This course covers popular advanced machine learning and deep learning concepts including recommendation systems (RS) and Deep RS, Bayesian networks, probabilistic graphical models, natural language processing (NLP), LSTM neural networks, Generative Adversarial Networks (GANs), and advanced computer vision (Unet). Students complete individual comprehensive projects in one of the topic areas and present their findings in a seminar format.
All of the models and algorithms learning in this course, are extensively use them. Also, students will choose one topic they like the most and dive deeper into it while learn all other topics from classes and classmates
By the end of this course, you will be able to ...
- Describe the Recommender System (RS) and the various methodologies in RS
- Understand all components in NLP, including Bag-of-Word, TFIDF, Topic Modeling, Word2Vec
- Learn, build, and gain inference from a probabilistic graphical model
- Generate computer images from GANs
- Apply PageRank on a defined Network
NOTE: Due to the shorter summer sessions, for some class sessions you will see multiple topics covered. This is to ensure that we cover the same material that we normally would in non-summer terms.
Course Dates: Monday, October 21 – Wednesday, December 11, 2019 (8 weeks)
Class Times: Monday and Wednesday at 3:30–5:20pm (14 class sessions)
| Class | Date | Topics |
|---|---|---|
| 1 | Mon, Oct 21 | NLP Part 1 |
| 2 | Wed, Oct 23 | NLP Part 2 |
| 3 | Mon, Oct 28 | Intro to Recommender Systems |
| 4 | Wed, Oct 30 | Deep Learning for Recommender Systems |
| 5 | Mon, Nov 4 | Advanced Keras |
| 6 | Wed, Nov 6 | Graphical Models |
| 7 | Mon, Nov 11 | Network Analysis |
| 8 | Wed, Nov 13 | GAN |
| 9 | Mon, Nov 18 | Seminars Part 1 |
| 10 | Wed, Nov 20 | Seminars Part 1 |
| 11 | Mon, Nov 25 | [Review] |
| - | Wed, Nov 27 | NO CLASS - Thanksgiving |
| 12 | Mon, Dec 2 | Seminars Part 2 |
| 13 | Wed, Dec 4 | Seminars Part 2 |
| 14 | Mon, Dec 9 | Final Exam |
| 15 | Wed, Dec 11 | Presentations |
You will complete individual comprehensive projects in one of the topic areas from class and present their findings in a seminar format.
Specs/requirements for the seminars coming soon!
You will complete individual comprehensive projects in one of the topic areas from class
Rubric/spec for this coming soon!
To pass this course you must meet the following requirements:
- Complete all required assignments
- Pass all projects according to the associated project rubric
- Pass the final summative assessment according to the rubric as specified in this class
- This will be further explained in the study guide
- Actively participate in class and abide by the attendance policy
- Make up all classwork from all absences