ikeybenz/ds-2.4-advanced-topics-in-data-science

Advanced-Topics-In-Data-Science

Course Description

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.

Why you should know this

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

Prerequisites:

• DS 2.2

Learning Outcomes

By the end of this course, you will be able to ...

1. Describe the Recommender System (RS) and the various methodologies in RS
2. Understand all components in NLP, including Bag-of-Word, TFIDF, Topic Modeling, Word2Vec
3. Learn, build, and gain inference from a probabilistic graphical model
4. Generate computer images from GANs
5. Apply PageRank on a defined Network

Schedule

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: Tuesday, May 28 – Tuesday, July 2, 2019 (6 weeks)

Class Times: Tuesday and Thursday at 3:30–5:20pm (11 class sessions)

Class	Date	Topics
1	Tu, May 28	NLP Pt 1
2	Th, May 30	NLP Pt 2
3	Tu, June 4	Intro to Recommender Systems
4	Th, June 6	Deep Learning for Recommender Systems
5	Tu, June 11	Advanced Keras
6	Th, June 13	Graphical Models
7	Tu, June 18	Network Analysis
8	Th, June 20	GAN
9	Tu, June 25	Seminars Pt 1
10	Th, June 27	Final Exam
11	Tu, July 2	Seminars Pt 2 + Project Presentations

Class Assignments

Seminars

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!

Projects

You will complete individual comprehensive projects in one of the topic areas from class

Rubric/spec for this coming soon!

Evaluation

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

Make School Course Policies

• Program Learning Outcomes
• Grading System
• Diversity and Inclusion Statement
• Academic Honesty
• Attendance Policy
• Course Credit Policy
• Disability Services (Academic Accommodations)
• Student Handbook