Course Description
Covers fundamental principles, concepts and theories and implementation details related to deep learning technologies. Includes common neural network architectures such as Multi-layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM and Transformer architectures and their applications in business.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
Understand core concepts and applications
Grasp the concepts, assumptions, and applications of generalized linear models (e.g., linear regression, logistic regression, gamma regression, Poisson regression) and graphical probabilistic models (e.g., Naïve Bayes, Hidden Markov models, Latent Dirichlet Allocation).
Understand the rationale and applications of deep learning models such as deep neural networks, convolutional networks, recurrent neural networks, and transformers.
Apply machine learning algorithms to business problems
Implement and apply machine learning algorithms in R, SAS, or Python for big data projects.
Use deep learning for tasks like regression, time-series prediction, computer vision, and natural language processing in business contexts.
Enhance programming and data skills
Strengthen R programming skills in data preparation, exploration, and visualization.
Gain proficiency in applying each machine learning algorithm to real-world projects.
Integrate theory with practice
Learn the theory and application of each selected model, including the reasoning behind algorithm design.
Apply these models to real-world business projects to solve practical problems.
Course Grading Information:
Activity/Performance Measure | Percentage/Points |
|---|---|
Discussion Assignment & Participation | 15 points |
Assignments (Individual) | 30 points |
Midterm Exam | 25 points |
Final Project (Final Exam) | 30 points |
Total | 100 points |
The letter grade will be based on the following distribution:
Percentage | Letter Grade |
|---|---|
> 95% | A |
90%-94.9% | A- |
87%-89.9% | B+ |
84%-86.9% | B |
80%-83.9% | B- |
77%-79.9% | C+ |
74%-76.9% | C |
70%-73.9% | C- |
Below 70% | F |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course. Asynchronous Online Course.
Course Materials Purchased by the Students:
Required Textbook (Free or for Purchase from Publisher): Deep Learning with Python Author: Francois Chollet & Matthew Watson Publisher: Manning Edition: 3rd (https://deeplearningwithpython.io)
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.