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AI - ML - DL
- 1. AI – ML - DL Rıdvan SIRMA UnrestrictedUnrestricted
- 2. What is AI ? ``The exciting new effort to make computers think ... machines with minds, in the full and literal sense'' (Haugeland, 1985) ``The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning ...'' (Bellman, 1978) ``The art of creating machines that perform functions that require intelligence when performed by people'' (Kurzweil, 1990) ``The study of how to make computers do things at which, at the moment, people are better'' (Rich and Knight, 1991)
- 3. What is AI ? ``The study of mental faculties through the use of computational models'' (Charniak and McDermott, 1985) ``The study of the computations that make it possible to perceive, reason, and act'' (Winston, 1992) ``A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes'' (Schalkoff, 1990) ``The branch of computer science that is concerned with the automation of intelligent behavior'' (Luger and Stubblefield, 1993)
- 4. What is AI ? Systems that think like humans. Systems that think rationally. Systems that act like humans Systems that act rationally
- 6. What is ML ? According to Arthur Samuel, Machine Learning algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed. According to Tom Mitchell, a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
- 8. Supervised Learning The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
- 10. Classification Problems: Classification problem can be defined as the problem that brings output variable which falls just in particular categories, such as the “red” or “blue” or it could be “disease” and “no disease”. Regression: A regression problem is when the output variable is a real value, such as “dollars” or it could be “weight”. The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete). Classification vs Regression
- 11. • Decision Trees • Naive Bayes Classification • Support vector machines for classification problems • Random forest for classification and regression problems • Linear regression for regression problems • Ordinary Least Squares Regression • Logistic Regression Supervised Learning Algorithms
- 12. Unsupervised Learning In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training.
- 13. Clustering vs Association Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
- 14. • K-means for clustering problems • Apriori algorithm for association rule learning problems • Single Link Clustering Supervised Learning Algorithms
- 16. Reinforcement Learning A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.
- 17. • Q-Learning • State-Action-Reward-State-Action Reinforcement Learning Algorithms
- 19. What is Deep Learning ? Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
- 20. What is Deep Learning ? Deep-learning networks are distinguished from the more commonplace single-hidden-layer neural networks by their depth; that is, the number of node layers through which data must pass in a multistep process of pattern recognition.