rasbt/stat479-machine-learning-fs18 排名第 18,161，此处有 8 个同类条目；JifuZhao/120-DS-Interview-Questions 排名第 18,149。

STAT479: Machine Learning (Fall 2018) Instructor: Sebastian Raschka Lecture material for the Machine Learning course (STAT 479) at University Wisconsin-Madison. For details, please see the course website at **Part I: Introduction** Lecture 1: What is Machine Learning? Lecture 2: Intro to Supervised Learning: KNN **Part II: Computational Foundations** Lecture 3: Using Python, Anaconda, IPython, Jupyter Notebooks Lecture 4: Scientific Computing with NumPy, SciPy, and Matplotlib Lecture 5: Data Preprocessing and Machine Learning with Scikit-Learn **Part III: Tree-Based

Methods** Lecture 6: Decision Trees Lecture 7: Ensemble Methods **Part IV: Evaluation** Lecture 8: Model Evaluation 1: Introduction to Overfitting and Underfitting Lecture 9: Model Evaluation 2: Uncertainty Estimates and Resampling Lecture 10: Model Evaluation 3: Model Selection and Cross-Validation Lecture 11: Model Evaluation 4: Algorithm Selection and Statistical Tests Lecture 12: Model Evaluation 5: Performance Metrics **Part V: Dimensionality Reduction** Lecture 13: Feature Selection Lecture 14: Feature Extraction **Due to time constraints, the following topics could unfortunately not

be covered:** **Part VI: Bayesian Learning** Bayes Classifiers Text Data & Sentiment Analysis Naive Bayes Classification **Part VII: Regression and Unsupervised Learning** Regression Analysis Clustering **The following topics will be covered at the beginning of the Deep Learning class next Spring. **Part VIII: Introduction to Artificial Neural Networks** Perceptron Adaline & Logistic Regression SVM Multilayer Perceptron This work is licensed under a Creative Commons Attribution-NonCommercial 4. Teaching this class was a pleasure, and I am especially happy about how awesome the

class projects turned out. Listed below are the winners of the three award categories as determined by ~210 votes.