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CS 490 :: Machine Learning :: Spring 2019
Announcements
- Please fill out this second comprehensive course evaluation of this ML course. This is a new course with a novel structure so your feedback is appreciated!
- The IC CS490 Music Genre Classification Challenge is open!
- A first submission is due on Tuesday, April 16th.
- The IC Office of Analytics and Institutional Research is hiring an analytics intern.
- Seems like a great on-campus job for someone with ML skills.
Schedule
- Videos are from the Machine Learning by Andrew Ng (MLAN) playlist on YouTube.(Online Course Notes)
- Programming assignments are from Gerges Dib's ML Python Assignments and match up with the videos.
- Doug's CS490 Course Material can be found on Google Drive: lectures, code samples, notes
| Week | Topic / Lecture Notes | Videos/ Readings | Labs | Exams / Project |
|---|---|---|---|---|
| Week 1 Jan 22 |
Intro to ML |
ML on Wikipedia 1.1-1.3 (Wed) 2.1-2.4 (Fri) |
HW -1: Code Setup | |
| Week 2 Jan 28 |
Linear Algebra Python/Numpy |
2.5-2.8 (Mon) 3.1-3.6 (Wed) LinAlg Notes (Sec 1.1-3.7) |
Python Tutorial HW 0: Data (Due: Tues 2/5) |
|
| Week 3 Feb 4 |
Linear Regression | 4.1-4.4 (Mon) 4.5-4.7 (Wed) SuperLearn Notes (Sec 1-3) |
HW 1: Linear Regression (Due: Tues 2/12) |
|
| Week 4 Feb 11 |
Logisitic Regression | 6.1-6.7 (Mon) 7.1-7.4 (Wed) SuperLearn Notes (Sec 5) |
HW 2: Logistic Regression (Due:Tues 2/19) |
|
| Week 5 Feb 18 |
Neural Networks | 8.1-8.4 (Mon) 8.5-8.7 (Wed) NN Notes Sec 1-2 |
HW 3: Neural Networks (Due: Tues 2/26) |
Exam 1: Linear/Logistic Regression (2/22) |
| Week 6 Feb 25 |
Neural Networks | 9.1-9.3 (Mon) 9.4-9.8 (Wed) NN Notes Sec 3 |
HW 4: Neural Networks Training (Due: Tues 3/8) |
|
| Week 7 March 4 |
Bias vs. Variance | 10.1-10.3 (Mon) 10.4-10.7 (Wed) |
HW 5: Regularization (Due 3/22) |
|
| SB March 11 |
Spring Break | |||
| Week 8 March 18 |
ML Systems | 11.1-11.3 (Mon) 11.4-11.5 (Wed) |
Project Overview Music Processing Tools | |
| Week 9 March 25 |
SVM | 12.1-12.3 (Mon) 12.4-12.6 (Wed) |
HW 6: SVMs (Due 4/2) |
|
| Week 10 April 1 |
Unsupervised Learning | 13.1-13.5 (Mon) 14.1-14.7 (Wed) kMeans Notes PCA Notes |
HW 7: Clustering & PCA (Due 4/9) |
IC CS490 Music Genre Classification Challenge |
| Week 11 April 8 |
Anomaly Detection | 15.1-15.4 (Mon) 15.5-15.8 (Wed) |
1st Leaderboard Entry (4/16) | |
| Week 12 April 15 |
Recommender Systems | 16.1-16.6 (Mon) Netflix Paper (Wed) (Danny's Slides) |
HW 8: Anomaly Detection (4/26) |
|
| Week 13 April 22 |
Large Scale ML OCR |
17.1-17.6 (Mon) 18.1-18.4 (Wed) |
Final Exam (Tues. 4/23) | |
| Week 14 April 29 |
Deep Learning Research Talks |
CS490 Talk Schedule Project Report | ||
| Finals Week May 6 |
Final Reports Due (5/8 at 5pm) |
Course Overview
Machine Learning (ML) involves us finding patterns from data. In this course, we will
- introduce ML problems (classification, regression, clustering)
- understand the ML pipeline from raw data to decision making to evaluation
- implement popular ML algorithms (k-nearest neighbor, linear regression, k-Means)
- work on real-world ML tasks (optical character recognition, music recommendation)
See the Syllabus for more Information
Course Information
- Prof. Doug Turnbull (dturnbull@ithaca.edu, (607) 274-5743)
- Section 1
- Class: MWF 2-2:50pm in Williams 309
- Lab: Tu 2:35-3:50pm in Williams 309
- Section 2
- Class: MWF 12-12:50pm in Williams 309
- Lab: Tu 10:50am-12:05pm in Williams 210
- Office Hours: Williams 321E
- MW 3pm-4pm, Tu 4-5pm
- By Appointment
- Whenever my door is open
- Course Teaching Assistant: Danny Akimchuk
- Evening Help Sessions: TBA in Williams 309