Introduction to Artificial
Intelligence & Machine Learning
Nicolas Thome Professor at Cnam Computer science dpt Machine Learning team
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 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 vs overfitting
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
