Titlebook: Machine Learning and Artificial Intelligence; Ameet V Joshi Book 20201st edition Springer Nature Switzerland AG 2020 Artificial Intelligen

Heresy

Support Vector Machinesalternative to neural networks, when neural networks were not performing up to the grand expectations that they came with. SVM proposed a very targeted mathematical approach towards finding the optimal solution in case of classification or regression. We will first study the original SVM theory that

CRANK

Probabilistic Models discriminative and generative models. In the discriminative models we will study the concepts of Bayesian approach and Maximum likelihood approach. We will derive the solution of a same problem using both approaches to illustrate the differences and advantages and disadvantages. Then we will study

Lobotomy

Dynamic Programming and Reinforcement Learningr dive deep into its generalization. We will understand the class of problems that can be solved with the framework of dynamic programming. Then we will study reinforcement learning as one subcategory of dynamic programming in detail. We will study the concepts of exploration and exploitation and th

forebear

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過濾

Deep Learninge become extremely popular tools in modern machine learning due to tremendous success they have achieved using the distributed and parallel computing technology available at disposal. We will study two specific types of deep networks in the form of convolutional neural networks (CNN) and recurrent n

Truculent

Emerging Trends in Machine Learningto the existing techniques, while some of them may seem outright crazy and futuristic. Most of the techniques discussed here are in their infancy and need significant research efforts to mature. However, each one of these techniques represents an area of active research. Any of these techniques, if

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耐寒