CSCI 335:
Machine Learning

RIT CS Department, Fall 2023 (Section 1)
Instructor: Prof. Zanibbi

Week 15

This week we will focus on the project and briefly touch on some additional topics in the Charniak text.

  • Final Office Hours:
  •     This Week:  Monday 3-5 (Zoom -- see discord), Tuesday 3:30-4:30 (in-person), Friday 11am-2pm (usual, Zoom + in-person)
  •     Next Week (*Reading Day Only):  9am-10am, 11am-12pm, 1pm-4pm (in-person + Zoom)
  • Assignment 4 (required) is due Wednesday at 11:59pm. Let submission will close Mon Dec 11 at 11:59pm.
  • (Optional) Assignment 5 (resubmission for *one* of A1/2/3 - hard deadline Mon Dec 11, 11:59pm). Your final assignment grade will be based on A1-A4, whether or not you resubmit one of the earlier assignments (1/2/3).
  • Final Project:  
  •    Project presentations will be given during the exam, Friday Dec. 15, 1:30-4pm regular classroom (Slaughter Hall Rm. 2150).
  •    The final report, code, and rough work due the Sunday after (submit through MyCourses by Dec. 17, 11:59pm). 
  • Please complete the course evaluation.  This will help improve future offerings of the course, particularly in sorting out what to keep, change, or replace in the course.

Week 14

This week we will focus on the project and some additional topics from the Charniak text.

  • The final quiz (Quiz 10) will be released on Wednesday, and will be due Thursday before class.
  • Assignment 4 (required) is due next week, Wednesday of Week 15 (Dec. 6th). 
  • Assignment 5 (optional - hard deadline Mon Dec 11, 11:59pm). This is not a new assignment, instead, students may resubmit one of Assignments 1-3. If five assignment (with the optional A1-3 resubmission) are submitted, the four highest assignment grades will be kept, otherwise your assignment grade will be based on A1-A4.
  • Project Proposals will be returned this week.
  • (Reminder) Final Project:  Project presentations during the exam (Dec. 15, 1:30-4pm). Final report, code, and rough work due the Sunday after (Dec. 17, 11:59pm). 
  • *Please complete the course evaluation.  This will help improve future offerings of the course, particularly in sorting out what to keep, change, or replace in the course.

Week 13

This week will finish up RNNs / LSTMs, and introduce sequence-to-sequence models.

  • No Class on Thursday.  Enjoy the holiday.
  • Assignment 4 will be released over the break, and will be due Friday. Dec 1st after the holiday break.
  • Project Proposals will be returned by Monday of next week.
  •     (Reminder) Final Project:  Project presentations during the exam (Dec. 15, 1:30-4pm). Final report, code, and rough work due the Sunday after (Dec. 17, 11:59pm). 
  • Reading:  Ch. 5 of Introduction to Deep Learning (Charniak)  -- on sequence-to-sequence models

Week 12

Topics this week: recurrent neural networks, including Long-Short Term Memory (LSTM) models.

  • Quiz 9 is due Thursday at 11:59pm
  • Assignment 4 will be released by Tuesday before the break, and will be due Friday. Dec 1st after the holiday break.
  • Assignment 5 will be optional, and due Fri Dec 8th.  Your lowest assignment grade will be dropped -- this means that each remaining assignment will be 12.5% of your final grade (rather than 10%).  
  • Final Project:  Project presentations during the exam (Dec. 15, 1:30-4pm). Final report, code, and rough work due the Sunday after (Dec. 17, 11:59pm). 
  • Reading:  Ch. 4 of Introduction to Deep Learning (Charniak)  -- on recurrent neural networks

Week 11

We are continuing our discussion of language models and recurrent neural networks this week.

  • Quiz 8 will be released Wednesday, and due Thursday before class (1pm)
  • Project Proposals (Deadline extension): Project proposal is due Sunday, Nov. 12th at 11:59pm (submit through MyCourses).
  •     Late submission deadline is still Friday, Nov. 17th at 11:59.     
  •     Review the recommendations for completing the project 
  •      Suggestions for using conda to run different research systems are provided in the #projects channel on discord.
  • Reading:  Ch. 4 of Introduction to Deep Learning (Charniak)  -- on recurrent neural networks
  • Assignment 4 will be released Monday of Week 12, and is due Sunday evening before Wk 13.

Week 10

We are concluding our discussion of CNNs and starting on recurrent neural nets this week.

  • Assignment 3: due Friday at 11:59pm **Make sure to check the updated documents and code (MyCourses)
  •     Clarifications made for Q3, Q4, correction for bonus, and additional installation instructions/tools provided.
  •     Additional details available in the #assignments and #systems-install channels on discord. 
  • Quiz 6 will be released Wednesday, and due Thursday before class (1pm)
  • Project Proposals (Update): Project proposal is due next Friday (Nov. 10th) at 11:59pm (submit through MyCourses).
  •      Reminder: check the recommendations for completing the project successfully
  •      Suggestions regarding use of conda for running different research systems provided in the #projects channel on discord.
  • Reading:  Ch. 4 of Introduction to Deep Learning (Charniak)  -- on recurrent neural networks

Week 9

We are continuing our discussion of CNNs (convolutional neural networks) this week.

  • Assignment 3: out Thursday, due next Friday at 11:59pm (Nov 3) 
  • Quiz 6 will be released Wednesday, and due Thursday before class (1pm)
  • Project Proposals (Update): Project proposal is due Friday Nov. 10th at 11:59pm (submit through MyCourses).
  •     Consult the documents on the proposal and final project in MyCourses, 
  •      including the recommendations for completing the project successfully.
  • Reading:  Ch. 3 of Introduction to Deep Learning (Charniak)  -- on convolutional neural networks
  • The course schedule has been updated (see link above).
  • The syllabus has been updated regarding contacting the instructor on day that items are due (see link above).

Week 8

We are continuing our discussion of Deep Neural Networks and their implementation this week.

  • Reading:  Ch. 3 of Introduction to Deep Learning (Charniak)  -- on convolutional neural networks
  • Quiz 5 will be released by Friday.
  • Project Proposal Due Tues, Nov. 6:  Students need to select groups by this Friday in MyCourses. Project materials are online in MyCourses. 

Week 7

There is no class on Tuesday -- enjoy the break.  On Thursday we will continue our discussion of implementing a single-layer neural net for MNIST classification in TensorFlow (see lecture slides for additional information on using TensorFlow).

  • Reading:  Ch. 2 of Introduction to Deep Learning (Charniak) 
  • Assignment 3 will be released this week
  • Quiz 4 has been released, and is due Saturday at 12pm.
  • Project: Start thinking about forming your group for the project. The project will be released in Week 8, and students will need to select groups by Friday of Wk 8 (students without a group will be randomly assigned by the MyCourses shell)
  • Lectures:  **No Class on Tuesday**

Week 6

This week we are concerned with training feed-forward neural networks using backpropagation, and its implementation using TensorFlow.

  • Reading:  Read Ch. 2 of Introduction to Deep Learning (Charniak). 
  • Assignment 2 is due Thursday (Oct 5th at 11:59pm) 
  • We will have a quiz on Wednesday, due 1 hr before class.
  • Lectures:  An additional video lecture on Bayesian classification using Gaussian density functions (see under Zoom 'Cloud recordings') was posted Monday. Some suggestions related to A2 are also provided in the video (and in the #assignment channel on the course discord).

Week 5

This week we are starting to work on neural networks, and specifically feed-forward networks and the backpropagation algorithm used to fit their model weights.

  • **Office Hours Change this Week.**  Office hours will be Friday from 10am-10:45am, and then from 12:30pm-2:30pm (this week only, due to a conflict).
  • Reading:  Read Ch. 1 of Introduction to Deep Learning (Charniak). 
  • Assignment 2 has been posted, and is due next week (Thurs Oct 5th at 11:59pm) 
  • There is no quiz this week. Best of luck with the career fair!
  • Lectures: Update: The missed lecture from last week (on Bayesian classification, and a bit on Assignment 2) will be posted by Wednesday evening.

Week 4

This week we will continue our discussion of Bayesian Decision Theory, along with approximated Bayes' models using gaussian functions for the probability density function in each class.

  • Reading:  Read the probability and language model review from Charniak's Statistical Language Learning Ch. 2, and the introduction to Bayesian Decision Theory for classification in Duin et al.'s  Classification, Parameter Estimation, and State Estimation (also Ch. 2). Both are well-written and reader-friendly. They are also available through MyCourses.
  • Quiz 2 will be released Wednesday afternoon, and will be due at 1pm (1 hr before class) on Thursday.  Note: Answers to Quiz 1 have been posted on MyCourses.
  • Lectures: The missed lecture from last week will be posted over the weekend, before Monday of next week. This will provide  an opportunity to finish and review topics from our study of Bayesian Decision Theory.
  • Assignment 2 will be released by Monday of next week.

Week 3

**Lecture is cancelled Tuesday (Prof. Zanibbi away); the missed lecture will be posted as a video on MyCourses later this week.**

  • Assignment 1  is due Thursday (Sept. 14) at 11:59pm through MyCourses. Late submissions will be accepted up to one week later, with a 10% penalty and a possible delay in return of the grade.

Week 2

Welcome to Week 2. Announcements will continue to be posted here throughout the semester.

  • Reading:  Read the Hastie book, Ch. 1, and Ch. 2.1-2.3. This is available through MyCourses.
  • Assignment 1  has been posted in MyCourses. It is due next Thursday (Sept. 14) at 11:59pm through MyCourses. Late submissions will be accepted up to one week later, with a 10% penalty and a possible delay in return of the grade.
  • Quiz 1 will be released Wednesday afternoon, and will be due at 1pm (1 hr before class) on Thursday.
  • Lecture for Tuesday of Wk 3 is cancelled (Sept 12, No Class). This missed lecture will be posted as a video in MyCourses.

Week 1

Welcome to the Fall 2023 (Section 1) Machine Learning course web pages.  These web pages will be used to communicate information about the course, along with news, deadlines, etc. 

  • Prof. Zanibbi is the course instructor.
  • The course syllabus and schedule are available. The schedule may change during the semester, and changes will be announced here and in-class. Use the links above to see the schedule and syllabus.
  • Lectures:  Tuesdays and Thursdays in SLA-2150 (Slaughter Building, Building 78)
  • Lectures will be given in-person and live over Zoom. Lecture attendance is strongly recommended; students will be tested on additional material discussed in lecture. 
  • Deliverables: A description and grade weight for course deliverables can be found below.
  • Quizzes, assignments and projects are distributed and submitted using MyCourses.

Grade Components

10 quizzes will be given out weekly beginning in Week 2 of the semester. The two lowest quiz grades will be dropped.

Quizzes will be available through a "Quizzes" link in MyCourses. Students are permitted to retake a quiz as many times as they like, and will receive the highest score that they receive across these attempts before the deadline. Students will have at least one day (24 hrs) to complete each quiz.

5 assignments will be given, beginning in Week 3 of the semester.

Assignments involve both writing and programming questions. Students are expected to follow submission instructions as provided in the assignments carefully. 

Instead of an exam, students will complete a group project at the end of the semester in groups of 3 students. The project involves designing, executing, and reporting on an experiment with a machine learning model.

The first deliverable for the project is an experiment design, and a draft of the final experiment report and materials to be delivered for the final project.

Instead of an exam, students will complete a group project at the end of the semester in groups of 3 students. The project involves designing, executing, and reporting on an experiment with a machine learning model.

The final deliverable for the project are the experimental results, code, and experiment report  (20%), along with a short 5-10 minute presentation given during the exam slot (5%). 

CSCI 335 Machine Learning, Fall 2023
RIT Department of Computer Science