MECH-8290-43

Introduction to Machine Learning using Python

Course Description 

Broad introduction to machine learning, datamining, and statistical pattern recognition. The intention of the course is to familiarize students with different topics in the field, explain concepts, and implement the learning in numerous case-studies. This course will try to cover (i) Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction)

Class and lab information 

Resources  

Course Schedule 

Week

01

02

03

04

05

06

07

08

09

10

11

12

Topics

L 01: Course Introduction + Linear Regression 

L 02: Probability + MLE + MAP + Gradient Descent + Cross Validation

L03: Linear Classification + Logistic Regression 

L04: Non-parametric (Nearest Neighbor) + Multi-class Classification (KNN)

L 05: Probabilistic Classifiers (GDA + Naive Bayes)

L 06: Neural Networks (MLP)

L 07: Clustering (k-means) + Mixture of Gaussians (GMM, EM)

L 08: Principal Component Analysis (PCA) & Autoencoders

L 09: Support Vector Machines (SVM)

L 10: Ensemble Methods

L 11: Project Presentation

L 12: Final Exam Review

Readings

1.0, 1.1, 1.2, 3.1

2.0, 2.1, 2.3, 4.2.4, 1.3

pp. 179-195, 203-207

2.5, pp. 179-184, 4.1.2, 4.3.4

4.2.2, pp. 380-381

5.1-5.3

9.1, 9.2, 9.3, 2.3.9

12.1, 4.1

7.1, 4.1.1, 4.1.2, 6.1, 6.2, pp. 325-337

14.2-14.3

N/A

N/A