Artificial Intelligence (AI) and Machine Learning

In the field of Artificial Intelligence (AI) and Machine Learning at Paris Metropolitan University, students will delve into the foundations and applications of cutting-edge technologies. They will learn about various AI algorithms, data preprocessing, feature engineering, model training, and evaluation techniques.

By the end of the program, students can expect to gain a deep understanding of AI concepts, develop the skills to build intelligent systems, and be prepared to tackle real-world challenges in industries such as healthcare, finance, automation, and more, contributing to advancements in AI and shaping the future of technology.

This curriculum aims to provide a comprehensive understanding of AI and Machine Learning principles, algorithms, and practical applications. Students will learn to develop, evaluate, and deploy machine learning models while gaining hands-on experience through projects and real-world applications. By the end of the program, students will be equipped with the knowledge and skills to contribute to AI research, tackle complex data-driven problems, and pursue careers in areas such as data science, AI engineering, research, or further academic study in the field.

Course information

  1. Educational Background: Possess a high school diploma or its equivalent. Prior coursework or experience in mathematics or computer science may be advantageous but is not mandatory.
  2. Language Proficiency: Demonstrate proficiency in the English language, as the course may be delivered in English. Applicants may need to provide evidence of their English language skills, such as a valid English proficiency test score.
  3. Application Form: Complete the university’s application form, providing personal and educational information.
  4. Statement of Purpose: Submit a statement of purpose explaining their motivations, career goals, and expectations from the program. This statement helps the admissions committee assess the applicant’s alignment with the program’s objectives.
  5. Identification Documentation: Submit a clear copy of their ID or passport to verify their identity.
  6.  
  1. Coursework: Attend and actively participate in all modules and classes, demonstrating a commitment to learning and a solid understanding of the subject matter.
  2. Assignments and Projects: Complete assignments, projects, and assessments assigned during the course, showcasing the application of knowledge and practical skills.
  3. Examinations: Successfully pass any required examinations or assessments that are part of the course evaluation.
  4. Capstone Project: Successfully complete the final project-based learning module, which involves building a software application or system. This project serves as a culmination of the skills and knowledge acquired during the program.
  5. Attendance: Maintain satisfactory attendance and participation in classes and activities throughout the course duration.
  6.  

Module 1:

  • Introduction to Artificial Intelligence and Machine Learning
  • Supervised Learning: Linear Regression, Logistic Regression, and Decision Trees
  • Model Evaluation and Performance Metrics

Module 2:

  • Unsupervised Learning: Clustering Algorithms (K-Means, Hierarchical, DBSCAN)
  • Dimensionality Reduction Techniques (PCA, t-SNE)
  • Introduction to Neural Networks and Deep Learning

Module 3:

  • Deep Learning: Feedforward Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
  • Natural Language Processing (NLP) Fundamentals
  • Model Optimization and Regularization Techniques

Module 4:

  • Advanced Deep Learning Architectures: Generative Adversarial Networks (GANs), Reinforcement Learning, and Transfer Learning
  • Time Series Analysis and Forecasting
  • Ethical and Responsible AI

Module 5:

  • Advanced Topics in Machine Learning: Support Vector Machines (SVMs), Ensemble Methods, and Recommender Systems
  • Large-Scale Data Processing with Apache Spark
  • Deploying Machine Learning Models

Module 6:

  • Real-world AI Applications: Computer Vision, Robotics, and Autonomous Systems
  • Capstone Project: Solving a Complex Problem using AI and Machine Learning
  • Industry Perspectives and Emerging Trends in AI

Course Details:

  • Assessment Method: Combination of Coursework and Capstone Project
  • Program Duration: 3 months
  • Study Mode: Online
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