CS 335: Machine Learning
Logistics
Lectures: Tues, Thurs 11:30am12:45pm
Fourth Hour: Fri 8:30am9:20am
Room: Clapp Laboratory 206
Office hours: Tues 13pm, Thurs 9:1511:15am, Clapp 200
Piazza: https://www.piazza.com/mtholyoke/spring2020/cs335/home
Gradescope: https://www.gradescope.com/courses/76996
Moodle: https://moodle.mtholyoke.edu/course/view.php?id=17913
Learning Goals
The goals of this course are to:More concretely, by the end of the semester, the students will be expected to have mastered fundamental concepts in machine learning including:
Students will be expected to apply machine learning to a project and in the process:
Grading
The project grade (30%) is further broken down to:
Class engagement grades are a composite of nongraded assignments including but not limited to: (a) class discussion, (b) inclass worksheets, (c) posting on Piazza, (d) optional work (like HW0) and fourthhour work.
Homework deadlines are strict. For homework that is late, you will be penalized 33% of the assignment’s value for each day or fraction thereof that it is late (0–24 hours = 33% penalty; 24–48 hours = 66% penalty; 48+ hours = no credit). An assignment is considered late until all components (written and code) are submitted.
There will be two "celebrations of learning." These will be inclass, closedbook exams.
Course schedule
week  date  day  topic  homework  project 

Week 1 
Jan 21  Tu  Introduction and Logistics 
Homework 0
Due: Fri Jan 31 11:59pm  
Jan 23  Th  Linear Regression  
4th hour  Fri  Calculus review  
Week 2 
Jan 28  Tu  Gradient descent [code]  
Jan 30  Th  Linear algebra for ML [code]  
4th hour  Fri  Python review [soln]  
Week 3 
Feb 4  Tu  Multivariate linear regression  
Feb 6  Th  Normal equations and vectorized gradient descent [code]  
4th hour  Fri  
Week 4 
Feb 11  Tu  Logistic Regression  
Feb 13  Th  Evaluating Models  
4th hour  Fri  Probability and logarithms review  
Week 5 
Feb 18  Tu 
Overfitting & Regularization
[code]
[End Unit 1] 

Feb 20  Th  MultiClass Classification  
4th hour  Fri  Review Unit 1  
Week 6 
Feb 25  Tu  Project descriptions and Ethics in ML 
Idea Proposal
Due: Mar 6, 11:59pm 

Feb 27  Th  Celebration of Learning 1 (in class)  
4th hour  Fri  Project brainstorm/discussion 
Homework 4
Due: Tues Mar 10 11:59pm 

Week 7 
Mar 3  Tu  KNN and decision trees  
Mar 5  Th  
4th hour  Fri  Project brainstorm/discussion  
Week 8 
Mar 10  Tu  Kernel Trick 
Paper Selection
Due: Mar 13 11:59pm 

Mar 12  Th  
4th hour  Fri  Literature review practice 
Literature Review
Due: Mar 27 11:59pm 

Week 9 
Midsemester break: no class  
Week 10 
Mar 24  Tu  Neural nets + Backprop  
Mar 26  Th  
4th hour  Fri 
Project Plan
Due: Apr 10 11:59pm 

Week 11 
Mar 31  Tu  PCA  
Apr 2  Th  
4th hour  Fri  
Week 12 
Apr 7  Tu  Community Day (no classes)  
Apr 9  Th  Bayesian classification  
4th hour  Fri  Clustering  
Week 13 
Apr 14  Tu 
Project Final Report
Due: Apr 28 11:59pm 

Apr 16  Th  [End Unit 2]  
4th hour  Fri  Review  
Week 14 
Apr 21  Tu  Project Work Day  
Apr 23  Th  Celebration of Learning 2 (in class)  
4th hour  Fri  Project Work Day  
Week 15 
Apr 28  Tu  Project Final Presentations 
Project
Projects will focus on "impactful" applications of machine learning focused on three areas: (1) health, (2) education, or (3) climate change. Project details will be posted later in the course.Resources
This course has no official textbook. However, some of lecture material will be drawn from the following resources and they could be helpful in your learning:Python
Programming assignments will use Python, NumPy, and SciPy. The required Python environment is Anaconda 2019.10 distribution of Python 3.7. If you are not working with this environment, your work will not be graded and you will not receive help debugging.See this page on getting started with Python for CS 335.
Resources for Python: