El programa formativo Samsung Innovation Campus ofrece a los jóvenes la oportunidad de impulsar su aprendizaje y mejorar su empleabilidad.
Los conocimientos que conforman el núcleo del plan de estudios de Samsung Innovation Campus (SIC) se basan en tecnologías clave para la Cuarta Revolución Industrial, que incluyen Inteligencia Artificial (IA), Internet de las Cosas (IoT) o Big Data.
La formación es totalmente gratuita y se imparte en colaboración con algunas Universidades. Al finalizar el programa se entrega un certificado de aprovechamiento si se finaliza el curso con éxito.
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This AI course is intended for students to learn the essential foundations of AI and gain the fundamental
data science skills through hands-on exercises.
Specific objectives
- Understand the foundational math behind data science and machine learning: linear algebra,
probability, and statistics. - Be able to do data preprocessing with the Python libraries (NumPy and Pandas) for the execution
of optimal machine learning models and data visualization. - Explore supervised and unsupervised learning and be able to apply the most suitable machine
learning algorithm. - Learn to process textual data to derive highquality information from text and apply new insights to
real-world business (NLP) - Build and train deep neural networks, use the deep learning libraries such as TensorFlow and Keras
to gain proficiency, as well as handle various deep learning techniques.
Access profile
- Required:
- Mathematics:
- Strong foundation of algebra
- Basic understanding of linear algebra
- Coding Experience: Python programming
- Be able to use Jupyter Notebook
- Python basics: variables, conditionals, functions, and input/output
- Python data type: basic and compound (list, tuple, dictionary, and set)
- Mutable sequences
- Python class
- Mathematics:
- Recommendable:
- Data structure (stacks, queues) and algorithm (sorting and searching) as problem solving ability in Python (Advanced Python)
- Basic understanding of probability and statistics fundamentals
Course Contents
Chapter 1. Introduction to Artificial Intelligence
- Unit 1. The Concept of Artificial Intelligence
- Unit 2. Applications of Artificial Intelligence
- Unit 3. Techniques in Artificial Intelligence
- Unit 4. Artificial Intelligence: Trends and Markets
- Unit 5. Course Roadmap
Chapter 2. Math for Data Science
- Unit 1. Introduction
- Unit 2. Basic Math for Data Science
- Unit 3. Understanding Data Science: Vectors
- Unit 4. Understanding Data Science: Matrix
- Unit 5. Understanding Deep Learning: Derivatives
- Quiz
Chapter 3. NumPy Arrays for Optimized Numerical Computation & Pandas for Exploratory Data Analysis
- Unit 1. NumPy Array Data Structure for Optimal Computational Performance
- Unit 2. Optimal Data Exploration Through Pandas
- Unit 3. Pandas Data Preprocessing for Optimal Model
- Unit 4. Data Visualization For Various Data Scales
- Quiz
Chapter 4. Probability and Statistics
- Unit 1. Understanding of Probability
- Unit 2. Understanding of Statistics I
- Unit 3. Understanding of Statistics II
- Unit 4. Statistical Hypothesis Testing
- Quiz
Chapter 5. Machine Learning – Supervised Learning
- Unit 1. Machine Learning Based Data Analysis
- Unit 2. Supervised Learning Model for Numerical Prediction
- Unit 3. Supervised Learning Model for Classification
- Unit 4. Decision Tree
- Unit 5. Naïve Bayes Algorithm
- Unit 6. KNN Algorithm
- Unit 7. SVM Algorithm
- Unit 8. Ensuring Algorithms
- Quiz
Chapter 6. Machine Learning – Unsupervised Learning
- Unit 1. Unsupervised Learning Machine Learning Algorithm
- Unit 2. Hierarchical Clustering
- Unit 3. Non-Hierarchical Clustering
- Unit 4. Linear Factor Model for Dimensionality Reduction
- Quiz
Chapter 7. Natural Language Processing and Language Models for Text Mining
- Unit 1. Text Mining
- Unit 2. Text Preprosessing
- Unit 3. Language Models
- Unit 4. Natural Language Processing with Keras
- Quiz
Chapter 8. Neural Network and Deep Learning
- Unit 1. Understanding Neural Network
- Unit 2. Basics of TensorFlow
- Unit 3. Deep Learning Metods using TensorFlow Structure and Keras
- Quiz
Chapter 9. Various Deep Learning Techniques: Deep Learning Techniques for Video and Language Intelligence
- Unit 1. CNN for Computer Vision
- Unit 2. RNN for Sequential Data Modeling
- Unit 3. Generative Adversarial Neural Network to Create Non-Existent Images
- Quiz
Chapter 10. Starting an AI Project
- Project Preparation
- Design Thinking
Chapter 11. AI Capstone Project Tutorial
- Using a Ready-Made CNN Model
- AI Application Cases
During the capstone project, student’s project activities take more time than lecture
itself. Please expect up to 80 hours to complete the whole project.
| Título de la Actividad | Samsung Innovation Campus de Big Data (C31040) |
| Entidad Organizadora | Servicio de Formación Permanente Vicerrectorado de Estrategia y Ordenación Académica |
| ECTS reconocidos | 3 ECTS |
| Duración | 240 horas |
| Modalidad | Presencial |
| Lugar de impartición | ETSI Sistemas Informáticos |
| Fecha de impartición | Pendiente de confirmar |
| Plazas ofertadas | 22 |
| Contacto | formacion.permanente@upm.es |
| Importe de la actividad | – |
