1. State of the art of artificial intelligence
  2. Philosophy of artificial intelligence
  3. Future of artificial intelligence
  4. Project development process with artificial intelligence
  5. Data, your greatest asset

  1. Machine learning
  2. Deep learning
  3. Transformers
  4. Generation of synthetic data
  5. Hyperparameters in artificial intelligence models

  1. Linear regression
  2. Non-linear regression and support vector machines (SVM)
  3. Decision trees, random forests
  4. Fuse logic and gradient down
  5. Recommendation systems

  1. Preparation of the working environment: Anaconda, Visual Studio Code and Python
  2. Input dataset and data preprocessing
  3. TensorHub, TensorFlow and Keras
  4. Image processing
  5. Generation of artificial intelligence models

  1. Introduction
  2. Simple, multiple and logistic linear regression (I)
  3. Simple, multiple and logistic linear regression (II)
  4. Support vector machines (SVM)
  5. Decision trees

  1. KNN (k-nearest neighbours)
  2. Naive Bayes
  3. Evaluation of supervised models
  4. Example exercise
  5. Proposed exercise

  1. Introduction to clustering. purconsider and metrics
  2. K-means clustering
  3. Hierarchical clustering, other techniques and examples
  4. Principal component analysis (PCA)
  5. PCA example exercise

  1. Artificial Neural Networks (ANN) (I)
  2. Artificial Neural Networks (ANN) (II)
  3. Artificial Neural Networks (ANN) (III)
  4. Example exercise
  5. Proposed exercise

  1. Introduction
  2. Review. Artificial neural network (ANN)
  3. Review. ANN exercises
  4. Convolutional Neural Networks (CNN)
  5. CNN Exercises

  1. Natural language processing (I)
  2. Recurrent neural networks (RNN) (I)
  3. Recurrent neural networks (RNN) (II)
  4. Natural language processing (II)
  5. RNN Exercise

  1. Boltzmann Machines (BM)
  2. Restricted Boltzmann Machines (RBM)
  3. Recommender systems
  4. Recommender systems. metrics
  5. RBM exercise

  1. Self-organising maps (SOM)
  2. SOM exercises
  3. Autoencoders (AE)
  4. AE exercises
  5. Proposed exercise

  1. The fourth industrial revolution
  2. Digital transformation in companies
  3. Fundaments and key points
  4. Benefits
  5. Enabling technologies

  1. Big data
  2. Cloud computing
  3. Blockchain
  4. Artificial intelligence
  5. Augmented and virtual reality

  1. BIM
  2. Collaborative robots
  3. Additive manufacturing
  4. Hyperconnectivity
  5. IoT

  1. Manufacturing execution system (MES)
  2. Process integration and efficiency
  3. Use cases
  4. New methodologies. agile, lean startup or design thinking
  5. Business change management

  1. Introduction
  2. Key elements in AI project management
  3. AI project characteristics
  4. Introduction to the main agile and ideation methodologies
  5. Methodology integration

  1. Introduction
  2. Phase I. Empathize
  3. Phase II. Define
  4. Phase III. Devise
  5. Phase IV. Prototype

  1. Lean start-up. Basic concepts
  2. Lean start-up. Tools
  3. Scrum. Introduction
  4. Scrum. Roles
  5. Scrum. Ceremonies and artifacts

  1. Introduction
  2. Project ideation
  3. Project implementation
  4. Advise on implementing methodologies
  5. Summary and conclusions

  1. Financial sector
  2. Retail sector
  3. Industrial sector
  4. Agricultural sector
  5. Health sector

  1. Logistics and operations
  2. Marketing
  3. Sales and customer service
  4. Finance and control
  5. People analytics

  1. Current scenario of a booming sector
  2. Financing
  3. Featured start-ups
  4. Future of the AI ecosystem
  5. Starting an AI company

  1. Ethics. General remarks
  2. Bias examples
  3. Global initiatives
  4. Public Institutions and regulations
  5. AI in the SDGs

  1. Data information knoledge wisdom
  2. Data management (i)
  3. Data management (ii)
  4. Corporate performance management
  5. Databases

  1. Business intelligence
  2. Datawarehousing
  3. Big data
  4. Hadoop
  5. Spark

  1. Hadoop ecosystem (I)
  2. Hadoop ecosystem (II)
  3. Hadoop ecosystem (III)
  4. Spark ecosystem
  5. Installation and configuration of big data architectures

  1. Analytics
  2. Main algorithms (I)
  3. Main algorithms (II)
  4. Machine learning and deep learning
  5. Internet of things

  1. Introduction to SQL
  2. Database manipulation
  3. Data types
  4. Normalization
  5. Creating tables in SQL

  1. Table manipulation
  2. SQL table query
  3. Table joining in SQL
  4. Table combinations and views
  5. Other SQL commands

  1. String functions and numeric functions (I)
  2. Numeric function (II)
  3. Date and time functions
  4. Other functions
  5. Loops, conditionals and triggers in SQL

  1. Data warehousing introduction
  2. Databases in a data warehouse. Stage
  3. Databases in a data warehouse. ODS (I)
  4. Databases in a data warehouse. ODS (II)
  5. Databases in a data warehouse. DDS

  1. Introduction
  2. Polyglot persistence
  3. ACID model
  4. New trends
  5. Comparison between SQL and NOSQL

  1. Data models
  2. Aggregation models
  3. Key-value aggregation models
  4. Document-oriented data models
  5. Column-oriented aggregation models
  6. Graph data model

  1. Distributed databases
  2. Strategies for the design of distributed DBS
  3. NOSQL database design
  4. Hadoop distributed file system (HDFS)

  1. Example of a NOSQL aggregation database
  2. Riak. Example of a key-value database
  3. MongoDB. Example of a document database
  4. Neo4J. Example of a graph NOSQL database
  5. HBASE. Example of a columnar database