Description
Curriculum
Instructor
Course Overview
| Item | Description |
|---|---|
| Course Title | CS50’s Introduction to Artificial Intelligence with Python |
| Institution | Harvard University (HarvardX) |
| Platform | edX |
| Instructor | Prof. David J. Malan & Brian Yu |
| Level | Intermediate (requires CS50 or equivalent) |
| Delivery Type | Self-paced, Fully Online |
| Certificate | Verified Certificate available |
| Access to Content | Free (audit mode) |
| Duration | 12 weeks (flexible) |
| Effort Required | 10–15 hours per week |
| Prerequisites | CS50 Introduction to Computer Science or equivalent |
2. Learning Outcomes
| No. | Learning Outcome |
|---|---|
| 1 | Understand the fundamentals of Artificial Intelligence |
| 2 | Implement algorithms for search, optimization, and problem-solving |
| 3 | Develop AI applications using Python |
| 4 | Build knowledge of machine learning and neural networks |
| 5 | Work with knowledge representation and reasoning systems |
| 6 | Explore probabilistic models and decision-making algorithms |
| 7 | Integrate AI into real-world projects |
| 8 | Complete a final AI project (Capstone) |
3. Course Modules (Weekly Breakdown)
| Week | Module Title | Topics Covered | Project/Exercise |
|---|---|---|---|
| 1 | Search | Uninformed search, DFS, BFS, heuristics | Maze solving |
| 2 | Knowledge Representation | Logical reasoning, propositional logic | Logic puzzles |
| 3 | Constraint Satisfaction | CSP problems, backtracking | Sudoku solver |
| 4 | Machine Learning | Supervised learning, regression, classification | Python ML exercises |
| 5 | Neural Networks | Perceptrons, backpropagation, TensorFlow basics | Handwritten digit recognition |
| 6 | Probabilistic Models | Bayes, Markov models, Hidden Markov Models | Weather prediction |
| 7 | Optimization | Hill climbing, simulated annealing | Pathfinding optimization |
| 8 | Natural Language Processing | Tokenization, parsing, sentiment analysis | Text processing project |
| 9 | Reinforcement Learning | Q-learning, policies | Simple RL game |
| 10-12 | Final Project | Full AI project of student’s choice | Capstone AI project |
4. Course Materials
| Material Type | Description |
|---|---|
| Video Lectures | Recorded lectures by David Malan & Brian Yu |
| Notes & Slides | Comprehensive downloadable material |
| Labs | Step-by-step guided exercises |
| Problem Sets | Weekly graded coding challenges |
| Walkthroughs | Solution walkthroughs for exercises |
| Documentation | Python libraries and AI references |
5. Skills Gained
| Category | Skills |
|---|---|
| Programming | Python, AI libraries |
| AI Techniques | Search, logic, machine learning, neural networks |
| Data Handling | Probabilistic models, NLP |
| Problem Solving | Algorithmic thinking for AI |
| Project Development | Capstone AI application |
6. Assessment Structure
| Component | Weight / Importance |
|---|---|
| Labs & Exercises | 50% |
| Problem Sets | 20% |
| Quizzes | 10% |
| Final Project | 20% |
7. Certificate Information
| Item | Description |
|---|---|
| Issuer | HarvardX / edX |
| Verification | Unique serial number and URL |
| Format | Digital certificate |
| Yes | |
| Credential Type | Verified Certificate |
8. Summary
CS50’s Introduction to AI with Python provides students with the knowledge and hands-on experience to implement artificial intelligence algorithms using Python.
Completing this course demonstrates competency in AI fundamentals, machine learning, neural networks, and real-world AI applications.
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