Course Introduction

Course Introduction. What is machine learning (ML) ? Why machine learning is very important ? Useful tasks for machine learning Types of Machine Learning.

Traditional Programming. Machine Learning. Data Output.

Why ML is very important ?. Recently, ML is applied for solving tasks for large- scale application domains. The industry starts rethinking to incorporate machine learning (AI solutions) to automate its process..

Useful tasks for machine learning. 1. Classification: Determine the target category of an input ( sample).

Useful tasks for machine learning. Example of a classification task.

Useful tasks for machine learning. Classification: determine the target category of an input (the sample or example). Recognizing patterns: speech recognition, facial identity, etc..

Useful tasks for machine learning. Example of a speech recognition task.

Useful tasks for machine learning. Classification: Determine the target category of an input (the sample or example). Recognizing patterns: Speech Recognition, facial identity, etc. Recommender Systems: Recommendation to users (e.g., Amazon, Netflix)..

Useful tasks for machine learning. Example of a recommendation task.

Useful tasks for machine learning. Classification: Determine the target category of an input (the sample or example). Recognizing patterns: Speech Recognition, facial identity, etc. Recommender Systems: Recommendation to users (e.g., Amazon, Netflix). Information retrieval: Find documents or images with similar content.

Useful tasks for machine learning. Example of an Information retrieval task.

Useful tasks for machine learning. Classification: Determine the target category of an input (the sample or example). Recognizing patterns: Speech Recognition, facial identity, etc. Recommender Systems: Recommendation to users (e.g., Amazon, Netflix). Information retrieval: Find documents or images with similar content. Computer vision: Detection, Classification, etc..

Useful tasks for machine learning. Example of a computer vision task (Detection).

Useful tasks for machine learning. a) Normal retinal image b) Abnormal retinal image.

Types of Machine learning. Machine Learning. Course Introduction.

Course Introduction. PART ONE: DATA ANALYSIS Data Matrix Types of Attributes Graph Data Kernel Methods Dimensionality Reduction PART TWO: CLASSIFICATION Nearest Neighbors Classifier Linear Discriminant Analysis Support Vector Machines PART THREE: CLUSTERING K-means Algorithm DBSCAN Algorithm PART Four: REGRESSION Linear Regression Model Logistic Regression.