Chapter 2-Supervised Learning
Chapter2-Supervised Learning-Part 2
Chapter 3- Unsupervised Learning
Chapter 4- introduction to Neural Network
Chapter 5-Model Evaluation
Lab Session-Data Preprocessing
Practical Session-Classification
Practical Session-Clustering
Practical Session- Introduction to Python Programming
Practical Session- Introduction to Python Programming
Practical Session-Neural Network
Practical Session-Regression
Introduction to Machine Learning
Overview
Course Description
Machine Learning is the study of how to build computer systems that learn from experience. This course will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.
Course Outline
Course objectives
At the end of the course, students should:
- Know about the fundamental concepts in machine learning, the different classes of machine learning algorithms, and ways to choose and apply different basic machine learning algorithms.
- Learn about ways to evaluate the performance of learning systems.
- Be able to prepare data and apply machine learning methods to achieve a learning goal within an intelligent system.
- Be able to judge the suitability of a machine learning paradigm for a given problem and the available data, have an understanding of the capabilities and limitations of the considered machine learning algorithms, and is able to identify problems or misleading results.
Course outline
Chapter 1: Introduction
- What is machine learning?
- History and relationships to other fields
- Essential math and statistics for machine learning
- Applications of machine learning
- Types of machine learning techniques
Chapter 2: Supervised learning
- Introduction
- Linear model
- Regression
- Understand the operation regression
- Linear regression
- Polynomial regression
- Regularization techniques
- Understand the metrics used to evaluate regression
- A case study in regression
- Classification
- Understand the operation of classifiers
- KNN
- Naïve Bayes
- Logistic regression
- Decision trees
- Random forest
- Support vector machines
- A case study in classification
- Understand the metrics used to evaluate classifiers
- How to improve supervised models
- Parametric models for classification and regression
- Understand the problems of over-parameterization and the curse of dimensionality
- Use regularization on over-parameterized models
Chapter 3: Unsupervised learning
- Introduction
- Understand the principles of unsupervised learning models
- Clustering approaches
- K-Means
- K nearest neighbors
- Hierarchical clustering
- Correctly apply and evaluate clustering models
- Association rule learning
- Apriori algorithm
- Reinforcement learning
- Markov decision
- Monte Carlo prediction
- Case study
Chapter 4: Neural Network
- Introduction
- Understanding the brain
- Neural networks as a paradigm for parallel processing
- The Perceptron
- Training a Perceptron
- Artificial neural network
- Multilayer Perceptron
- Back propagation algorithm
- Nonlinear Regression
- Two-Class Discrimination
- Multiclass Discrimination
- Multiple Hidden Layers
- Training procedures
- Improving convergence
- Overtraining
- Structuring the network
- Tuning the network size
- A case study in neural network
- Introduction
Chapter 5: Model Evaluation
- Data processing
- Data cleaning and transforming
- Feature selection and visualization
- Model selection and tuning
- Methods of dimensional reduction
- Principal component analysis (PCA)
- Singular value decomposition (SVD)
- T-distributed Stochastic Neighbor Embedding (t-SNE)
- Optimize the performance of the model
- Control model complexity
- Over-fitting and Under-fitting
- Cross-Validation and Re-sampling methods
- K-Fold Cross-Validation
- 5 ×2 Cross-Validation
- Bootstrapping
- Gradient descent (batch, stochastic)
- Bias, variance
- Performance evaluation methods
Introduction
This chapter serves as an introduction to the fundamental aspects of machine learning, covering key concepts such as its definition, historical development, and intricate connections with other fields. Furthermore, it explores the indispensable mathematical and statistical foundations essential for a comprehensive understanding of machine learning. The chapter also covers diverse applications of machine learning across various domains, and sheds light on the different types of learning in this dynamic field.
Supervised Learning
In this chapter, our goal is to introduce the foundational principles of supervised learning. As we progress, we place particular emphasis on both regression and classification techniques, offering learners a more comprehensive perspective on the practical application of these methodologies in real-world scenarios. By the end of this chapter, learners will not only possess a robust understanding of the core principles but will also be armed with valuable insights into the tangible applications of supervised learning. This knowledge empowers them to skillfully navigate and leverage the full potential of this influential paradigm within the vast expanse of machine learning.
Unsupervised Learning
This chapter discusses unsupervised learning techniques, with a particular emphasis on two popular approaches: clustering and association rule learning. The chapter extensively covers two key clustering algorithms: K-means and hierarchical clustering. Learners gain a comprehensive understanding of how these algorithms work, including their strengths and applications in grouping data points based on inherent patterns. The chapter also covers association rule learning, which involves discovering meaningful associations with datasets, focusing on two prominent algorithms: Apriori and FP-Growth. By engaging in in-depth discussions, learners gain a better grasp of how these algorithms enable the extraction of relevant rules from complex datasets.
In a nutshell, this chapter equips learners with a solid foundation in unsupervised learning, providing them with practical insights into clustering and association rule learning through in-depth discussions of K-means, hierarchical clustering, Apriori, and FP-Growth algorithms.
Introduction to Neural Network
This chapter provides a comprehensive exploration of the principles underlying neural networks, offering a detailed introduction to this groundbreaking field. We begin by examining the core principles that govern neural networks, drawing parallels to the complicated workings of the human brain. Furthermore, the chapter presents an extensive explanation of perceptrons, which form the foundational building blocks of neural networks. Progressing forward, the chapter navigates through the complexities of multilayer perceptrons, demonstrating their capabilities through practical examples and problem-solving scenarios. Through this exploration of multilayer perceptrons, students gain invaluable insights into how neural networks can be structured to tackle real-world challenges effectively. In essence, this chapter serves as a crucial stepping stone in comprehending neural networks, equipping students with the knowledge and skills necessary to navigate this dynamic and rapidly evolving field with confidence and proficiency.
Model Evaluation
This chapter, i.e., organized into two sections, introduces the key concepts of model evaluation in machine learning. The first section provides an overview of key terminology, model evaluation metrics tailored to various tasks, and introduce the concepts of the bias-variance tradeoff. The following section covers important data preprocessing techniques such as data cleansing, transformation, and reduction.