Introduction to Machine Learning and Perception

Virginia Tech, Electrical and Computer Engineering
Spring 2013: ECE 4984 / ECE 5984

Course Information

Have you ever wondered how Siri understands voice commands? How Netflix recommends movies to watch? How Kinnect recognizes full-body gestures? How Watson defeated Jeopardy champions? The answer is Machine Learning -- the study of algorithms that learn from large quantities of data, identify patterns and make predictions on new instances.

Instructor: Dhruv Batra
Fri 1-2pm, Whittemore 468
TA: Sherin Aly
Mon 1:15-2:15pm, Durham 377

Class meets: Mon, Wed 2:30 - 3:45, MCB 307
Homeworks
Q&A and Discussions: https://scholar.vt.edu/portal/site/ece4984ece5984; ECE4984_ECE5984 Forum

Notable Mentions

[Updated Aug 1, 2013] Here are some phenomenal achievements by the students who took this class:

Marc Lichtman's class project accepted for publication at MILCOM.

Marc Lichtman, William Headley, Jeffrey Reed. Automatic Modulation Classification under IQ Imbalance using Supervised Learning. In Proceedings of MILCOM 2013 (Track 1: Waveforms and Signal Processing).

Delasa Aghamirzaie's class project accepted for publication at ICCCB.

D.Aghamirzaie, D. Batra, E. Collakova, L. Heath, R. Grene. Modeling and Identifying Regulatory Modules in (Glycine max) Soybean Time Series Gene Expression Data using Bayesian Networks. International Conference on Computational Cell Biology, 2013.

Gordon Christie and Ujwal Krothapalli created a demo video for their class project.
Student poster/demo session featured in ECE News.
If you took this class and have something to share, please email me!

Best Project Prize Winners

Winning Team:

Ujwal Krothapalli and Gordon Christie.
Gesture Activated Interactive Assistant

Runners-up:

SaiDhiraj Amuru and Vireshwar Kumar.
Enabling Hands to Speak American Sign Language Detection.

Talia Weiss
Predicting Ratings in Fanfiction.

Schedule

Week/Day	Date		Topic
1 W	Jan	23	Overview of Machine Learning & Perception	HW0 is out
2 M	Jan	28	Statistical Learning Probability Review Statistical Estimatation (MLE,Map,Bayesian)
2 W	Jan	30	MAP + Bayesian Estimation Gaussians Regression	HW1 is out
3 M	Feb	4	Supervised Learning Supervised learning setup. Linear Models Regression Error Decomposition Bias-Variance Tradeoff Cross-validation
3 W	Feb	6	Error Decomposition (Bias-Variance Tradeoff) Cross-validation Crash Course on Optimization	HW1 due Feb 8
4 M	Feb	11	(Finish) Crash Course of Optimization Classification: Naive Bayes	HW2 is out
4 W	Feb	13	Classification: (Finish) Naive Bayes Classification: Logistic Regression
5 M	Feb	18	Project Details Classification: (Finish) Logistic Regression NB & LR connections
5 W	Feb	20	Support Vector Machines Linear SVMs Hard & soft-margin SVMs	HW2 due Feb 22
6 M	Feb	25	Support Vector Machines Kernel Trick SVM dual Multi-class SVM
6 W	Feb	27	Decision Trees Mutual Information
7 M	Mar	4	In class Mid-Term	Mid-Term
7 W	Mar	6	Neural Networks Perceptron Multilayer Perceptron Backprop	Project Proposal Due
8 W	Mar	11	Spring break. No class.
8 M	Mar	13	Spring break. No class.
9 M	Mar	18	(Finish) Neural Networks Backprop Instance-based Learning 1/k Nearest Neighbour Kernel regression/classification
9 W	Mar	20	(Finish) Nearest Neighbour Ensemble Methods Bagging Boosting
10 M	Mar	25	(Finish) Ensemble Methods Bagging Boosting	HW3 is out
10 W	Mar	27	Unsupervised & Semi-Supervised Learning Clustering K-Means Gaussian Mixture Models
11 M	Apr	1	Unsupervised & Semi-Supervised Learning (Continued) Gaussian Mixture Models
11 W	Apr	3	Unsupervised & Semi-supervised Learning (Continued) EM
12 M	Apr	8	Factor Analysis PCA SVD Dimensionality Reduction	HW3 due
12 W	Apr	10	(Finish) PCA LDA (Intro) Probablistic Graphical Models	project midterm report due
13 M	Apr	15	In-class Project Presentations
13 W	Apr	17	In-class Project Presentations
14 M	Apr	22	(Finish) In-class Project Presentations Bayes Nets: Representation	HW4 is out
14 W	Apr	24	Bayes Nets: Representation conditional independence d-separation
15 M	Apr	29	(Finish) Bayes Nets: Representation Bayes Nets learning parameters via MLE Bayes Nets: Inference Variable elimination
15 W	Mar	1	Hidden Markov Models (HMMs) Representation Learning from fully observed data: N-grams Inference with VE: forward-backward algorithm	HW4 due
16 M	Mar	6	(Finish) Markov Nets / Markov Random Fields Representation Inference Overview of Advanced Topics PAC-Learning VC dimension Structured-SVMs Reinforcement Learning Markov Decision Processes Online Learning Active Learning Semi-Supervised Learning Applications
16 W	Mar	8	Final Poster/Demo Presentations 2:00-4:30pm at 236 Whittemore
17 M	Mar	13	Final Exam 2:05-4:05pm in 307 MCB

Grading

40% Homework
25% Project
10% Mid Term
20% Final
5% Class Participation

Late policy for deliverables

No penalties for medical reasons (bring doctor's note) or emergencies
Every student has 4 free late days (4 x 24hours-chunks) for this course; these cannot be used for midterm or final
After all late days are used up, half credit after two days, zero credit after 2 days.