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CS 490 :: Machine Learning :: Fall 2020
Announcements
- Take some time for your course evaluation (Check your email.)
- Also, your feedback on my Comprehensive Course Evaluation is also greatly appreciated.
- Check out notes for the Project Report and Research Talk Schedule
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 Sept 8 |
Intro to ML |
ML on Wikipedia 1.1-1.3 (Wed) 2.1-2.4 (Fri) |
Lab -1: Code Setup | ||
| Week 2 Sept 14 |
Linear Algebra Python/Numpy |
2.5-2.8 (Mon) 3.1-3.6 (Wed) LinAlg Notes (Sec 1.1-3.7) |
Lab 0: Python Tutorial | ||
| Week 3 Sept 21 |
Linear Regression | 4.1-4.4 (Mon) 4.5-4.7 (Wed) SuperLearn Notes (Sec 1-3) |
Lab 1: Linear Regression | ||
| Week 4 Sept 28 |
Logisitic Regression | 6.1-6.7 (Mon) 7.1-7.4 (Wed) SuperLearn Notes (Sec 5) |
Lab 2: Logistic Regression | ||
| Week 5 Oct 5 |
Neural Networks | 8.1-8.4 (Mon) 8.5-8.7 (Wed) NN Notes Sec 1-2 |
Lab 3: Optical Character Recognition |
||
| Week 6 Oct 12 |
Neural Networks | 9.1-9.3 (Mon) 9.4-9.8 (Wed) NN Notes Sec 3 |
HW 4: Neural Networks Training |
Exam 1 Linear and Logistic Regression with Regularization (10/16) | |
| Week 7 Oct 19 |
Bias vs. Variance | 10.1-10.3 (Mon) 10.4-10.7 (Wed) |
Lab 5: Regularization | Project Overview | |
| Week 8 Oct 26 |
ML Systems | 11.1-11.3 (Mon) 11.4-11.5 (Wed) |
IC Alum Panel #1 Laurence, Naomi, Alex | ||
| Week 9 Nov 2 |
SVM scikit-learn |
12.1-12.3 (Mon) 12.4-12.6 (Wed) |
Lab 6: SVMs | ||
| Week 10 Nov 9 |
Unsupervised Learning | 13.1-13.5 (Mon) 14.1-14.7 (Wed) kMeans Notes PCA Notes |
Lab 7: Clustering & PCA | ||
| Week 11 Nov 16 |
Anomaly Detection | 15.1-15.4 (Mon) 15.5-15.8 (Wed) |
IC Alum Panel #2 Anika V., Tim C., Caitlin W. | ||
| Week 12 Nov 23 (Thxgiving) |
Recommender Systems | 16.1-16.6 (Mon) | Lab 8: Anomaly Detection | ||
| Week 13 Nov 30 |
Large Scale ML OCR |
17.1-17.6 (Mon) 18.1-18.4 (Wed) |
Exam 2 Cummulative (12/4) | ||
| Week 14 Dec 7 |
Deep Learning Research Talks |
Project Report and Talk Schedule | |||
| Finals Week Dec 14 |
Research Talks | Research Reports Due (Tuesday, Dec 15 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 1-1:50pm in Zoom
- Lab: Tu 1:10-2:25pm in Zoom
- Office Hours: Williams 321E
- Mondays 12-12:50pm
- Wednesdays 3-4 pm
- Fridays 2-2:50pm
- By Appointment
- Ping me in the CS490_ML Slack Group.