機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations

Go to Course: https://www.coursera.org/learn/ntumlone-algorithmicfoundations

Introduction

### Course Review: Machine Learning Foundations (Algorithmic Foundations) #### Overview If you're venturing into the realm of machine learning, understanding the fundamental concepts and algorithms is essential. The course titled **機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations**, offered on Coursera, serves as an excellent stepping stone for anyone looking to solidify their knowledge in this dynamic field. This course aligns well with its sister course, which emphasizes mathematical tools, ensuring a comprehensive grasp of both theory and practice in machine learning. #### Course Structure and Syllabus The course consists of several modules, each focusing on critical components of machine learning algorithms. Here’s a brief rundown of the key topics covered: 1. **Linear Regression**: The course begins with an exploration of linear regression, discussing the weight vector for linear hypotheses and the methods for calculating squared error with analytic solutions. 2. **Logistic Regression**: The next lecture delves into logistic regression, emphasizing the application of gradient descent on cross-entropy error to derive effective logistic hypotheses. 3. **Linear Models for Classification**: This module expands the classification techniques, covering binary classification via logistic regression and multiclass classification through One-vs-All (OVA) and One-vs-One (OVO) decomposition methods. 4. **Nonlinear Transformation**: Participants learn to navigate nonlinear models through nonlinear feature transforms combined with linear models, weighing the balance between model complexity and performance. 5. **Hazard of Overfitting**: The course addresses the common pitfall of overfitting, helping students recognize its causes, including excessive model complexity versus the available data. 6. **Regularization**: Here, strategies to mitigate overfitting are discussed, focusing on how to minimize augmented error through effective regularization techniques. 7. **Validation**: This module teaches students about model validation methods, particularly cross-validation, to ensure robustness in model selection. 8. **Three Learning Principles**: Finally, it wraps up with a discussion on the three core principles of machine learning: model complexity, data quality, and the importance of professional expertise in the field. #### Review The **Machine Learning Foundations (Algorithmic Foundations)** course is incredibly well-structured, offering a blend of theoretical knowledge and practical application. Throughout the modules, the instructors present complex ideas in an accessible manner, making the content digestible for learners at various stages of their education. The focus on algorithmic tools provides a vital perspective for aspiring data scientists and machine learning practitioners. Each section builds upon the last, ensuring that students leave with a comprehensive understanding of how different algorithms operate and how they can be applied in real-world scenarios. The course also incorporates numerous practical exercises and examples, fostering a hands-on approach that reinforces learning. The balance between the rigorous theoretical foundations and real-world applications is one of the course's standout features. #### Recommendation I highly recommend the **Machine Learning Foundations (Algorithmic Foundations)** course to anyone eager to dive into machine learning. Whether you are a beginner looking to gain a solid introduction or an intermediate learner aiming to refine your algorithmic skills, this course caters to various skill levels. For those who aspire to work in data science, artificial intelligence, or related fields, mastering the concepts taught in this course will be invaluable. The knowledge gained here will not only assist in future studies but also equip you with the tools necessary to tackle real-world challenges in machine learning. In conclusion, enrolling in this course is a proactive step toward mastering machine learning fundamentals. With a solid foundation in both theory and practice, you will be well-prepared to advance your career in this rapidly evolving field.

Syllabus

第九講: Linear Regression

weight vector for linear hypotheses and squared error instantly calculated by analytic solution

gradient descent on cross-entropy error to get good logistic hypothesis

binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition

nonlinear model via nonlinear feature transform+linear model with price of model complexity

overfitting happens with excessive power, stochastic/deterministic noise and limited data

minimize augmented error, where the added regularizer effectively limits model complexity

(crossly) reserve validation data to simulate testing procedure for model selection

be aware of model complexity, data goodness and your professionalism