CSC 246: Machine Learning
Fall 2025


Staff




Course 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.


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 - Required: Full Calculus sequence and MATH 165. Recommended: CSC 242 and STAT 190.


Class - Tuesdays and Thursdays 1105-1220. (See UR Student for location.)



Teaching Staff


Course Outcomes and Goals

In this course, students will leverage their prior knowledge of mathematics and computer science to develop an understanding of the foundations of the field of machine learning, including supervised, unsupervised, and reinforcement learning paradigms. They will deepen their understanding by designing, implementing and analyzing computational systems and by solving problems by theoretical analysis.

By the end of this course, students will be able to:
  1. Analyze machine learning systems using appropriate practical and theoretical models.

  2. Design, implement, test and validate machine learning systems subject to appropriate requirements and external constraints.

  3. Communicate ideas and research findings effectively in written reports.

  4. Recognize, recall, and explain fundamental models and concepts of machine learning using both language and mathematics.

Course Mechanics

Class time will be used for lectures, collaborative problem solving activities, quizzes, and end of semester project presentations. Graded work will of three exams (two quizzes and a final), programming assignments (6), problem sets (6), and a project.


You will be expected to do substantial reading outside of class.



Textbook and Materials


Learning Activities



There are four distinct kinds of graded learning activity in this course:


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 >=93%
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


Academic Honesty

Do not misrepresent someone (or something) else's work as your own.

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

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

Do not misrepresent AI generated work as your own. You should not use AI to complete whole assignments for this course. You may use AI to explore course topics at the level of ideas and examples. You may use AI to help you improve your writing, or to improve your ability to write programs; however, you should not use it to directly complete the tasks of the assignments. Submission of uncited AI generated work will be considered an act of academic dishonesty.

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.

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.

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.



Schedule


DateDoWTopicReading
Aug 26TMachine LearningDL 1.1-1.3
Aug 28RProbability and Information"DL 2.1-2.3, 2.5-2.6; 3.1-3.2;"
Sep 2TRegression and ClassificationDL 4.1-4.3; 5.1-5.4; Appx A
Sep 4RMultilayer PerceptronsDL 6.1-6.4; 8.1
Sep 9TGradient DescentDL 7.1-7.4; 8.1-8.2; 9.2-9.3
Sep 11RConvolutional Neural Networks"DL 9.1, 10.1-10.5"
Sep 16TTransformersDL 12.1
Sep 18RLarge Language ModelsDL 12.2-12.4
Sep 23TNon-parametric MethodsDL 3.5
Sep 25RMax Margin ClassifiersPRML 7.1
Sep 30TThe Kernel TrickPRML 6.1; Appx E
Oct 2RReview
Oct 7TQuiz 1
Oct 9RQuiz Solutions
Oct 14TFall break.
Oct 16RConditional IndependencePRML 8.1-8.2; (DL 11.1-11.2)
Oct 21TMarkov Random FieldsPRML 8.3-8.4
Oct 23RSampling AlgorithmsITLA 29.1-29.3; (DL 14.1-14.2)
Oct 28TMetropolis-HastingsITLA 29.4-29.5;
Oct 30RClustering AlgorithmsDL 15.1-15.3
Nov 4TExpectation MaximizationDL 15.4
Nov 6RPrinciple Component AnalysisDL 16.1
Nov 11TReinforcement LearningRLIA Ch 1-3
Nov 13RQ-LearningRLIA Ch 4
Nov 18TReview
Nov 20RQuiz 2
Nov 25TQuiz solutions
Nov 27RNo class (Thanksgiving)
Dec 2TSymposium
Dec 4RSymposium
Dec 17WFinal Quiz