Human: What do we want!?
Computer: Natural language processing!
Human: When do we want it!?
Computer: When do we want what?
Photo by Google DeepMind on Unsplash
What Is Engineering Machine Intelligence Engineering?
Machine intelligence (MI) is the study, development, and application of algorithms that can identify patterns in data and, using these insights, make decisions when confronted with new situations. The EngSci MI major covers the math, hardware, computer science, and software engineering involved in artificial intelligence, machine learning, and big data analytics. Graduates from the major can pursue graduate studies or enter industry to develop products and services with their skills.
The incredible versatility of machine intelligence means graduates can use their skills and knowledge in applications as diverse as self-driving vehicles, finance (modelling markets and automatic trading), marketing (analyzing marketing data), and healthcare (using computers to diagnose diseases on the microscopic scale), etc.
Why Choose MI?
- You’re interested in computer science and artificial intelligence! If you have an interest in the theory or application of these fields, this is the option for you. Especially for theory; other majors involve aspects of computer science, programming, and artificial intelligence, but MI provides you with the most opportunities to study theory in these fields.
- You want a skillset that can be used in many fields! Many companies that aren’t primarily concerned with software need MI professionals. For example, a cell phone manufacturer may hire you to analyze their marketing data using machine learning, or a sports betting company may use machine learning to set their odds. In each case, the company isn’t concerned with producing software or algorithms, but they benefit by having machine learning professionals on their team.
- You’re interested in entrepreneurship! There are many startups around the world that use machine learning to develop unique products and services. New companies are using machine learning to trade on the stock market, develop camera systems that can detect objects and their surroundings, develop software that can suggest a diagnosis for a patient, and more! The power of machine learning has created new tools for solving problems, and many of these tools can be developed into a successful company!
- You could be as amazing as Matt Zeiler (EngSci 0T9) and his startup Clarifai. He came to ESEC 2018!
Courses in Year 1 and Year 2 That Relate to MI:
Year 1
ESC103: Engineering Mathematics and Computation and MAT185: Linear Algebra
In ESC103, you’re introduced to some topics in linear algebra such as matrices and vectors before moving on to computation. Computation is the process of using a computer to calculate or approximate an answer. In ESC103, you’ll learn to use the MATLAB programming language to turn your math calculations into automated processes. Many topics in machine learning are heavily dependent on computation, so the introduction to this course is very useful.
In MAT185, you’ll dive much deeper into the mathematical theory of linear algebra. Many concepts and fields in Computer Science make heavy use of linear algebra.
ESC180: Introduction to Computer Programming and ESC190: Computer Algorithms and Data Structures
Machine learning and artificial intelligence require coding to implement; thankfully ESC180 is here to help! In ESC180 you’re introduced to the basics of programming including conditional statements, loops, and functions. These small components come together to build the sophisticated machine learning tools we have today.
In ESC190, you’ll dive into an important topic related to machine learning: algorithms. An algorithm is just a set of rules that processes data into a way you want, such as sorting, building a set with certain properties, or optimizing the data. Algorithms are used throughout machine learning, so ESC190 is indispensable to this major. The course also covers data structures. These are objects that store and organize information on a computer in a useful way. Of course, with how data heavy machine learning is, data structures are an integral part of the field. The course will also cover a bit of computer hardware. Having knowledge of hardware helps programmers use their computer to its full capabilities and make more efficient and useful programs.
ESC101: Praxis I and ESC102: Praxis II
While Praxis probably won’t directly involve machine learning or artificial intelligence, the courses usually give you plenty of design opportunities to work programming into your solution. Moreover, Praxis’ themes of analyzing a situation, identifying an opportunity, then designing a solution all match up with themes in machine intelligence.
Year 2
ECE286 will introduce you to the fundamentals of probability and statistics. Statistical mechanics is one of the most important fields in modern physics, and has wide application in quantum mechanics. ECE286 will provide you with the tools you need to begin studying statistical mechanics. Statistics and probability are also of deep interest in experimental physics, especially when considering the validity of an experiment’s results (error and uncertainty).
Interesting Courses in This Major
ECE368 Probabilistic Reasoning
This course focuses on probabilistic models and discerning actionable information and trends from data. The applications of this course cover machine learning, searching, recommendation systems, finance, robotics and much more. You will start with a review of probability concepts, then move into probabilistic models. These include temporal, vectors, and graphical models.
ROB311 Artificial Intelligence
This course tackles artificial intelligence from a rigorous mathematical standpoint. From the EngSci majors site: “Topics include the history and philosophy of AI, search methods in problem solving, knowledge and reasoning, probabilistic reasoning, decision trees, Markov decision processes, natural language processing, and elements of machine learning such as neural-network paradigms.”
Where To Get Some Experience Before Deciding?
aUToronto
aUToronto is U of T’s self-driving car team. The team competes in self-driving car competitions and is the consecutive winner of the 2018, 2019, 2020, and 2021 SAE AutoDrive Challenge. aUToronto has several sub teams, including a deep learning team, which is probably the closest tied to the MI major. However, there are tons of other ways you can apply the skills MI gives you with this team since a major part of self-driving cars is programming the sensors and processing their signals.
If you’re interested in machine intelligence or its applications, apply to the team! aUToronto is interested in recruiting enthusiastic first years.
U of T AI
University of Toronto Artificial Intelligence is an undergraduate student group that helps U of T students get involved in the growing field of artificial intelligence. The designs and runs the annual ProjectX research competition, hosts the U of T AI Conference, and runs an introductory ML Educational program for undergraduates. For more information on how to get involved in their programs, you can visit the U of T AI website.
UTMIST
The University of Toronto Machine Intelligence Student Team or UTMIST is another great club for professional development. This group is focused on “clearing the mist” around Machine Intelligence and the field of deep learning. The club specifically aims to connect undergraduates with top scholars in this field to foster a deeper understanding of the topic in undergraduates and potentially cultivate research opportunities. It’s worth mentioning that Geoffrey Hinton’s Machine Learning Group – the first of the three world-leading forces in the field of deep learning, is based at U of T!
Visit the Skule Clubs and Design Teams pages to find more extracurriculars.
Check out the EngSci majors website here for more info: