Introduction on artificial Intelligence and Machine Learning

In short time understanding with Artificial Intelligence and Machine Learning

1. Artificial Intelligence And Machine Learning Presented by K M Pandey

2. Tentative Outline What is Intelligence? Introduction of Artificial Intelligence How does A I work? Neural Networking Difference between Artificial Intelligence And Machine Learning. Machine learning. Supervised Learning. Unsupervised Learning. Reinforcement Learning

3. What is Intelligence??? Intelligence is the ability to learn about, to learn from, to understand about, and interact with one’s environment. Intelligence is the faculty of understanding

4. What Is Artificial Intelligence??? Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. A.I is the study of ideas that enable computers to be intelligent John McCarthy, widely recognized as one of the godfathers of  Artificial Intelligence (AI) , defined AI as “ the science and engineering of making intelligent machines that have the ability to achieve goals like humans do ” in the year 1955. In short,  Artificial Intelligence is human intelligence exhibited by Machines.

5. How Does AI Works?? Artificial intelligence works with the help of Artificial Neurons (Artificial Neural Network) And Scientific theorems(If-Then Statements, Logics)

6. An Artificial Neuron Sec 2: ANN 6 W 1 W 2 W 3 W n  W f X 1 X 2 X 3 Xn Axons Synapses Dendrites Body (Soma) Axon Bias Output (y)

7. Actions Agent Sensors Actuators ? Environment Percepts Intelligent agents

8. Machine Learning! Machine learning is a scientific discipline concerned with the design and development of algorithms that allow machines to mimic human intelligence.

9. Machine Learning is the part of Artificial Intelligence

10. Machine Learning “Learning denotes changes in a system that ... enable a system to do the same task … more efficiently the next time.” - Herbert Simon “Learning is constructing or modifying representations of what is being experienced.” - Ryszard Michalski “Learning is making useful changes in our minds.” - Marvin Minsky “Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge.”

11. Why Machine Learning? No human experts industrial/manufacturing control mass spectrometer analysis, drug design, astronomic discovery Black-box human expertise face/handwriting/speech recognition driving a car, flying a plane Rapidly changing phenomena credit scoring, financial modeling diagnosis, fraud detection Need for customization/personalization personalized news reader movie/book recommendation

12. - Supervised Learning : Find the class labels or value of the new input, given the dataset. ( Linear Regression  is a  machine learning  algorithm based on supervised  learning) Brief Introduction on Supervised Learning, Unsupervised Learning And Reinforced Learning - In game theory: Learn to act in a way that maximized the future rewards, in an environment that contains other machines. - Unsupervised Learning: contains neither targert outputs or reward from its environment. - Reinforcement learning: Learn to act in a way that maximizes the future rewards (or minimizes a cost function)

13. Types of Inductive (Supervised) Learning Types of Supervised Learning: Supervised learning is classified into two categories of algorithms: Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”. Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” , “disease” or “no disease”. Supervised learning deals with or learns with “labeled” data. This implies that some data is already tagged with the correct answer.

14. - Features can be: - continuous - categorical - binary - Training set: The output of each data point is known. - Training Algorithms... - Test set: The output of each data point is estimated. - Output can be: - a class label - a real number SUPERVISED LEARNING

15. - No supervised target outputs - No rewards from the environment - No feedback (UNSUPERVISED LEARNING) SO? Build representations of the inputs Find patterns in the inputs Decision making Predict future inputs

16. Types of UNSUPERVISED LEARNING Unsupervised learning is classified into two categories of algorithms: 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.

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