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

cntk 
Using Computer Network ToolKit (CNTK) 
28 hours 
Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multimachine, MultiGPU, Highly efficent RNN training machine learning framework for speech, text, and images.
Audience
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
Toplevel 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
Toplevel 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 classroombased 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. 
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
GradientBased Learning
Energy for inference,
Objective for learning
PCA; NLL:
Latent Variable Models
Probabilistic LVM
Loss Function
Handwriting recognition

annmldt 
Artificial Neural Networks, Machine Learning, Deep Thinking 
21 hours 
DAY 1  ARTIFICIAL NEURAL NETWORKS
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.
Revisiting vector and matrix algebra.
Statespace concepts.
Concepts of optimization.
Errorcorrection learning.
Memorybased 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 Multilayer 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.
Neurofuzzy systems.
Background of fuzzy sets and logic.
Design of fuzzy stems.
Design of fuzzy ANNs.
Applications
A few examples of Neural Network applications, their advantages and problems will be discussed.
DAY 2 MACHINE LEARNING
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
Overfitting
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
DAY 3  DEEP 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
SelfTaught Learning
Deep Networks
Linear Decoders
Convolution and Pooling
Sparse Coding
Independent Component Analysis
Canonical Correlation Analysis
Demos and Applications

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
BiasVariance tradeoff
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
KNearest neighbors
Exercises
Crossvalidation and Resampling
Crossvalidation approaches
Bootstrap
Exercises
Unsupervised Learning
Kmeans clustering
Examples
Challenges of unsupervised learning and beyond Kmeans

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.
Audience
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 
Audience
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
Datatypes
Structured vs unstructured
Static vs streamed
Attitudinal, behavioural and demographic data
Datadriven vs userdriven analytics
data validity
Volume, velocity and variety of data
Models
Building models
Statistical Models
Machine learning
Data Classification
Clustering
kGroups, kmeans, nearest neighbours
Ant colonies, birds flocking
Predictive Models
Decision trees
Support vector machine
Naive Bayes classification
Neural networks
Markov Model
Regression
Ensemble methods
ROI
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 Rproject 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

neuralnet 
Introduction to the use of neural networks 
7 hours 
The training is aimed at people who want to learn the basics of neural networks and their applications.
The Basics
Whether computers can think of?
Imperative and declarative approach to solving problems
Purpose Bedan on artificial intelligence
The definition of artificial intelligence. Turing test. Other determinants
The development of the concept of intelligent systems
Most important achievements and directions of development
Neural Networks
The Basics
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
XOR problem
Lotto?
Equities
OCR and image pattern recognition
Other applications
Implementing a neural network modeling job predicting stock prices of listed
Problems for today
Combinatorial explosion and gaming issues
Turing test again
Overconfidence in the capabilities of computers

appliedml 
Angewandtes Maschinelles Lernen 
14 hours 
Der Übungskurs ist für alle diejenigen gedacht, die "Machine Learning" in praktischen Applikationen anwenden möchten
Teilnehmer
Dieser Kurs ist für Data Scientists und Statistiker, die Grundkenntnisse in Statistik haben und wissen, wie man R programmiert. Der Schwerpunkt des Kurses liegt auf dem praktischen Aspekt von Daten/ModellVorbereitung, Execution, post hoc Analyse und Visualisierung.
Das Ziel ist es, den Teilnehmern praktische Kenntnisse im Maschinellen Lernen zu vermitteln.
Bereichsspezifische Beispiele erhöhen die Relevanz der Schulung für die Teilnehmer.
Naive Bayes
Multinomial Modelle
Bayesian categorical Datenanalyse
Diskriminante Analyse
Lineare Regression
Logistischge Regression
GLM
EM Algorithm
Mixed Models
Zusätzliche Modelle
Klassifikation
KNN
Bayesian GraphikModelle
Factor Analysis (FA)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Support Vector Machines (SVM) für Regression und Klassifikation
Boosting
Ensemble Modelle
Neural networks
Hidden Markov Models (HMM)
Space State Modelle
Clustering

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

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

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
Regression
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
Biasvariance trade off [underfitting/overfitting] for regression models
Resampling Methods
CrossValidation
The Validation Set Approach
LeaveOneOut CrossValidation
kFold CrossValidation
BiasVariance TradeOff for kFold
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
Classification
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
KNearest Neighbors
Classification with Nonlinear Decision Boundaries
Support Vector Machines
Optimization Objective
The Maximal Margin Classifier
Kernels
OneVersusOne Classification
OneVersusAll Classification
Comparison of Classification Methods
Introduction to Deep Learning
ANN Structure
Biological neurons and artificial neurons
Nonlinear Hypothesis
Model Representation
Examples & Intuitions
Transfer Function/ Activation Functions
Typical classes of network architectures
Feed forward ANN.
Structures of Multilayer 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
SelfTaught Learning
Deep Networks
Demos and Applications
Lab:
Getting Started with R
Introduction to R
Basic Commands & Libraries
Data Manipulation
Importing & Exporting data
Graphical and Numerical Summaries
Writing functions
Regression
Simple & Multiple Linear Regression
Interaction Terms
Nonlinear Transformations
Dummy variable regression
CrossValidation and the Bootstrap
Subset selection methods
Penalization [Ridge, Lasso, Elastic Net]
Classification
Logistic Regression, LDA, QDA, and KNN,
Resampling & Regularization
Support Vector Machine
Resampling & Regularization
Artificial Neural Network
Deep Learning
Note:
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

rneuralnet 
Training Neural Network in R 
14 hours 
This course is an introduction to applying neural networks in real world problems using Rproject software.
Introduction to Neural Networks
What are Neural Networks
What is current status in applying neural networks
Neural Networks vs regression models
Supervised and Unsupervised learning
Overview of packages available
nnet, neuralnet and others
differences between packages and itls limitations
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

aiint 
Ein Überblick über künstliche Intelligenz 
7 hours 
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development.
Artificial Intelligence History
Intelligent Agents
Problem Solving
Solving Problems by Searching
Beyond Classical Search
Adversarial Search
Constraint Satisfaction Problems
Knowledge and Reasoning
Logical Agents
FirstOrder Logic
Inference in FirstOrder 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
Learning from Examples
Knowledge in Learning
Learning Probabilistic Models
Reinforcement Learning
Communicating, Perceiving, and Acting;
Natural Language Processing
Natural Language for Communication
Perception
Robotics
Conclusions
Philosophical Foundations
AI: The Present and Future
