INTRODUCTIONTOML2024 for graphic era.pptx

Creating a detailed 4000-word introduction for a PowerPoint presentation named "INTRODUCTIONTOML2024 for Graphic Era.pptx" would require an in-depth understanding of the specific content, objectives, and context of the presentation. Since I can't access the content of external files like PowerPoint presentations, I'll provide a general approach you can take to create such an introduction:

Title: Introduction to ML 2024 - A Path Towards Innovation

1. Overview of ML 2024 Presentation:

Briefly introduce the purpose and scope of the presentation.
Highlight the importance of Machine Learning (ML) in driving innovation and advancements in various fields.
2. Evolution of Machine Learning:

Trace the evolution of ML from its early roots to its current state.
Discuss key milestones, breakthroughs, and advancements that have shaped the field.
3. Importance of Machine Learning in the 21st Century:

Explore the significance of ML in addressing complex challenges and driving technological progress.
Discuss real-world applications of ML across industries such as healthcare, finance, transportation, and entertainment.
4. ML Trends and Predictions for 2024:

Provide insights into the current trends and emerging technologies in ML.
Discuss predictions for the future of ML in 2024, including advancements in deep learning, reinforcement learning, and natural language processing.
5. Role of Graphic Era University in Advancing ML:

Highlight the contributions of Graphic Era University in the field of ML.
Showcase research initiatives, collaborations, and academic programs focused on ML at the university.
6. Objectives of the Presentation:

Define the specific objectives and goals of the ML 2024 presentation.
Outline the key topics, themes, and discussions that will be covered.
7. Target Audience:

Identify the target audience for the presentation, including students, faculty, industry professionals, and researchers.
Tailor the content and delivery approach to meet the needs and interests of the audience.
8. Structure of the Presentation:

Provide an overview of the structure and organization of the presentation.
Outline the sequence of topics, sections, and slides that will be covered.
9. Conclusion:

Summarize the key points discussed in the introduction.
Emphasize the significance of ML in driving innovation and shaping the future.
Invite the audience to engage with the presentation and participate in discussions.
10. References and Further Reading:

Provide a list of references, resources, and recommended reading materials for those interested in learning more about ML.
By following this structured approach, you can create a comprehensive introduction for the ML 2024 presentation, setting the stage for an engaging and informative discussion on the latest trends and developments in the field of Machine Learning.

1. MACHINE LEARNING INTRODUCTION

2. Machine Learning (ML) ML is a branch of artificial intelligence: Uses computing based systems to make sense out of data Extracting patterns, fitting data to functions, classifying data, etc ML systems can learn and improve With historical data, time and experience Bridges theoretical computer science and real noise data. Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Solve the optimization problem Representing and evaluating the model for inference

3. Growth of Machine Learning Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers Software too complex to write by hand New sensors / IO devices Demand for self-customization to user, environment It turns out to be difficult to extract knowledge from human experts  failure of expert systems in the 1980’s.

4. ML in real-life :-

5. Applications -: Supervised Learning Classification Regression /Prediction Unsupervised Learning Reinforcement Learning

6. Supervised Learning Supervised Learning For every example in the data there is always a predefined outcome Models the relations between a set of descriptive features and a target (Fits data to a function) 2 groups of problems: Classification Regression

7. Supervised Learning Types -: Classification Predicts which class a given sample of data (sample of descriptive features) is part of ( discrete value ). : The rule is easy to understand C The rule is simpler than the data it explain : Exceptions that are not covered by the rule, e.g., fraud

8. Regression Example: Price of a used car x : car attributes y : price y = g ( x | θ ) g ( ) model, θ parameters

9. Unsupervised Learning Learning “what normally happens” No output Clustering: Grouping similar instances Other applications: Summarization, Association Analysis Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs

10. Reinforcement Learning Policies : what actions should an agent take in a particular situation Utility estimation: how good is a state ( used by policy) No supervised output but delayed reward Credit assignment problem (what was responsible for the outcome) Applications: Game playing Robot in a maze

11. ANN -: Artificial Neural networks (ANN) or neural networks are computational algorithms. It intended to simulate the behavior of biological systems composed of “neurons”. ANNs are computational models inspired by an animal's central nervous systems. It is capable of machine learning as well as pattern recognition.

12. DECISION TREE A decision tree is a decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility

13. SUPPORT VECTOR MACHINE The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane . SVM chooses the extreme points/vectors that help in creating the hyperplane . These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine. Consider the below diagram in which there are categories that are classified using a decision boundary or hyperplane :

14. NAÏVE BAYES Naïve Bayes algorithm is a supervised learning algorithm, which is based on  Bayes theorem  and used for solving classification problems. It is mainly used in  text classification  that includes a high-dimensional training dataset. Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.

15. Logistic regression is a simple and more efficient method for binary and linear classification problems. It is a classification model, which is very easy to realize and achieves very good performance with linearly separable classes. It is an extensively employed algorithm for classification in industry.

16. THANKS FOR WATCHING