|Instructor: Prof. Richard Zanibbi (Contact info)
Office Hrs: T/Th 2:00-3:20 GOL 3551
Lectures: T/Th 3:30-4:45pm GLE 3139
Teaching Assistant: Timothy Zee (Contact info)|
TA Office Hrs: M/W 11:00am-12pm GOL 3650
Tutorials: (from Wk 2) Fri 1pm & 2pm Brown Hall 1110
- EXTENSION: Project 2 is due Thursday, Dec. 13th at 11:59pm. Late submissions will be accepted until Friday, Dec. 14th at 11:59pm.
- The final exam will be Tuesday, Dec. 18th from 1:30-4pm in GLE 3139 (our classroom). Students are permitted to bring one page of notes with things written front-and-back ('cheat sheet') to the exam.
- Please complete the course evaluation. Comments on what worked and what could be improved in the course would be greatly appreciated.
- Prof. Zanibbi's office hours next week (wk of Dec. 10th): Mon. Dec. 10th, 10am-12pm and Wednesday Dec. 12th, 11am-1pm.
- Grading keys for A3 and Project 1 are now available through MyCourses. We are aiming to have both graded by Mon. Dec. 10th.
- Tutorial this Friday will cover Project 2.
- This week we will finish talking about LSTM/BLSTM networks, and hold a brief review for the exam on Thursday.
- Assignment 4 is due next Wednesday (Dec. 5th, 11:59). The assignment is on Markov Models, and does not require any programming.
- Tutorial on Friday will focus upon Assignment 4.
- Project 2 will be posted by Friday. Please select your group number for teams of two on MyCourses (under the "Groups" link).
- Deadline Extension for Project 1 due to the power outages over the holiday weekend, the deadline for Project 1 has been extended to Wednesday at 11:59pm. Note: late submissions are still due by Thursday, 11:59pm.
- We will continue talking about Recurrent Neural Networks this week.
- Project 1 is due on Tuesday, Nov. 27th (after the break).
- There will be no class on Thursday due to the holiday break.
- Project 1 is due on Tuesday, Nov. 27th. Instructions on how to work with the nsynth dataset on 'granger' have been posted on MyCourses.
- There will be another tutorial on Friday related to the project.
- This week we will finish our discussion of HMMs, and start discussing Recurrent Neural Networks (RNNs). Assigned readings will be included in the lecture slides/notes.
- Project 1 has been released. It is due Tuesday, Nov. 27th at 11:59pm.
- There will be a turial on Friday, going over the first project.
- Assignment 3 extension: due Tuesday at 11:59pm. Please review the requirements of the assignment carefully before submitting your final report and code.
- Project 1 will be released by early next week. The project will be completed in teams of two. You and your partner need to join the same group (numbered Proj1- 1 through Proj1- 11) under the "Groups" link on MyCourses. All students must complete the project in a group of two, no exceptions. Please sign up with your partner before class on Thursday.
- This week we will be talking about probabilistic graphical model for sequence analysis (e.g., Hidden Markov Models (HMM)). Associated readings from the Barber book are provided in the lecture notes.
- Assignment 3 is due Sunday at 11:59pm.
- Thursday office hrs moved to Friday, 3-4:30pm this week.
- Friday there will be a tutorial (Questions on A3).
- Papers on GANs have been posted on MyCourses.
- Assignment 3 has been posted, and is due next Sunday.
- There will be a tutorial on Friday related to Assignment 3.
- Keys for Assignments 1 and 2 are now available in MyCourses, including sample programs. Make sure to review both the key and the code.
- We will continue our discussion of Conv. Networks and training networks this week; we'll try to get to Generative Adversarial Networks (GANs) next week.
- There will not be a tutorial this Friday. Perhaps stop by the AI@GCCIS event in the atrium, where Tim will be presenting his work.
- We are covering Ch. 9 in the Deep Learning book this week, on Convolutional Neural Networks.
- Assignment 3 will be delayed - we will update you when we know more.
- Read Deep Learning Ch. 6.1-6.4
- This week we are starting to discuss deep feedforward neural networks.
- There will be a tutorial on Friday.
- Assignment 2 has been slightly revised (to correct wording in Q2) and is due Sunday.
- Read Section 5.11 of the Deep Learning book.
- This week we will finish up with graphical models, and switch to talking about fundamentals for 'deep' neural networks.
- There will be a tutorial this Friday, focusing upon Assignment 2.
- Assignment 2 has been posted on MyCourses. It is due next Sunday.
- This week were are shifting to probabilistic graphical models.
- Read Ch. 3/4 of the book on Belief Networks by Barber.
- There will be a tutorial this Friday.
- Read Ch. 5 of the Deep Learning book (link to chapter).
- Assignment 1 is due Sunday at 11:59pm. Please note that the assignment has been updated to clarify Question 1.
- There will be a tutorial this Friday related to Assignment 1.
- Lecture slides and whiteboard captures are available through MyCourses.
- There will be a tutorial on Friday (at 1, repeated at 2pm) introducing PyTorch. Tutorials are in Brown Hall, 1110.
- This week we will be reviewing probability, distributions, and statistics.
- Read Ch. 3 of the Deep Learning book. (link to chapter)
- Assignment 1 will be online by Friday afternoon, and will be due next Sunday at 11:59pm.
- The TA will hold office hours on Wednesday this week, and give a tutorial on Friday (at two times - 1pm and 2pm). TA office hours will be in the CS Grad Lab, and tutorials will be given in Brown Hall (details above).
- The final exam will be given Dec. 18th at 1:30-4pm in our regular classroom (GLE 3139).
- The course syllabus and schedule are online.
- Assignments and projects for the course will be submitted and graded through MyCourses.
- References and textbooks are available through MyCourses. Readings will be announced throughout the semester.