Each lab will be used for two purposes. First, the students will be given a programming assignment to implement
one of the class demos using Python to get a “hands-on” learning experience. The output of this assignment and
students’ understanding of the related concept will be graded and will contribute towards the grading assessment
component (see below) of the lab. Second, the lab will also serve as a time when the students will be able to
discuss the progress (queries/questions/received feedback) on their semester project for the course with the TFs
and the instructor.
| Week |
Task |
| Week 1 |
Features Extraction |
| Week 2 |
k-Means Clustering |
| Week 3 |
Dimensionality Reduction using PCA |
| Week 4 |
Classification using LDA |
| Week 5 |
Distance-based Classification |
| Week 6 |
Classification using k-NN and Performance Metrics |
| Week 7 |
Classification using SVM and Performance Metrics |
| Week 8 |
[Mid-term Evaluation] |
| Week 9 |
Performing Time Series and NLP Analysis on Real-world Data |
| Week 10 |
Perceptron |
| Week 11 |
Backpropagation |
| Week 12 |
Hyperparameters Tuning |
| Week 13 |
CNNs and LSTMs |
| Week 14 |
Data Visualization using t-SNE and Autoencoders |
| Week 15 |
Reinforcement Learning |
| Week 16 |
[Final Evaluation] |
