Machine Learning
Spring 2024


Key Information


Course Description - Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. Prerequisites: This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms. No formal prerequisites but MATH 165, MATH 164, and CSC 242 strongly recommended.


Credit Hours - This course follows the College credit hour policy for four-credit courses, including lectures and study sessions. Students are expected to do significant work outside of class time. You may find it necessary to spend several hours most weeks reading the textbook, studying derivations, and working on assignments. You should plan to spend approximately eight hours each week working on material for this course.


Prerequisites - MATH 164, MATH 165, CSC 242 strongly recommended.


Class - Tuesdays and Thursdays 1105-1220 in Gavett 202.



Teaching Staff


Course Mechanics

Class time will primarily be lecture format. Questions are highly encouraged. We will explore various models of machine learning (and proofs of correctness) using a combination of chalkboard work and slides.


You will be expected to do significant reading and practice outside of class. Graded work consists of three exams (two midterms and a final) as well as several programming and problem solving homework assignments.


The majority of your work will be individual; however, there will be a term project which may be completed either solo or as a small team. You will be expected to identify your own application domain, obtain or collect your own dataset, and apply one or more models from the course. Your grade on the project will be based on significance, methodology, and a written report. Teams will be further asked to communicate their results to the rest of the class.


Textbook and Materials

Schedule


Class NumberDateDayTopicsReading
1Jan 18ThuMachine Learning"PRML 1.1 1.2 1.4 new Bishop Ch 2"
2Jan 23TueBayesian Linear Regression"PRML 3.1-3.4 new Bishop Ch 4"
3Jan 25ThuLinear and Log-linear Classifiers"PRML 4.3 new Bishop Ch 5"
4Jan 30TueMultilayer Perceptrons; backprop. "PRML 5.1-5.3 new Bishop Ch 4 5 7"
5Feb 01ThuMax-margin Classifiers"PRML 3.1 4.1 7.1; M4ML Ch3 "
6Feb 06TueDuality and the Kernel Trick"PRML Apx E; M4ML Ch 12 CO Ch 4 Ch 5"
7Feb 08ThuSlack VariablesSame as above.
8Feb 13TueMidterm Review
9Feb 15ThuMidterm 1
10Feb 20TueConvolutional NetworksKrizhevsky 2012 + new Bishop Ch 10
11Feb 22ThuRecurrent NetworksPapers TBD
12Feb 27TueTransformersPapers + new Bishop Ch 12
13Feb 29ThuBayesian Networks"PRML 8.1 8.2 new Bishop Ch 11"
14Mar 05TueMarkov Random Fields"PRML 8.3 new Bishop Ch 11"
15Mar 07ThuExact InferencePRML 8.4
*Mar 12TueSpring Break
*Mar 14ThuSpring Break
16Mar 19TueMidterm 2 review
17Mar 21ThuMidterm 2
18Mar 26TueSampling Algorithms"PRML 11.1 new Bishop Ch 11"
19Mar 28ThuMarkov Chains"PRML 11.2 new bishop"
20Apr 02Tue"Metropolis-Hastings Gibbs Sampling""PRML 11.2 11.3 new bishop"
21Apr 04ThuHidden Markov Models"PRML 13.1 13.2"
22Apr 09TueExpectation MaximizationPRML 9.4
23Apr 11ThuMixture Models and Clustering"PRML 9.1 9.2"
24Apr 16TueEM for HMMsPRML 13.2.1
25Apr 18ThuReinforcement Learning"RL book Ch 1 3 4 6"
26Apr 23TueDeep Reinforcement LearningPapers TBD
27Apr 25ThuEthicsPapers TBD
28Apr 30TueFinal Review
May 7TueFinal Exam (715pm-1015pm)

Grading


Your Overall Numeric Grade

Your scores on the individual components of this course will weighted to obtain your course score. All appeals of grades on individual scores must be made within one week of the grade being available. The following table represents this weighting of components.


CategoryWeight
Project 10%
Homeworks 40%
Midterm 1 15%
Midterm 2 15%
Final Exam 20%
Total 100%


Letter Grades

Letter grades will follow the Official University of Rochester Grading Scheme. Note that the University scheme puts “average” somewhere between C and B.


A: Excellent >=94%
A-: >=90%
B+: >=87%
B: Above Average >=83%
B-: >=80%
C+: >=77%
C: >=73%
C-: Minimum satisfactory grade >=70%
D: UG Minimum passing grade, Grads convert to E >=60%
E: <60%

Policies


Policies

Inclusivity

It is my hope that all students, TAs, and workshop leaders in all my courses feel welcome, respected, and supported. If you have any concerns please feel free to contact me directly.

Academic Honesty

All assignments and activities associated with this course must be performed in accordance with the University of Rochester’s Academic Honesty Policy. More information is available at: www.rochester.edu/college/honesty

Many of the assignments will require you to implement basic algorithms of machine learning. You can likely find approximate solutions online to many of the algorithms in a variety of languages. In general, you should avoid using source code obtained online for any assignments, unless a specific writeup indicates otherwise. Similarly, you should consider your work on the regular assignments to be individual - you may collaborate at the level of ideas, but sharing of code (either digitally or by manually retyping) is prohibited. Please see the separate note on AI.

All incidents of academic dishonesty will be reported. This is because academic dishonesty is harmful to the entire university community, AND because it is the explicit requirement of the University of Rochester Academic Honesty Policy.

Artificial Intelligence

Use of AI is acceptable in this course, within limits. Part of the course is about understanding the abilities (and limits) of AI, so for that reason you are encouraged to explore it's careful use on portions of the work which are lesss critical to your understanding of the material (data cleaning and boilerplate); however, students are expected to complete the numerical programming portion of the course on their own, and such kinds of problems may be present on the exams. The goal of university education is for you, personally, to acquire knowledge and skills -- don't let AI be a stunt double for your actual life. Similarly, students may use AI to improve the quality of their writing; however, all conclusions and analysis must be their own, and students are responsible for the factual and technical accuracy of their work. Submission of hallucinated results or falsified methodology is strictly forbidden and will result in a zero on any given assignment. If you use AI, please disclose how you used it, and how well you feel it worked for the task.

Disability Resources

The University of Rochester respects and welcomes students of all backgrounds and abilities. In the event you encounter any barrier(s) to full participation in this course due to the impact of disability, please contact the Office of Disability Resources. The access coordinators in the Office of Disability Resources can meet with you to discuss the barriers you are experiencing and explain the eligibility process for establishing academic accommodations. You can reach the Office of Disability Resources at: disability@rochester.edu; (585) 276-5075; Taylor Hall.

Students with an accommodation for any aspect of the course must make arrangements in advance through the Disability Resources office. Then, as instructed by the office, contact the instructor to confirm your arrangements.

Late work

No late work will be accepted without prior approval from the instructor. If you are unable to complete your work on time, please submit what you have before the deadline in order to be considered for partial credit. If there are serious conflicts (e.g., varsity travel, serious illness) please contact the instructor well in advance.

Policy Revisions

The instructor hopes that these policies are stable and fixed; however, they may be modified during the semester according to changing circumstances. Any modifications will be communicated clearly to students and updated on this page.