Week 6 : Data Visualization, Introduction to ML, Supervised Learning
[ July 9 to July 15 ]
Objectives
Course Objectives:
- Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.
- Apply data analysis and visualization techniques to derive insights from diverse datasets.
- Develop proficiency in using data science tools and programming languages.
- Explore the ethical considerations associated with data-driven decision-making.
Learning Outcomes:
- Write appropriate visualizations for different sources and types of data.
- Explain why we seek to build machine learning models that generalize rather than memorize their input
- Explain the different uses for training, validation, and testing datasets
- Articulate the difference between supervised and unsupervised machine learning, as well as select the appropriate methodology for a given problem
- Select the appropriate evaluation measure for the dataset and task being solved
- Demonstrate awareness of bias and ethics in data science.
Lectures
6.1 - Data Vizualisation and Introduction to Machine Learning
6.2 - Machine Learning - Key Concepts & Supervised ML : Linear Regression
Activities
Homework
Weekly Checkin
Weekly Checkin for Week 6 | Due 5 pm , July 23, Sunday ( Based on Lecture 6.1,6.2,6.3 )
Others
Summary ( Plan versus Achievements):
- Strikethrough text is changes in plan.
- Green is new items added after planning.
- Checked boxes are completed items.
Week 6 ( June 9 to 16 )
1. Lectures
2. Homeworks
- Homework 3 released [delayed by 2 days]
- Bonus Homeworks ( 3 total points )
- Participation Activity
3. Weekly Check-in
- Check-in for week 6 opens ( Based on Lecture 6.1,6.2,6.3 )
4. Assigned Reading
- Week 5 assigned reading discussion question to be released
5. Honours
- Ethical Question start
- Dataset Selection