Lecture: Tuesdays and Thursdays, 2:30-3:45 in Agricultural Engineering Building (North?), Room 119
The course provides an introduction to machine learning algorithms and applications. Machine learning algorithms answer the question: "How can a computer improve its performance based on data and from its own experience?" The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness. Specific topics include linear and non-linear regression, (stochastic) gradient descent, neural networks, backpropagation, classification, Markov decision processes, state-value and action-value functions, temporal difference learning, actor-critic algorithms, the reward prediction error hypothesis for dopamine, connectionism for philosophy of mind, and ethics, safety, and fairness considerations when applying machine learning to real-world problems.
Note: To display .ipynb files, right click the link and select "Save Link As" (Firefox), or a similar option in your browser. Then open the downloaded file using VSCode.
| Title | Date | Description | Document Link |
|---|---|---|---|
| Jupyter Notebook Installation | N/A | Instructions describing how to install Python, VSCode, and set them up to work with .ipynb (Jupyter Notebook) files on MacOS and Windows. | instructions (.pdf), instructions (.md) |
| Jupyter Notebook Introduction | N/A | An example Jupyter Notebook explaining how notebooks work and providing a very basic introduction to Python. | notebook (.ipynb) |
| Assignment Number | Date Assigned | Date/Time Due | Document Link |
|---|
| Lecture | Lecture Date | Topic | Document Link |
|---|---|---|---|
| Lecture #1, Part 1 | January 30, 2025 | Course Introduction | slides (.pdf) |
| Lecture #1, Part 2 | January 30, 2025 | Introduction to ML | slides (.pdf) |