Introduction to Machine Learning

10-315, Spring 2022

Carnegie Mellon University

Aarti Singh, Henry Chai

Home Teaching Staff Lecture Schedule Recitations Homeworks

Lecture:
Day and Time: Monday and Wednesday, 11:50am - 1:10pm
Location: HOA 160, Remote (connect via Zoom link on Canvas)
Recitation: Day and Time: Friday, 11:50am - 1:10pm
Location: HOA 160, Remote (connect via Zoom link on Canvas)
Office Hours:
Day Time Location Staff
Mondays 1:30 PM - 2:30 PM WEH 4625; Remote via Zoom while in Modified Posture (link on Piazza) Roochi
Tuesdays 12:00 PM - 1:00 PM WEH 5302; Remote via Zoom while in Modified Posture (link on Piazza) Owen
6:00 PM - 7:00 PM Remote via Zoom (link on Piazza) Tarun
Wednesdays 10:00 AM - 11:00 AM WEH 6423; Remote via Zoom while in Modified Posture (link on Piazza) Julia
Thursdays 3:00 PM - 4:00 PM Remote via Zoom (link on Piazza) Devanshi
4:00 PM - 5:00 PM GHC 8118; Remote via Zoom while in Modified Posture (link on Piazza) Henry
Fridays 10:00 AM - 11:00 AM Remote via Zoom (link on Piazza) Aarti
6:00 PM - 7:00 PM Remote via Zoom (link on Piazza) Sedrick

In-person attendance: Stopping the spread of all contagious illnesses: Please do not come to class if you feel sick or have symptoms of a potentially contagious illness. Following CMU policy, students that have symptoms of COVID-19 should contact University Health Services (UHS) at 412-268-2157. Even if you know you do not have COVID-19 but have symptoms that may be a sign of other contagious illnesses, such as a cold or flu, please do not come to class. In the past, some students have thought that coming to class even though they were sick would make them seem like a hard worker. However, this culture has changed. The pandemic has underscored that being around other people when you are sick risks spreading the illness to them, and staying home stops transmission throughout the community. Staying home when you are sick shows that you are responsible and that you care about your classmates and the campus community.

Video recordings: All lectures and recitations will be recorded, and the recordings will be available at the Panopto link on Canvas ONLY for the use of students in this course. Please note that you are not allowed to share the recording anywhere to protect the FERPA rights of all students in the classroom. Breakout rooms and Office hours will NOT be recorded.


Course Description:
Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the core concepts, theory, algorithms and applications of machine learning. We cover supervised learning topics such as classification (Naive Bayes, Logistic regression, Support Vector Machines, neural networks, k-NN, decision trees, boosting) and regression (linear, nonlinear, kernel, nonparametric) as well as unsupervised learning (density estimation, MLE, MAP, clustering, PCA, dimensionality reduction).
Prerequisites: Principles of Imperative Computation (15-122) and Math Foundations of Computer Science (21-127 or 21-128 or 15-151) and Probability (21-325 or 36-217 or 36-218 or 36-225 or 15-359)

Additionally, linear algebra and calculus are central pieces to this machine learning course. Given the lack of a linear algebra and calculus prequisite, we will provide the necessary resources and instruction. That being said, if you have never been exposed to matricies, vectors, differentiation and integration in any context, please contact the instructor to discuss how to best meet your linear algebra and calculus needs.
Learning Outcomes: After completing the course, students will be able to:
  • Select and apply an appropriate supervised learning algorithm for classification problems (e.g., naive Bayes, support vector machine, logistic regression, neural networks).
  • Select and apply an appropriate supervised learning algorithm for regression problems (e.g. linear regression, ridge regression, nonparametric kernel regression).
  • Recognize different types of unsupervised learning problems, and select and apply appropriate algorithms (e.g., clustering, linear and nonlinear dimensionality reduction).
  • Work with probabilities (Bayes rule, conditioning, expectations, independence), linear algebra (vector and matrix operations, eigenvectors, SVD), and calculus (gradients, Jacobians) to derive machine learning methods such as linear regression, naive Bayes, and principal components analysis.
  • Understand machine learning principles such as model selection, overfitting, and underfitting, and techniques such as cross-validation and regularization.
  • Implement machine learning algorithms such as logistic regression via stochastic gradient descent, linear regression, or k-means clustering.
  • Run appropriate supervised and unsupervised learning algorithms on real and synthetic data sets and interpret the results.
Recommended Textbooks:
  • Pattern Recognition and Machine Learning, Christopher Bishop (available online)
  • Machine Learning: A probabilistic perspective, Kevin Murphy (available online)
  • Machine Learning, Tom Mitchell.
  • The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Grading:
  • 4 Homeworks (48%)
  • 4 QnAs (20%)
  • Midterm and final exam (15+15=30%)
  • Participation (2%)
Grades will be collected on Canvas.

Homeworks: Homeworks will be released here and student will turn it in via Gradescope.
Late Days:
  • There are a total of 6 late days across all homeworks (including QnAs), no more than 1 late day for a QnA, no more than 2 late days for a HW.
  • HWs submitted after all late days are exhausted will be awarded 0 points. There is no partial credit after the late days.
Communication: All class discussions (outside of lectures, recitations and office hours), announcements and other communication will take place via Piazza.
Policies:
Collaboration
  • You may discuss the questions.
  • Each student writes their own answers.
  • Each student must write their own code for the programming part.
  • Please don't search for answers on the web, Google, previous years' homeworks, etc.
    • Please ask us if you are not sure if you can use a particular reference.
    • List resources used (references, discussants) on top of submitted homework.
Audits and Pass/Fail Audits NOT allowed. Pass/Fail allowed.
Academic Integrity Any violations of academic integrity will always be reported to the university authorities (your Department Head, Associate Dean, Dean of Student Affairs, etc.) as an official Academic Integrity Violation, in compliance with CMU's Policy on Academic Integrity, and will carry severe penalties.
Support: Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful. If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/ . Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
CaPS: 412-268-2922
Re:solve Crisis Network: 888-796-8226
If the situation is life threatening, call the police:
On campus: CMU Police: 412-268-2323
Off campus: 911.

If you have questions about this or your coursework, please let the instructors know.