Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

1. WHAT IS MACHINE LEARNING?

2. Machine learning (ML) is a type of artificial intelligence ( AI ) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time . 2

3. Machine learning  algorithms  are trained to find relationships and patterns in data. They use historical data as input to make predictions, classify information, cluster data points, reduce dimensionality and even help  generate new content , as demonstrated by new ML-fueled applications such as ChatGPT, Dall-E 2 and GitHub Copilot. MACHINE LEARNING EXAMPLES : machine learning is widely applicable across many industries.  Recommendation engines , for example, are used by e-commerce, social media and news organizations to suggest content based on a customer's past behavior. Machine learning algorithms and  machine vision  are a critical component of self-driving cars, helping them navigate the roads safely. In healthcare, machine learning is used to diagnose and suggest treatment plans. Other common  ML use cases  include fraud detection, spam filtering, malware threat detection, predictive maintenance and business process automation . 3

4. ML Process

5. The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement

6. Step 1-> Problem Definition : Specify the goal of weather prediction, such as forecasting temperature, precipitation, or severe weather events . Step 2-> Data Collection: Gather historical weather data, including temperature, humidity, wind speed, and atmospheric pressure, from various sources like weather stations and satellites

7. Step 3: Preparing the Data * Data cleaning involves getting rid of inconsistencies in data as missing values or redundant variables . * Transform Data into desired format . *Data cleaning -> Remove Unnecessary data ->Missing values ->Corrupted Data

8. Step 4: Exploratory Data Analysis Data exploration involves understanding the patterns and trends in data. At this stage all the insights are drawn and correlations between the variables are understood. Step 5: Building the Machine Learning Model ▪ At this stage a Predictive Model is built by using Machine Learning Algorithms such as Linear Regression, Decision Trees, etc. ▪ Machine Learning model is built by using the training data set. ▪ The model is the machine learning algorithm that predicts the output by using the data fed to it.

9. Step 6: Model evaluation and optimization ▪ The efficiency of the model is evaluated and any further improvement in the model are implemented. ▪ ML model is evaluated by using the testing data set. ▪ The accuracy of the model is calculated. ▪ Further improvement in the model are done by using techniques like parameter tuning. The final Outcome is predicted after performing parameter tuning and improving the accuracy of the model . Step 7: Predictions

10. TYPES OF MACHINE LEARNING MACHINE LEARNING SUPERVISED LEARNING UNSUPERVISED LEARNING BROADLY CLASSIFIED INTO THREE TYPES REINFORCEMENT LEARNING

11. 1. SUPERVISED LEARNING Supervised Learning is a type of learning in which we train the machine using well labelled data , which means some data is already tagged with the correct answer/output. Supervised Learning requires external supervision to train the model. This learning model takes direct feedback to check whether it is producing correct output or not. The goal of supervised learning is to train the model so that it can predict the output. ADVANTAGES:- 1.Model can predict the output using its prior knowledge. 2. Helps in solving various real life problems like Spam filtering, Fraud detection etc. DISADVANTAGES:- 1. Not suitable for handling complex tasks. 2.Can not predict the correct output if test data is different from training set.

12. Example- We are given a basket of fruits and we have different kinds of fruits in it. The machine has to predict that the fruit is an apple. LET US TAKE AN EXAMPLE The model identifies the fruit by using various parameters like the shape , color and texture then it is labelled as an Apple .

13. Supervised and unsupervised learning are the two primary approaches in  artificial intelligence  and  machine learning . With supervised learning, an algorithm uses a sample dataset to train itself to make predictions, iteratively adjusting itself to minimize error. In contrast, unsupervised learning algorithms work independently to learn the data's inherent structure without any specific guidance or instruction.

14. Unlike Supervised learning, Unsupervised machine learning models are given unlabeled data and allowed to discover patterns and insights without any explicit guidance or instruction. Unsupervised learning models, for instance, might be used to identify buyer groups that purchase related products together to provide suggestions for other items to recommend to similar customers.

15. Using the example above, we have input data consisting of images of different shapes. Machine learning algorithms try to find the similarity among other images based on the color pixel values, size, and shapes and form the groups as outputs in which similar input instances lie. If you notice, squares get clustered together, and similarly, circles and hexagons.

16. CLUSTERING Clustering is the method of dividing the objects into clusters that are similar between them and are dissimilar to the objects belonging to another cluster. For example, finding out which customers made similar product purchases. Suppose a telecom company wants to reduce its customer churn rate by providing personalized call and data plans. The behavior of the customers is studied and the model segments the customers with similar traits. Several strategies are adopted to minimize churn rate and maximize profit through suitable promotions and campaigns.

17. ASSOCIATION Association is a rule-based machine learning to discover the probability of the co-occurrence of items in a collection. For example, finding out which products were purchased together. Let’s say that a customer goes to a supermarket and buys bread, milk, fruits, and wheat. Another customer comes and buys bread, milk, rice, and butter. Now, when another customer comes, it is highly likely that if he buys bread, he will buy milk too. Hence, a relationship is established based on customer behavior and recommendations are made.

18. Reinforcement Reinforcement learning in machine learning is like a trial-and-error learning process.

19. It’s similar to training a pet: when the pet does something good, it gets a treat, and when it does something bad, it doesn’t. Over time, the pet learns to repeat the good behaviors to get more treats.

20. In reinforcement learning, an AI agent (like a robot or software) learns to make decisions by performing actions and getting rewards or penalties based on the results. The agent isn’t told what to do but instead learns from its experiences, trying to get as many rewards as possible.

21. Applications of reinforcement Autonomous Vehicles: Guides decisions for self-driving cars, including lane changes and obstacle avoidance. Robotics: Used in motion control for navigating and manipulating objects. Game Playing: Excels in training AI for complex games like Go and chess. Natural Language Processing (NLP): Applies to text summarization and question-answering for human-like text generation. Personalized Recommendations: Enhances user experience through tailored content recommendations.

22. Pros 1.Complex problem solving :can solve very complex problem 2.Error correction :capable of correcting errors during training. 3.Performance maximization :intended to maximizing performance within a specific context. Cons 1.Maintenance cost :High maintenance cost due to complexity. 2.Complexity for simple problems not preferable for solving simple problems.

23. PART-2 FIRST ORDER LOGIC (FOL)

24. What is First Order Logic? FOL  is a mode of representation in Artificial Intelligence. It is an extension of PL. FOL represents natural language statements in a concise way. FOL is also called  predicate logic . It is a powerful language used to develop information about an object and express the relationship between objects. FOL not only assumes that does the world contains facts (like PL does), but it also assumes the following: Objects: A, B, people, numbers, colors, wars, theories, squares, pit, etc. 25

25. Relations: It is unary relation such as red, round, sister of, brother of, etc. Function : father of, best friend, third inning of, end of, etc. 26

26. Representing Simple Statements in FOL: I t is important that you know the logical operators/connectives that are used in Propositional Logic. 27

27. Parts of First Order Logic: FOL also has two parts: Syntax Semantics Syntax: The  syntax  of FOL decides which collection of symbols is a logical expression. The basic syntactic elements of FOL are symbols. We use symbols to write statements in shorthand notation. 28

28. Basic Elements of FOL: 29

29. Atomic and Complex Sentences in FOL: Atomic Sentence : This is a basic sentence of FOL formed from a predicate symbol followed by a parenthesis with a sequence of terms. We can represent atomic sentences as a predicate (value1, value2…., value n). Example- John and Michael are colleagues  colleagues (John, Michael) German Shepherd is a dog  Dog ( German Shepherd ) 30

30. Complex Sentence: Complex sentences are made by combining atomic sentences using connectives. FOL is further divided into two parts: Subject: the main part of the statement. Predicate: defined as a relation that binds two atoms together. Example- “x is an integer” 31

31. It has two parts; First, x is the subject. Second, “is an integer” is called a predicate . 32

32. 33

33. 34

34. 35

35. 36 All boys like cricket ∀x: boys(x)  like (x, cricket) Some boys like football ∃ x: boys(x) ∧ like (x, football) Some girls hate football ∃ x: girls(x) ∧ hate (x, football) FOPL Examples: