## Machine Learning Course

Rui Xia

School of Computer Science & Engineering

Nanjing University of Science & Technology

**Course Information** (including contacts, syllabus, assessment, references, etc.)

#### Contents

- An Introduction to Machine Learning [slides]
- Linear Regression [slides]
- Logistic Regression & Softmax Regression [slides]
- Perceptron Algorithm [slides]
- Review of Linear Models [slides]
- Multi-layer Forward Neutral Network & Back Propagation [slides]
- Concept Revisit (Generative vs. Discriminative, Overfitting and Regularization) [slides]
- Naive Bayes Model [slides]
- Support Vector Machines [slides]

#### Projects

- Implement linear regression (Analytic, GD) for Nanjing housing price prediction [page 12 of slides]
- Implement logistic regression (GD, SGD, Newton) and softmax regression for admit/non-admit binary classification [page 15 of slides]
- Implement softmax regression (GD, SGD) for the same problem in Practice 2 and compare it with logistic regression [page 23 of slides]
- Implement Perceptron and multi-class Perceptron for the same problem in Practice 2, and compare them with logistic regression and softmax regression (SGD) respectively [page 10 of slides]
- Implement 3-layer Forward Neutral Network for the same problem in Practice 2 with 5-fold cross validation, based on 1) self-coding, and 2) Tensorflow resepcitively, and compare them [page 15 of slides]
- Implement naïve Bayes algorithm with Multinomial event model and Multi-variate Bernoulli model, and run the algorithm based on the training & testing data given in [page 18 of slides]

Last updated by Rui Xia on 2019-1-9