Course Schedule
The materials used for the course.
Table of contents
Machine Learning Week 1
All lecture content has been made from scratch for this course. Some content has been taken from MIT’s Machine Learning course (6.036, Spring 2022) offered to undergraduate students.
| Lecture | Topic | Slides/Notes | Lab |
|---|---|---|---|
| 1 | Introduction to Machine Learning | Slides | Calculus Review Notes / Calculus Review Problems |
| 2 | Python Basics | Colab | Colab |
| 3 | Linear Algebra Review | Notes | TBD |
| 4 | Linear Regression | Slides | TBD |
Python Course Week 2
The lecture material for this course is from the Fall 2016 6.0001 lectures at MIT (on MIT OCW website). This is the first course offered to CS majors, run over a period of 6 weeks. We have chosen a subset of lectures so we can fit the content for this course in one week.
This course is offered to the first year students at JIT. Two lectures are given per day for a total of 4 days.
| Lecture | Topic | Slides/Notes | Lab |
|---|---|---|---|
| 1 | What is Computation? | Slides | Lab |
| 2 | Branching and Iteration | Slides | Same as above |
| 3 | String Manipulation | Slides | Lab |
| 4 | Decomposition, Abstractions, Functions | Slides | Same as above |
| 5 | Tuples, Lists, Aliasing, Mutability, Cloning | Slides | Lab |
| 6 | Recursion | Slides | Same as above |
| 7 | Dictionaries & Guess and Check, Approximations, Bisection | Slides and Slides | Lab |
| 8 | Object Oriented Programming | Slides | Same as above |
The descriptions for the Python final projects can be viewed in this Colab notebook.
ML Course Week 3
The lecture material for this course is from the Spring 2022 and Fall 2020 6.036/6.3900 lectures at MIT. We have chosen a subset of lectures so we can fit the content for this course in one week.
This course is offered to the third and fourth year students at JIT. Two lectures are given per day for a total of 4 days.
If you missed any lectures in person or want to learn more, please watch Tamara Broderick’s online lectures on YouTube here. The slides and lectures side by side can be seen here.
| Lecture | Topic | Slides/Notes | Lab | Recommended Reading |
|---|---|---|---|---|
| 1 | Linear Regression | Slides | Student Copy / Solution | Introduction / Regression |
| 2 | Introduction to Numpy | Documentation | Same as above | N/A |
| 3 | Gradient Descent | Slides | Student Copy | Gradient Descent |
| 4 | Logistic Regression | Slides (used Spring 2022 lecture) | Same as above | Logistic Regression |
| 5 | Neural Networks | Slides | Worked on previous lab | Neural Networks |
| 6 | Convolutional Neural Networks | Slides | Demonstration Lab | CNNs |
The descriptions for the ML final projects can be viewed in this Colab notebook. The notebook for the Linear regression datasets can be found here. The notebook for Logistic regression datasets can be found here. The notebook for the CNN datasets can be found here.