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.

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

		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)
		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)

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

		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)
		Curse of Dimensionality Feature Selection		High Dimensionality Lab	Quiz 3 (in Canvas)

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

		Guide Bias/Variance Bagging	IDD 4.10 - 4.10.4	Bias Variance Lab
		Ensemble Methods Random Forests	IDD 4.10.6	Ensemble Lab

		Guide Probability Refresher	Chp 4.4	Probability Refresher Lab
		Naïve Bayes Conjugate Priors	Chp 4.10	Naive Bayes PA2:Naive Bayes

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

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

		Guide TensorFlow Keras Introduction	IDD 4.8
		Keras		Introduction to Keras

		Guide BackPropagation Automatic Differentiation		PA3:Keras and MNist
		Convolutional NN		CNN Lab

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

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

		Thanksgiving Break
		Thanksgiving Break

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

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

		Final Exam (due 12:00 pm)