Cours introduction Machine learningcedric.cnam.fr/~thomen/cours/DUI5/Cours_intro_ML.pdf · Cours...
Transcript of Cours introduction Machine learningcedric.cnam.fr/~thomen/cours/DUI5/Cours_intro_ML.pdf · Cours...
Introduction to ArtificialIntelligence & Machine Learning
Nicolas ThomeProfessor at Cnam
Computer science dptStatistical Learning team (MSDMA)
1.Definition of AI and ML
2.Unsupervised learning
3.Supervised learning
Outline
Artificial Intelligence• Building machines able to solve problems, work & react like humans
• Requiring understanding of the problem
• Very general, being able to • Acquire and understand information from the world, environment => perception
• Image, audio, text, … and any sensor / measurement (physics )
Artificial Intelligence• Building machines able to solve problems, work & react like humans
• Requiring understanding of the problem
• Very general, being able to • Perform action in the world
• Robot, chatbot, playing games, etc
Artificial Intelligence & big data• Big data => huge number of data
• Impossible to manually to process such data=> Obvious need for automatic processing
• Big data applications: essentially all data sience domains• Email filtering, Online recommendations
• Voice recognition, Face recognition
• Medical diagnosis
• Autonomous driving
Artificial Intelligence & Machine learning
AI ambiguous
Historical Artificial Intelligence• Traditional IA (1950-1990): symbolic problems
• Constraint satisfaction problem (CSP) => Optimization/ search issues
• games (chess, go), Travelling salesman problem, etc
• Ex: Travelling salesman problem (TSP)
• Find the shortest path to visit all n cities• Exhaustive search: O(n!) • Explodes very quickly with n
Historical Artificial Intelligence: Expert systems
• Knowledge base collected by experts, expressed by if-then rules
• Inference: deduce new facts from knowledge
Artificial Intelligence & Machine learning
• Traditional AI: explicit rules, handcrafted programs• Difficult to build and maintain knowledge database
• For many pbs: impossible to explicitly express rules (ex: image classification)
• ML: rules learned from data, emerged from data
Machine learning: methods and supervision
• Unsupervised vs supervised learning
Machine learning: methods and supervision
• Reinforcement learning
Machine learning & generalization
• Inductive learning: training database => extract rules•Apply to new data
•Machine Learning ≠ optimization
Under-fitting vsoverfitting
Machine learning: representation
• For many tasks: input representations not adequate
Deep learning: learning representations
• ML on hacrafted features • DL on raw data
1.Definition of AI and ML
2.Unsupervised learning
3.Supervised learning
Outline
Unsupervised learning
• General motivation: learning the structure of data
• Useful for: • Clustering
• Visualization
• Learning representations, manifold learning etc …
K-Means
K-Means
K-Means
K-Means: python example on MNIST for clustering
Cluster with min entropy
K-Means: python example on MNIST for clustering
Cluster with max entropy
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
Unuspervised learning
• And many other methods…• Generative models, e.g. Gaussian Mixture Models (GMMs)
• Maximum likelihood vs Maximum a Posteriori
1.Definition of AI and ML
2.Unsupervised learning
3.Supervised learning
Outline
Supervised learning
• General methods • Decision trees and variants (random forest)
• K-NN (nearest neighbor): For each test example, simply find its closest example• Or compute k-NN, and apply majority class voting
Supervised learning
=> Train a model with gradient descent
Supervised learning: gradient descent
Supervised learning
Neural Networks
Deep Neural Networks
Deep Neural Networks & expressivity
Deep Neural Networks: Training with backprop
Backprop: chain rule
Deep Neural Networks: specific architectures
Deep Neural Networks: specific architectures
Deep Neural Networks: specific architectures
Deep Neural Networks: specific architectures
Deep learning History
Deep learning History
Deep learning History
Deep learning since 2012
Deep learning since 2012
Deep learning since 2012: ressources
Deep learning & AI: ongoing issues
Deep learning & AI:ongoing issues