Deep Learning (MSc in Data Science program)
Students dive deeper into neural networks (NN), the bedrock of deep learning, and how variants are used for predictive analytics—from natural language processing to cancer detection (image processing) to predicting stock market performance. Students are tasked to construct their own single-layer artificial neural network in Python with numpy. This allows students to understand better how neural networks genuinely function. Participants are also introduced to other types of NNs, including deep NNs such as convolutional neural networks, recurrent neural networks, and GANs with Tensorflow/Keras, which are relevant packages/libraries in the field, among other tools. The course is hands-on and involves multiple real-world projects– business and/or industry-driven. The course culminates with students giving a public presentation of original and novel applications of deep learning methods.
Advanced Deep Learning Algorithms(PhD in Data Science program)
Advanced Time Series Analysis (PhD in Data Science program)
Mathematics for Data Science (MSc in Data Science program)
The module introduces the maths, including various techniques and formulations, necessary to implement data science models, algorithms, and machine learning models. Students will familiarize themselves with the different concepts, notations, and rules on which most data science techniques and models are based.
Machine Learning (MSc in Data Science program)
This course introduces students to the world of machine learning and predictive analytics. It is a hands-on and application-heavy module that examines real-world data and cases from different fields. At the end of the course, students are expected to acquire needed machine learning skills involving both supervised and unsupervised learning. In addition, they will be exposed to the best practices in predictive analytics, including how to evaluate models properly.
Complexity Science (MSc in Data Science program)
Artificial Intelligence (MSc in Innovation and Business program)
Electromagnetic Theory (Physics 131 & 132)
Theoretical Mechanics (undergraduate [121, 122], graduate [221], and advanced graduate level [212]);
General Physics (mechanics [71, 101, 71.1], electromagnetism[72, 72.1, 102], modern physics [73, 73.1], Physics and Astronomy for Pedestrians [10]);
Advanced Methods in Experimental Physics (Physics 191 & 192);
Advanced Methods in Computational Physics (Physics 212 & Physics 215)
Mathematical Methods in Contemporary Physics (Physics 313)