  • Feeding, Reading and Preloading TensorFlow Data
  • How to use TensorFlow infrastructure to train models at scale
  • Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

  • Inputs and Placeholders
  • Build the GraphS
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluate the Model
    • Build the Eval Graph
    • Eval Output

The Perceptron

  • Activation functions
  • The perceptron learning algorithm
  • Binary classification with the perceptron
  • Document classification with the perceptron
  • Limitations of the perceptron

From the Perceptron to Support Vector Machines

  • Kernels and the kernel trick
  • Maximum margin classification and support vectors

Artificial Neural Networks

  • Nonlinear decision boundaries
  • Feedforward and feedback artificial neural networks
  • Multilayer perceptrons
  • Minimizing the cost function
  • Forward propagation
  • Back propagation
  • Improving the way neural networks learn

Convolutional Neural Networks

  • Goals
  • Model Architecture
  • Principles
  • Code Organization
  • Launching and Training the Model
  • Evaluating a Model
neuralnet Introduction to the use of neural networks 7 hours

Szkolenie skierowane jest do osób, które chcą zapoznać się z podstawami sieci neuronowych oraz ich zastosowań.


  • Czy komputery mogą myśleć?
  • Podejście deklaratywne i imperatywne do rozwiązywania problemów
  • Cel bedań nad sztuczna inteligencją
  • Definicja sztucznej inteligencji. Test Turinga. Inne wyznaczniki
  • Rozwój koncepcji inteligentnych systemów
  • Najważniejsze osiągniącia i kierunki rozwoju

Sieci neuronowe

  • Podstawy
  • Koncepcja neuronu i sieci neuronowych
  • Uproszczony model mózgu
  • Możliwości neuronu
  • Problem XOR i charakter podziału wartości
  • Polimorficzny charakter funkcji sigmoidalnej
  • Pozostałe funkcje aktywacji
  • Budowa sieci neuronowych
  • Koncepcja łączenie neuronów
  • Sieć neuronowa jako węzły
  • Budowa sieci
  • Neurony
  • Warstwy
  • Wagi
  • Dane wejściowe i wyjściowe
  • Zakresy 0..1
  • Normalizacja
  • Uczenie sieci neuronowych
  • Propagacja wsteczna
  • Kroki propagacji
  • Algorytmy uczenia sieci
  • Zakres zastosowań
  • Estymacja
  • Problemy z możliwością przybliżenia wyniku
  • Przykłady
  • Problem XOR
  • Totolotek?
  • Kursy akcji
  • OCR i rozpoznawanie wzorów obrazów
  • Inne zastosowania
  • Modelowanie sieci neuronowej realizującej zadanie przewidywania kursów akcji giełdowych

Problemy na dziś

  • Eksplocja kombinatoryczna i problemy gier
  • Test Turinga raz jeszcze
  • Zbytnia ufność w możliwości komputerów
datamodeling Pattern Recognition 35 hours

This course provides an introduction into the field of pattern recognition and machine learning. It also touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, continuous feedback, and testing of knowledge and skills acquired.

    Data analysts
    PhD students, researchers and practitioners



Probability theory, model selection, decision and information theory

Probability distributions

Linear models for regression and classification

Neural networks

Kernel methods

Sparse kernel machines

Graphical models

Mixture models and EM

Approximate inference

Sampling methods

Continuous latent variables

Sequential data

Combining models


rneuralnet Sieci Neuronowe w R 14 hours Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie R-project.

Introduction to Neural Networks

  1. What are Neural Networks
  2. What is current status in applying neural networks
  3. Neural Networks vs regression models
  4. Supervised and Unsupervised learning

Overview of packages available

  1. nnet, neuralnet and others
  2. differences between packages and itls limitations
  3. Visualizing neural networks

Applying Neural Networks

  • Concept of neurons and neural networks
  • A simplified model of the brain
  • Opportunities neuron
  • XOR problem and the nature of the distribution of values
  • The polymorphic nature of the sigmoidal
  • Other functions activated
  • Construction of neural networks
  • Concept of neurons connect
  • Neural network as nodes
  • Building a network
  • Neurons
  • Layers
  • Scales
  • Input and output data
  • Range 0 to 1
  • Normalization
  • Learning Neural Networks
  • Backward Propagation
  • Steps propagation
  • Network training algorithms
  • range of application
  • Estimation
  • Problems with the possibility of approximation by
  • Examples
  • OCR and image pattern recognition
  • Other applications
  • Implementing a neural network modeling job predicting stock prices of listed
Torch Torch: Getting started with Machine and Deep Learning 21 hours

Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.

In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.

By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

    Software developers and programmers wishing to enable Machine and Deep Learning within their applications

Format of the course
    Overview of Machine and Deep Learning
    In-class coding and integration exercises
    Test questions sprinkled along the way to check understanding

Introduction to Torch
    Like NumPy but with CPU and GPU implementation
    Torch's usage in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking

Installing Torch
    Linux, Windows, Mac
    Bitmapi and Docker

Installing Torch packages
    Using the LuaRocks package manager

Choosing an IDE for Torch
    ZeroBrane Studio
    Eclipse plugin for Lua

Working with the Lua scripting language and LuaJIT
    Lua's integration with C/C++
    Lua syntax: datatypes, loops and conditionals, functions, functions, tables, and file i/o.
    Object orientation and serialization in Torch
    Coding exercise

Loading a dataset in Torch
    CIFAR-10, CIFAR-100

Machine Learning in Torch
    Deep Learning
        Manual feature extraction vs convolutional networks
    Supervised and Unsupervised Learning
        Building a neural network with Torch    
    N-dimensional arrays

Image analysis with Torch
    Image package
    The Tensor library

Working with the REPL interpreter

Working with databases

Networking and Torch

GPU support in Torch

Integrating Torch
    C, Python, and others

Embedding Torch
    iOS and Android

Other frameworks and libraries
    Facebook's optimized deep-learning modules and containers

Creating your own package

Testing and debugging

Releasing your application

The future of AI and Torch

MLFWR1 Machine Learning Fundamentals with R 14 hours

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off


  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises


  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means
OpenNN OpenNN: Implementing neural networks 14 hours

OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

    Software developers and programmers wishing to create Deep Learning applications.

Format of the course
    Lecture and discussion coupled with hands-on exercises.

Introduction to OpenNN, Machine Learning and Deep Learning

Downloading OpenNN

Working with Neural Designer
    Using Neural Designer for descriptive, diagnostic, predictive and prescriptive analytics

OpenNN architecture
    CPU parallelization

OpenNN classes
    Data set, neural network, loss index, training strategy, model selection, testing analysis
    Vector and matrix templates

Building a neural network application
    Choosing a suitable neural network
    Formulating the variational problem (loss index)
    Solving the reduced function optimization problem (training strategy)

Working with datasets
     The data matrix (columns as variables and rows as instances)

Learning tasks
    Function regression
    Pattern recognition

Compiling with QT Creator

Integrating, testing and debugging your application

The future of neural networks and OpenNN

appliedml Applied Machine Learning 14 hours

This training course is for people that would like to apply Machine Learning in practical applications.


This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.

  • Naive Bayes
  • Multinomial models
  • Bayesian categorical data analysis
  • Discriminant analysis
  • Linear regression
  • Logistic regression
  • GLM
  • EM Algorithm
  • Mixed Models
  • Additive Models
  • Classification
  • KNN
  • Bayesian Graphical Models
  • Factor Analysis (FA)
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA)
  • Support Vector Machines (SVM) for regression and classification
  • Boosting
  • Ensemble models
  • Neural networks
  • Hidden Markov Models (HMM)
  • Space State Models
  • Clustering
mldt Machine Learning and Deep Learning 21 hours This course covers AI (emphasizing Machine Learning and Deep Learning)

Machine learning

Introduction to Machine Learning

  • Applications of machine learning

  • Supervised Versus Unsupervised Learning

  • Machine Learning Algorithms

    • Regression

    • Classification

    • Clustering

    • Recommender System

    • Anomaly Detection

    • Reinforcement Learning


  • Simple & Multiple Regression

    • Least Square Method

    • Estimating the Coefficients

    • Assessing the Accuracy of the Coefficient Estimates

    • Assessing the Accuracy of the Model

    • Post Estimation Analysis

    • Other Considerations in the Regression Models

    • Qualitative Predictors

    • Extensions of the Linear Models

    • Potential Problems

    • Bias-variance trade off [under-fitting/over-fitting] for regression models

Resampling Methods

  • Cross-Validation

  • The Validation Set Approach

  • Leave-One-Out Cross-Validation

  • k-Fold Cross-Validation

  • Bias-Variance Trade-Off for k-Fold

  • The Bootstrap

Model Selection and Regularization

  • Subset Selection [Best Subset Selection, Stepwise Selection, Choosing the Optimal Model]

  • Shrinkage Methods/ Regularization [Ridge Regression, Lasso & Elastic Net]

  • Selecting the Tuning Parameter

  • Dimension Reduction Methods

    • Principal Components Regression

    • Partial Least Squares


  • Logistic Regression

    • The Logistic Model cost function

    • Estimating the Coefficients

    • Making Predictions

    • Odds Ratio

    • Performance Evaluation Matrices

    • [Sensitivity/Specificity/PPV/NPV, Precision, ROC curve etc.]

    • Multiple Logistic Regression

    • Logistic Regression for >2 Response Classes

    • Regularized Logistic Regression

  • Linear Discriminant Analysis

    • Using Bayes’ Theorem for Classification

    • Linear Discriminant Analysis for p=1

    • Linear Discriminant Analysis for p >1

  • Quadratic Discriminant Analysis

  • K-Nearest Neighbors

  • Classification with Non-linear Decision Boundaries

  • Support Vector Machines

    • Optimization Objective

    • The Maximal Margin Classifier

    • Kernels

    • One-Versus-One Classification

    • One-Versus-All Classification

  • Comparison of Classification Methods

Introduction to Deep Learning

ANN Structure

  • Biological neurons and artificial neurons

  • Non-linear Hypothesis

  • Model Representation

  • Examples & Intuitions

  • Transfer Function/ Activation Functions

  • Typical classes of network architectures

Feed forward ANN.

  • Structures of Multi-layer feed forward networks

  • Back propagation algorithm

  • Back propagation - training and convergence

  • Functional approximation with back propagation

  • Practical and design issues of back propagation learning

Deep Learning

  • Artificial Intelligence & Deep Learning

  • Softmax Regression

  • Self-Taught Learning

  • Deep Networks

  • Demos and Applications


Getting Started with R

  • Introduction to R

  • Basic Commands & Libraries

  • Data Manipulation

  • Importing & Exporting data

  • Graphical and Numerical Summaries

  • Writing functions


  • Simple & Multiple Linear Regression

  • Interaction Terms

  • Non-linear Transformations

  • Dummy variable regression

  • Cross-Validation and the Bootstrap

  • Subset selection methods

  • Penalization [Ridge, Lasso, Elastic Net]


  • Logistic Regression, LDA, QDA, and KNN,

  • Resampling & Regularization

  • Support Vector Machine

  • Resampling & Regularization

Artificial Neural Network

Deep Learning



  • For ML algorithms, case studies will be used to discuss their application, advantages & potential issues.

  • Analysis of different data sets will be performed using R

Other regions

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