機器學習技法 (Machine Learning Techniques)

National Taiwan University via Coursera

Go to Course: https://www.coursera.org/learn/machine-learning-techniques

Introduction

# Course Review: Machine Learning Techniques In the rapidly evolving landscape of data science, knowledge of machine learning has become indispensable. Coursera’s course titled **"機器學習技法 (Machine Learning Techniques)"** provides an in-depth exploration of advanced machine learning models, extending concepts introduced in its precursor course, “Machine Learning Foundations.” This course is ideal for individuals looking to deepen their understanding of machine learning beyond the basics, incorporating practical applications and theoretical insights. ## Course Overview The course builds upon foundational tools and techniques to develop robust and applicable models through three key areas: integrating numerous features, combining predictive capabilities, and extracting hidden features from data. It aims to equip learners with the skills to apply sophisticated machine learning methods to real-world problems, making it a significant asset for data professionals. ## Syllabus Breakdown The course is structured into sixteen detailed sections, each addressing a specific machine learning technique. Below is a brief overview of the key modules: 1. **Linear Support Vector Machine**: Focuses on a strong linear classification method using quadratic programming, offering robustness to data classification challenges. 2. **Dual Support Vector Machine**: Introduces a geometric understanding of support vector machines with minimal dependency on the dimensional transformations of input data. 3. **Kernel Support Vector Machine**: Explains the kernel trick, enabling learners to handle both simple and complex models efficiently, broadening the scope of analysis. 4. **Soft-Margin Support Vector Machine**: Discusses a pragmatic approach to margins, allowing for slight violations to improve overall model robustness. 5. **Kernel Logistic Regression**: Highlights an SVM-like model for soft classification, enriching the learning experience through multilevel strategies. 6. **Support Vector Regression**: An insightful module on applying kernel ridge regression, emphasizing error measurement and optimization techniques. 7. **Blending and Bagging**: Explores methods for improving predictive performance by combining diverse hypotheses, a critical skill for enhancing model accuracy. 8. **Adaptive Boosting**: Discusses optimal weighting strategies for diverse hypotheses, enabling participants to effectively bolster weaker models. 9. **Decision Tree**: Examines the fundamentals of recursive branching, aiding participants in creating interpretable models for aggregation. 10. **Random Forest**: This section covers the technique of bootstrap aggregating, enhancing model reliability through randomized decision trees. 11. **Gradient Boosted Decision Tree**: Teaches aggregation of trees through functional optimization, a cornerstone method in competitive machine learning environments. 12. **Neural Network**: Explores automatic feature extraction, employing advanced techniques like back-propagation for training deep learning models. 13. **Deep Learning**: Introduces an elementary deep learning model emphasizing pre-training and fine-tuning techniques, suitable for modern applications. 14. **Radial Basis Function Network**: Discusses distance-based similarity aggregation, promoting the clustering of data for improved model performance. 15. **Matrix Factorization**: Analyzes collaborative filtering methods vital for recommender systems, framed within the context of user-item relationships. 16. **Finale**: Wraps up the course, summarizing key takeaways on feature exploitation, error optimization, and combating overfitting, ensuring a practical mindset for learners. ## Pros and Cons ### Pros: - **Comprehensive Coverage**: The course tackles a wide array of techniques, ensuring learners have varied tools at their disposal. - **Practical Focus**: Emphasizes real-world applications, which is vital for professionals in data science roles. - **Well-Structured Content**: Each module builds upon knowledge incrementally, enhancing understanding and retention. - **Expert Instructors**: Courses on Coursera are usually led by industry experts or professors, adding credibility to the content. ### Cons: - **Prerequisite Knowledge Required**: The course is not designed for absolute beginners, and some familiarity with machine learning concepts is recommended. - **Intense Content Load**: With sixteen comprehensive topics, the course may feel overwhelming without adequate preparation. ## Recommendation I highly recommend the **"Machine Learning Techniques"** course on Coursera for anyone looking to advance their machine learning skills. Whether you are a working professional hoping to upskill or a student aiming to deepen your knowledge, this course provides valuable insights and practical tools that are essential in today’s data-driven world. By the end of the course, participants will not only be proficient in applying a wide array of machine learning techniques but will also understand how to synthesize these techniques for practical use cases. This course is undoubtedly a worthwhile investment for anyone serious about a career in data science or artificial intelligence.

Syllabus

第一講：Linear Support Vector Machine

more robust linear classification solvable with quadratic programming

第二講：Dual Support Vector Machine

another QP form of SVM with valuable geometric messages and almost no dependence on the dimension of transformation

第三講：Kernel Support Vector Machine

kernel as a shortcut to (transform + inner product): allowing a spectrum of models ranging from simple linear ones to infinite dimensional ones with margin control

第四講：Soft-Margin Support Vector Machine

a new primal formulation that allows some penalized margin violations, which is equivalent to a dual formulation with upper-bounded variables

第五講：Kernel Logistic Regression

soft-classification by an SVM-like sparse model using two-level learning, or by a "kernelized" logistic regression model using representer theorem

第六講：Support Vector Regression

kernel ridge regression via ridge regression + representer theorem, or support vector regression via regularized tube error + Lagrange dual

第七講：Blending and Bagging

blending known diverse hypotheses uniformly, linearly, or even non-linearly; obtaining diverse hypotheses from bootstrapped data

第八講：Adaptive Boosting

"optimal" re-weighting for diverse hypotheses and adaptive linear aggregation to boost weak algorithms

第九講：Decision Tree

recursive branching (purification) for conditional aggregation of simple hypotheses

第十講：Random Forest

bootstrap aggregation of randomized decision trees with automatic validation

第十一講：Gradient Boosted Decision Tree

aggregating trees from functional + steepest gradient descent subject to any error measure

第十二講：Neural Network

automatic feature extraction from layers of neurons with the back-propagation technique for stochastic gradient descent

第十三講：Deep Learning

an early and simple deep learning model that pre-trains with denoising autoencoder and fine-tunes with back-propagation

第十四講：Radial Basis Function Network

linear aggregation of distance-based similarities to prototypes found by clustering

第十五講：Matrix Factorization

linear models of items on extracted user features (or vice versa) jointly optimized with stochastic gradient descent for recommender systems

第十六講：Finale

summary from the angles of feature exploitation, error optimization, and overfitting elimination towards practical use cases of machine learning