Teaching Assistants

Name	Office Hours See Piazza for Updates	Zoom
Sravya Burugu	See Piazza	Zoom
Shruti Kunapuli	See Piazza	Zoom
Tianhao Qu	See Piazza	Zoom
Pulkit Saharan	See Piazza	Zoom
Anurag Sarkar	See Piazza	Zoom
Arif Waghbakriwala	See Piazza	Zoom

Course Information

Course Description

Provides an introduction to machine learning. Introduces core machine learning principles and methods, providing a foundation for learning more advanced techniques. Covers traditional supervised learning models for both classification and regression, and corresponding optimization methods. Discusses issues of data use and broader ethical considerations inherent to machine learning. Lectures provide in-depth presentation of such content, to be reinforced with homeworks and/or projects. 4.000 Credit hours

Homework

There will be one assignment about every two weeks. Programming exercises will be done in Python. The detailed dates are listed on the schedule below. Homeworks should be completed largely on your own. Informal discussions or seeking general help from fellow students is ok so long as you cite your sources. Do not simply copy another student's submission! In addition, as part of your homework, there may be occaisional take-home quizzes to verify your understanding of the reading material.

Homework Late Policy:

Up to 24 hours late: 5% penalty
Up to 48 hours late: 10% penalty
After 48 hours: Not accepted.

Class Project

There will be a comprehensive group project that you will present at the end of the semester involving a non-Kaggle integrated dataset of your own choosing. The project will involve exploratory data analysis and visualization and the design and development of an appropriate machine-learning model for classification and prediction.

Academic Misconduct

Programming is a creative process. Individuals must reach their own understanding of problems and discover paths to their solutions. During this time, discussions with friends and colleagues are absolutely encouraged—you will do much better in the course, and at Northeastern, if you find people with whom you regularly discuss problems. But those discussions should take place verbally. If you simply copy code, you're breaking the rules. Each program/application must be entirely your own work. The university's academic integrity policy discusses actions regarded as violations and consequences for students: http://www.northeastern.edu/osccr/academic-integrity

Evaluation

The final grade for this course will be weighted as follows:

Homework: 80%
Group Project: 20%

Final grades will be assigned based on the following scale. Any curving or changes will be to the benefit of students. Final grades will be rounded to the nearest integer. (e.g., 94.4999 is a 94 whereas 94.5000 is a 95.)

Letter	Range
A	95 - 100
A-	90 - 94
B+	87 - 89
B	83 - 86
B-	80 - 82
C+	77 - 79
C	73 - 76
C-	70 - 72
D+	67 - 69
D	63 - 66
D-	60 - 62
F	<60

Schedule

Note: This schedule is subject to change and will be adjusted as needed throughout the semester.

Week	Date	Topic	Reading	HW Due
1	Jan 10/13	Introduction and Core Concepts About the course What is ML? Python Software Environment History, Applications, Examples Lab01: Numpy Arrays	Grok 1, ISL 1,2
2	Jan 17/20	Concepts Accuracy-Variance Tradeoff Lab02: Visualization Lab03: Pandas Dataframe / Array Processing	Grok 2, ISL 3
3	Jan 24/27	Gradient Descent Calculus Review The Gradient Descent Algorithm Batch and Stochastic Gradient Descent Lasso and Ridge Regularization Lab04: Linear Regression Lab05: Gradients Lab06: Polynomial Regression	Grok 3,4	HW1:LinReg Due 1/27
4	Jan 31/Feb 3	Binary Classifiers Perceptrons Model Evaluation Lab07: Perceptrons Lab08: Confusion Matrices	Grok 5,7
5	Feb 7/10	Probabilistic Classifier Probability Review Bayes Theorem Naive Bayes Algorithm "Spam, spam, spam, spam!"	Grok 8
6	Feb 14/17	Nearest Neighbor Perspecuity Distance metrics Hyperparameter tuning Cross validation Lab09: Vectorizing word lists Lab10: Cosine Similarity	Grok 13	HW2:Perceptron Due 2/15
7	Feb 21/24	Decision Trees	Grok 9
8	Feb 28/Mar 3	Ensemble Methods Bagging AdaBoost Gradient boosting XGBoost	Grok 12	HW3:Naive+KNN Due: 3/2 (Late Due Date: 3/12)
9	Mar 7/10	Spring Break - No Class
10	Mar 14/17	Deep Learning	Grok 10, ISL 10	PROJECT PROPOSAL Due 3/17
11	Mar 21/24	Clustering	ISL 12	HW4:Trees Due: 3/22
12	Mar 28/31	Genetic Algorithms	TBA
13	Apr 4/7	Ethics of Machine Learning	TBA	HW5:NeuralNet Due: 4/5
14	Apr 11/14	Project Science Fair		PROJECT DUE Apr 10 @ 11:59pm
15	Apr 18	Wrap up

Inclusive Class

Northeastern University values the diversity of our students, staff, and faculty; recognizing the important contribution each makes to our unique community.

Respect is demanded at all times throughout this course. In the classroom, not only is participation required, it is expected that everyone is treated with dignity and respect. We realize everyone comes from a different background with different experiences and abilities. Our knowledge will always be used to better everyone in the class.

We strive to create a learning environment that is welcoming to students of all backgrounds. If you feel unwelcome for any reason, please let us know so we can work to make things better. You can let us know by talking to anyone on the teaching staff. If you feel uncomfortable talking to members of the teaching staff, please consider reaching out to your academic advisor.

Northeastern is committed to providing equal access and support to all qualified students through the provision of reasonable accommodations so that each student may fully participate in the learning experience. If you have a disability that requires accommodations, please contact the Disability Resource Center http://www.northeastern.edu/drc/, DRC@northeastern.edu, 617-353-2675. Accommodations cannot be made retroactively and to receive an accommodation, a letter from the DRC or LDP is required.

E-mail	j.rachlin@northeastern.edu
Web	https://www.khoury.northeastern.edu/people/john-rachlin/
Office Hours	Wed/Thu 3-5pm and by appointment only. Zoom: https://northeastern.zoom.us/my/rachlin Click here to reserve 20m for one-on-one help. Email me to arrange other times.

Machine Learning I

Time: Tue/Fri 3:25-5:05 ET/Boston

Location: Online (See Canvas for connection info.)

John Rachlin

Associate Teaching Professor, Northeastern University