Machine Learning Model overview Types of ML models
Description: Machine Learning Model overview Types of ML models New developments (if you are interested in research) We can design supervised training tasks for unlabeled data Self-supervised learning: generate labels from data, e.g., word2vec, BERT
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slide1. Machine Learning Model overview<br>
slide2. Types of ML models<br>
slide3. New developments (if you are interested in research) We can design supervised training tasks for unlabeled data
Self-supervised learning: generate labels from data, e.g., word2vec, BERT
GAN: generating fake data with trivial label from unlabeled data
Contrastive learning – learning the best vector representation of non-vector data
Similar kinds positive labels
Different kinds negative labels<br>
slide4. Components in supervised learning (from optimization’s angle)<br>
slide5. Tricky parts – loss functions Loss function – directly related to the specific ML problem
Common loss functions
Mean squared errors for regression
Cross-entropy --> for logistic/softmax regression and classification
Hinge-loss Support vector machine
Etc. – we will discuss more details later<br>
slide6. Types of Models Linear methods: decision is made from a linear combination of input features
Decision trees: use trees to make decisions
Probabilistic models, e.g., naïve bayes classifier: bayes rule
Kernel machines, e.g., SVM and kNN: use kernel function to compute feature similarity
Neural networks: use NN to learn feature representation<br>
slide7. Summary Supervised
Learning Semi-supervised
Learning Unsupervised
Learning Reinforcement
Learning Objective Loss function Models Optimization Linear Models Decision Trees Naïve Bayes Neural Networks Kernel machines<br>
slide8. Question How does the idea of ”function approximation” link the major concepts in supervised learning (loss, objective, and optimization)?<br>
slide2. Types of ML models<br>
slide3. New developments (if you are interested in research) We can design supervised training tasks for unlabeled data
Self-supervised learning: generate labels from data, e.g., word2vec, BERT
GAN: generating fake data with trivial label from unlabeled data
Contrastive learning – learning the best vector representation of non-vector data
Similar kinds positive labels
Different kinds negative labels<br>
slide4. Components in supervised learning (from optimization’s angle)<br>
slide5. Tricky parts – loss functions Loss function – directly related to the specific ML problem
Common loss functions
Mean squared errors for regression
Cross-entropy --> for logistic/softmax regression and classification
Hinge-loss Support vector machine
Etc. – we will discuss more details later<br>
slide6. Types of Models Linear methods: decision is made from a linear combination of input features
Decision trees: use trees to make decisions
Probabilistic models, e.g., naïve bayes classifier: bayes rule
Kernel machines, e.g., SVM and kNN: use kernel function to compute feature similarity
Neural networks: use NN to learn feature representation<br>
slide7. Summary Supervised
Learning Semi-supervised
Learning Unsupervised
Learning Reinforcement
Learning Objective Loss function Models Optimization Linear Models Decision Trees Naïve Bayes Neural Networks Kernel machines<br>
slide8. Question How does the idea of ”function approximation” link the major concepts in supervised learning (loss, objective, and optimization)?<br>