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EVENT DETAILS |
This 20-hour course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions.
Prerequisites
Knowledge of Python programming
Able to munge, analyze, and visualize data in Python
Syllabus
Unit 1: Introduction and Regression
What is Machine Learning
Simple Linear Regression
Multiple Linear Regression
Numpy/Scikit-Learn Lab
Unit 2: Classification I
Logistic Regression
Discriminant Analysis
Naive Bayes
Supervised Learning Lab
Unit 3: Resampling and Model Selection
Cross-Validation
Bootstrap
Feature Selection
Model Selection and Regularization lab
Unit 4: Classification II
Support Vector Machines
Decision Trees
Bagging and Random Forests
Decision Tree and SVM Lab
Unit 5: Unsupervised Learning
Principal Component Analysis
Kmeans and Hierarchical Clustering
PCA and Clustering Lab
Final Project
After 20 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.
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