An Introduction to fundamentals of finance in new ventures and high growth technology-driven businesses, students will learn how to interpret and analyse financial statements and develop pro-forma financial statements. Students will be exposed to and practice “Lean Startup” concepts as a means of maximizing the capital efficiency of a startup and increasing the probability of creating a financially sustainable business. The course will enable students to enhance their knowledge of sound principles of finance and alternative sources of finance. They will learn about best practices in angel and institutional venture capital investing, and the role they play in financing high growth, high tech businesses. Students will also develop skills in dealing with financial issues when pitching their ventures to investors.

The purpose of this course topic is to familiarize broad audiences of students from science and engineering into Artificial Intelligence (AI) and Machine Learning (ML) and encourage them to design their own data science workflow for a given real-life application. Students will learn how different data structures and data types are generated and handled from different acquisition modalities for the purpose of AI/ML development. Formats of Continues versus discrete data will be discussed in multi-dimensional structure. Different applications in real-world examples will be introduced such as in engineering, medicine, and science. Techniques of pre-processing for cleansing the data and their preparation will be introduced. Data management systems will be discussed and explained how to handle big data storage and communication. Post-processing techniques such as QA measures, enhancements methods, augmentation, dimensionality reduction, visualization of data for locally vs globally distributed data will be discussed.

Prerequisites:

• Multivariable Calculus
• Preliminaries in Linear Algebra
• Preliminaries in Statistics
• Preliminaries in Signal Processing and Analysis

The purpose of this course is to engage broad audiences in Engineering and Science for efficient design of AI/ML workflows in different application scenarios. Students will learn how different ML models can be designed and fitted into efficient data structures for representational learning. The course starts with data-centric approach all the way to the model-centric approach designs. In data-centric we will cover topics in supervised labeling, active learning, transferring expert domain knowledge into supervised labels and annotations, statistical analysis of supervised data and their class representation. In model-centric approach we will cover broad topics of supervised and unsupervised machine learning models in details. The general overview of deep learning will be introduced and how we can use different ML models as plug-play tool to fit the labeled data for training purposes. Techniques of optimization and hyper-parameter settings will be studied. Popular applications in deep learning will be introduced in the context of audio classification, image classification, tabulated data classification, and time-sequence data classification.

Prerequisites:

• Multivariable Calculus
• Preliminaries in Linear Algebra
• Preliminaries in Statistics
• Preliminaries in Signal Processing and Analysis

The purpose of this special topic course is to provide foundations and recent advances in designing, training, and testing of deep learning pipelines in computer vision applications using Convolutional Neural Networks (CNNs). The problem of image representation for general computer vision applications will be the core interest of this course. Students will learn how to design a deep CNN model from scratch for a particular computer vision problem, train the network with fast and high precision accuracy optimization algorithms, and optimize its hyper-parameters for fine tuning. The course syllabi will include Multi-Array (Tensor) Analysis, Convolution Layer Design, Feature Pooling, Activation Layers, Feature Normalization, Feature Classifiers, Loss-Functions, Gradient Back-Propagation, Stochastic Optimization, Generalization Problem, Data Augmentation techniques, Hyper-Parameter tuning, Data Augmentation, Transfer Learning, as well as three major applications in computer vision will be discussed in natural imaging, satellite imaging, and medical imaging.

Prerequisites:

• Python Programming (e.g. PyTorch, Tensorflow, Keras): you need to have basic knowledge/ preliminary experience with Python programming. This course, including assignments and projects, involve with Python coding and you should be feeling comfortable to further learn how to code in Python language and gain experience.
• Introduction to Calculus and Linear Algebra
• Preliminaries in Machine Learning

The purpose of this special topic course is to provide recent advances of machine learning/deep learning in the context of computer vision and how they are designed and applied in industrial imaging applications including natural camera imaging in industrial routines (such as autonomous driving systems, line of product quality control, surveillance, recommender systems, etc), satellite imaging, and medical imaging. The pipeline for building sophisticated User-Interface (UI) systems is discussed in several imaging problems including object (region-of-interest) detection, image classification, image segmentation, image enhancement, and image calibration.

Prerequisites:

• Python Programming (e.g. PyTorch, Tensorflow, Keras): you need to have basic knowledge/ preliminary experience with Python programming. This course, including assignments and projects, involve with Python coding and you should be feeling comfortable to further learn how to code in Python language and gain experience.
• Preliminaries in Machine Learning

This course is a full-year Product Design and Development course (fall and winter of same academic year) which forms the core of the Master of Engineering in Technology Management & Entrepreneurship Program.  The cornerstone is a project in which students, individually or in teams of up to four, conceive, design and prototype a product, and develop a business plan (using the business model canvas).  The proposed solution must use modern tools and methods for product design and development and should meet a broad range of constraints including health and safety, sustainable development and environmental stewardship. The course will follow a phase – gate process for which progress will be evaluated at each milestone representing the deliverable for each gate.

This course is a full-year Product Design and Development course (fall and winter of same academic year) which forms the core of the Master of Engineering in Technology Management & Entrepreneurship Program.  The cornerstone is a project in which students, individually or in teams of up to four, conceive, design and prototype a product, and develop a business plan (using the business model canvas).  The proposed solution must use modern tools and methods for product design and development and should meet a broad range of constraints including health and safety, sustainable development and environmental stewardship. The course will follow a phase – gate process for which progress will be evaluated at each milestone representing the deliverable for each gate.

An opportunity for experiential learning related to the management of technology and/or technological entrepreneurship. Students co-design, develop and implement a project in collaboration with an external organization or a designated mentor. The project must be jointly supervised by a representative of the external organization or mentor, and a designated faculty member.

A practical entrepreneurial project which provides an opportunity to explore, implement and recommendations. Students co-design, develop and implement a project in collaboration with an external organization or a designated mentor. The project must be jointly supervised by a representative of the external organization or mentor, and a designated faculty member. Note: Restricted to MTME students.