Master artificial intelligence

UNIT 1. INTRODUCTION TO AI

UNIT 2. INTRODUCTION

UNIT 3. MOTIVATION

UNIT 4. WHAT IS AI?

UNIT 5. KNOWLEDGE AREAS

UNIT 6. BRIEF HISTORY OF AI: FROM MYTH TO REALITY

UNIT 7. INTRODUCTION

UNIT 8. THE BIRTH OF AI

UNIT 9. FROM 1950’S TO 1960’S

UNIT 10. FROM 1960’S TO 1970’S

UNIT 11. FROM 1980’S TO 1990’S - AI WINTER

UNIT 12. FROM 2000 TO THE PRESENT

UNIT 13. KEY CONCEPTS, AGENTS, AND KNOWLEDGE REPRESENTATION

UNIT 14. INTRODUCTION

UNIT 15. WEAK AND STRONG AI

UNIT 16. THE TURING TEST

UNIT 17. AGENTS

UNIT 18. AGENT ARCHITECTURE

UNIT 19. KNOWLEDGE REPRESENTATION

UNIT 20. PROBLEM SOLVING: AUTOMATED REASONING AND SEARCH

UNIT 21. INTRODUCTION

UNIT 22. AUTOMATED REASONING

UNIT 23. SEARCH

UNIT 24. CONCLUSIONS

UNIT 25. AUTOMATED LEARNING: SUPERVISED, UNSUPERVISED, REINFORCEMENT LEARNING I

UNIT 26. INTRODUCTION

UNIT 27. AUTOMATED LEARNING BASIC PRINCIPLES

UNIT 28. SUPERVISED LEARNING

UNIT 29. CONCLUSIONS

UNIT 30. AUTOMATED LEARNING: SUPERVISED, UNSUPERVISED, REINFORCEMENT LEARNING II

UNIT 31. INTRODUCTION

UNIT 32. UNSUPERVISED LEARNING

UNIT 33. DEEP LEARNING

UNIT 34. REINFORCEMENT LEARNING

UNIT 35. CONCLUSIONS

UNIT 36. BIG DATA: LEARNING WITH MILLION DATA

UNIT 37. INTRODUCTION

UNIT 38. DATA LIFE CYCLE

UNIT 39. INFORMATION/DATA COLLECTION

UNIT 40. INFORMATION STORAGE

UNIT 41. INFORMATION ANALYSIS

UNIT 42. INFORMATION EXPLOITATION

UNIT 43. CONCLUSIONS

UNIT 44. HUMAN-MACHINE INTERACTION: ARTIFICIAL VISION AND NATURAL LANGUAGE PROCESSING

UNIT 45. INTRODUCTION

UNIT 46. ARTIFICIAL VISION

UNIT 47. COMPUTER VISION PROCESS

UNIT 48. COMPUTER VISION LEARNING TASKS

UNIT 49. SPEECH RECOGNITION

UNIT 50. LANGUAGE RECOGNITION PROCESS

UNIT 51. NATURAL LANGUAGE PROCESSING

UNIT 52. THE FUTURE OF AI: ETHICAL ISSUES AND DIVERSITY

UNIT 53. INTRODUCTION

UNIT 54. AI ETHICAL ISSUES

UNIT 55. THE MEANING OF DIVERSITY

UNIT 56. THE FUTURE OF AI

UNIT 1. DATA PREPARATION

1. Introduction
2. Self - Service solutions
3. Data processing techniques
4. Data quality management
5. Types of data problem

UNIT 2. EXCEL

1. Data cleaning with Excel
2. DATASET
3. Functions. Part I
4. Functions. Part II
5. Functions. Part III

UNIT 3. TALEND DATA PREPARATION

1. Instructions for installing talend data preparation free desktop
2. Data Cleansing with Talend Data Preparation
3. Basic cleansing functions
4. Data normalization
5. Data enrichment

UNIT 4. TRIFACTA WRANGLER

1. Registration instructions
2. Data cleansing with trifacta
3. Basic cleansing functions
4. Data normalization
5. Data enrichment

UNIT 1. SUPERVISED LEARNING (I)

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

UNIT 2. SUPERVISED LEARNING (II)

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

UNIT 3. UNSUPERVISED LEARNING

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

UNIT 4. DEEP LEARNING

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

UNIT 1. SUPERVISED DEEP LEARNING (I)

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

UNIT 2. SUPERVISED DEEP LEARNING (II)

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

UNIT 3. UNSUPERVISED DEEP LEARNING (I)

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

UNIT 4. UNSUPERVISED DEEP LEARNING (II)

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

UNIT 1. GETTING STARTED

1. Introduction to Power BI
2. Different types of Power BI
3. First simple report
4. Power Query: Data Sources
5. Data Transformation

UNIT 2. DATA MODELING AND DAX

1. Data Modeling
2. Starting with DAX (I)
3. Starting with DAX (II)
4. Getting proficient in DAX (I)
5. Getting proficient in DAX (II)

UNIT 3. DATA VISUALIZATION

1. Table and Matrix
2. Trends
3. How to properly filter your data
4. Bookmarks
5. Drill through

UNIT 4. TAKING IT TO THE NEXT LEVEL

1. Understanding Power BI Service in depth
2. Sharing content in Power BI Service
3. Comparing Power BI Service and Power BI Report Server
4. Integrating Python and R in Power BI Desktop
5. Introducing Bravo for Power BI Desktop

UNIT 1. MACHINE LEARNING

1. Linear regression
2. Logistic regression
3. Basic Neural Network
4. Clustering
5. Principal Component Analysis (PCA)

UNIT 2. DEEP LEARNING

1. Deep learning
2. Optimization
3. Convolutional Neural Network
4. Recurrent Neural Network
5. Natural Language Processing (NLP)

UNIT 3. DATA SCIENCE

1. Creating tables and Reports
2. Transformation and filtering data
3. Data visualization
4. Relation between data tables
5. Dashboard

UNIT 4. CASE STUDY APPLICATION

1. Object detection in images
2. Object classification in images
3. Facial recognition
4. Word detection
5. Business Intelligence application

UNIT 1. INTRODUCTION TO TECHNOLOGY ECOSYSTEMS

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

UNIT 2. ENABLING TECHNOLOGIES (I)

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

UNIT 3. ENABLING TECHNOLOGIES (II)

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

UNIT 4 ENABLING TECHNOLOGIES (III)

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

UNIT 1. INTRODUCTION

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

UNIT 2. DESIGN THINKING

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

UNIT 3. LEAN START-UP AND SCRUM

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

UNIT 4. APPLICATION TO AI PROJECTS

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

UNIT 1. AI APPLIED TO DIFFERENT SECTORS

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

UNIT 2. AI APPLIED TO DIFFERENT BUSINESS AREAS

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

UNIT 3. AI AND ENTREPRENEURSHIP

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

UNIT 4. ETHICS. BUSINESS AND SOCIETY

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