CS 445 - Machine Learning Fall 2020

CS 445 Machine Learning

Fall 2020

Last modified: December 03 2020 09:19

This page is for the 2020 Fall version of this class. For general information concerning this course, refer to the general course page.

The general learning flow for this class is:

Attend class where we will interleave lectures and labs. This is where we will introduce new concepts and methods.
Read the assigned reading AFTER class/lecture. These readings will reinforce the topics from class.
Take a short take home quiz on the material discussed in class and in the textbook.

Current Week

Week	Date	Topic	Readings (after class)	Assignment/Lab	Due
1	Aug 27	Guide What is Machine Learning? Types of Machine Learning	Chp 1 Chp 3.1-3.2	Workstation Config PA 0

2	Sep 1	Guide Data Types Decision Trees	IDD 2.1, 3.3	Decision Tree Lab Data Types and Decision Tree Quiz (in Canvas) PA1:Decision Trees	Workstation Config (mark complete or incomplete in Canvas) ML Background Survey Quiz (in Canvas)
	Sep 3	SkLearn Decision Tree Regression with Decision Trees	Scikit Learn Decision Tree Doc	Data Analysis & SkLearn Decision Trees Lab	Quiz 1 (in Canvas) PA0 (Autolab) (on Monday)

3	Sep 8	Guide Overfitting Model Evaluation Model Selection	IDD 3.4 - 3.8	Quiz 2 (in Canvas) Model Selection and Validation Lab
	Sep 10	Tuning Hyperparameters Cross Validation Nested Cross Validation		tree_warmup.py Normalization HW	Quiz 2 (in Canvas)

4	Sep 15	Guide Utilizing Numpy Distances and Normalization K-Nearest Neighbors	scipy-lectures (1.4.1 - 1.4.4) Video: An Intro to numpy IDD 2.4.1 - 2.4.4 on Distances IDD 4.3 (KNN) IDD 2.3.3 and 2.3.4	Numpy Lab Quiz 3 (in Canvas)
	Sep 17	Curse of Dimensionality Feature Selection		High Dimensionality Lab	Quiz 3 (in Canvas)

5	Sep 22	Guide KD Trees Probability with Decision Trees and KNN KNN Versus Decision Trees			PA1 (submit to Canvas/Autolab)
	Sep 24	Exam 1		Exam 1 Topics/Review

6	Sep 29	Guide Bias/Variance Bagging	IDD 4.10 - 4.10.4	Bias Variance Lab
	Oct 1	Ensemble Methods Random Forests	IDD 4.10.6	Ensemble Lab

7	Oct 6	Guide Probability Refresher	Chp 4.4	Probability Refresher Lab
	Oct 8	Naïve Bayes Conjugate Priors	Chp 4.10	Naive Bayes PA2:Naive Bayes

8	Oct 13	Guide Calculus Review Gradient Descent Linear Regression Lab		Partial Derivative HW (in Canvas)
	Oct 15	Logistic Regression Linear Regression Lab Multilayer Neural Nets

9	Oct 20	Guide L2 Regularization Multilayer Neural Nets	IDD 4.7 resources/exam2Review.php Exam 2 Topics/Review
	Oct 22	Exam 2 Review Project Work Day		Exam 2 (take home exam).	PA2: Due Friday Exam Due Monday at 5 pm

10	Oct 27	Guide TensorFlow Keras Introduction	IDD 4.8
	Oct 29	Keras		Introduction to Keras

11	Nov 3	Guide BackPropagation Automatic Differentiation		PA3:Keras and MNist
	Nov 5	Convolutional NN		CNN Lab

12	Nov 10	Guide Recurrent Neural Networks
	Nov 12	Principal Component Analysis Nonlinear dimensionality reduction	Covariance tutorial Video on PCA	PCA Notebook PCA Notebook 2

13	Nov 17	Word Embeddings	Chp 2.3.3 Appendix B.1.1
	Nov 19	Support Vector Machines (SVMs)	IDD Chp 4.9	SVM Worksheet

14	Nov 24	Thanksgiving Break
	Nov 26	Thanksgiving Break

15	Dec 1	Guide Class Imbalance Hierarchical Clustering K-Means Clustering	IDD 4.11 (CLass Imbalance Problem) IDD 7.1 - 7.3 (Clustering)	K-Means Lab
	Dec 3	Cluster Evaluation Techniques Density-based clustering Outlier Detection

16	Dec 8	Outlier Detection			K-Means Lab (submit to Canvas)
	Dec 10	Final Exam Review/Work day

17	Dec 15	Final Exam (due 12:00 pm)