This course provides a basic introduction to the machine learning pipeline and related concepts. Topics covered include: Machine learning uses and applications; data set requirements; data pre-processing; data annotation, and validation; data representation formats; features and feature representation and extraction; the vector space model; traditional machine learning algorithms; machine learning algorithms and programming; ML evaluation methods; introduction to deep learning algorithms; big data; reinforcement learning; Unsupervised learning; statistical significance analysis; and other special topics.
Monday and Wednesday12-2 pm on Zoom. Zoom meeting ID and code on Brightspace
Data Mining: Practical Machine Learning Tools and Techniques (latest edition). Authors: Witten, Frank
Ricardo A. Calix, Ph.D.
Purdue University Northwest
rcalix@pnw.edu
241 Anderson
Thursday 2-4 pm (or by appointment) on Zoom. Zoom meeting ID and code on Brightspace
Brightspace
Environment:
AWS
WL Scholar
Labs
We will use the following software:
Sun | Mon | Tue | Wed | Thu | Fri | Sat |
---|---|---|---|---|---|---|
Jan 10 |
Jan 11 Introduction |
Jan 12 |
Jan 13 Feature Engineering problems |
Jan 14 |
Jan 15 | Jan 16 |
Jan 17 | Jan 18 | Jan 19 |
Jan 20 The vector space model and KNN |
Jan 21
Lab: |
Jan 22 | Jan 23 |
Jan 24 |
Jan 25 Naive Bayes |
Jan 26 |
Jan 27 |
Jan 28 |
Jan 29 | Jan 30 |
Jan 31 |
Feb 1 Performance Metrics |
Feb 2 |
Feb 3 Lab: Weka |
Feb 4 |
Feb 5 | Feb 6 |
Feb 7 |
Feb 8 Feature Pre-Processing |
Feb 9 |
Feb 10 Chi-Square |
Feb 11 |
Feb 12 | Feb 13 |
Feb 14 |
Feb 15 Dimensionality Reduction |
Feb 16 |
Feb 17 Singular Value Decomposition |
Feb 18 |
Feb 19 | Feb 20 |
Feb 21 |
Feb 22 Recommender Systems |
Feb 23 |
Feb 24 | Feb 25 |
Feb 26 | Feb 27 |
Feb 28 |
Mar 1 Reinforcement Learning |
Mar 2 |
Mar 3 Taxi Cab |
Mar 4 |
Mar 5 | Mar 6 |
Mar 7 |
Mar 8 Optimization |
Mar 9 |
Mar 10 | Mar 11 |
Mar 12 | Mar 13 |
Mar 14 | Mar 15 | Mar 16 | Mar 17 | Mar 18 | Mar 19 | Mar 20 |
Mar 21 | Mar 22 Backpropagation |
Mar 23 | Mar 24 | Mar 25 |
Mar 26 | Mar 27 |
Mar 28 |
Mar 29 Feedforward Algorithms |
Mar 30 |
Mar 31 Neural Networks |
Apr 1 |
Apr 2 | Apr 3 |
Apr 4 |
Apr 5 Keras |
Apr 6 |
Apr 7 DNN |
Apr 8 |
Apr 9 | Apr 10 |
Apr 11 |
Apr 12 Sequence Mining |
Apr 13 |
Apr 14 Hidden Markov Models |
Apr 15 |
Apr 16 | Apr 17 |
Apr 18 |
Apr 19 K-Means |
Apr 20 |
Apr 21 GMMs |
Apr 22 |
Apr 23 | Apr 24 |
Apr 25 | Apr 26 Project Presentations |
Apr 27 | Apr 28 Project Presentations |
Apr 29 | Apr 30 | May 1 |
May 2 | May 3 Finals |
May 4 Finals |
May 5 Finals |
May 6 Finals |
May 7 Finals |
May 8 |